def classifier(rng,common_input_l,common_input_r,sents_mask_l, sents_mask_r,drop_conv_W_2_pre,conv_b_2_pre,drop_conv_W_2_gate,conv_b_2_gate,drop_conv_W_2,conv_b_2,drop_conv_W_2_context,
conv_b_2_context,labels):
conv_layer_2_gate_l = Conv_with_Mask_with_Gate(rng, input_tensor3=common_input_l,
mask_matrix = sents_mask_l,
image_shape=(batch_size, 1, emb_size, maxSentLen),
filter_shape=gate_filter_shape,
W=drop_conv_W_2_pre, b=conv_b_2_pre,
W_gate =drop_conv_W_2_gate, b_gate=conv_b_2_gate )
conv_layer_2_gate_r = Conv_with_Mask_with_Gate(rng, input_tensor3=common_input_r,
mask_matrix = sents_mask_r,
image_shape=(batch_size, 1, emb_size, maxSentLen),
filter_shape=gate_filter_shape,
W=drop_conv_W_2_pre, b=conv_b_2_pre,
W_gate =drop_conv_W_2_gate, b_gate=conv_b_2_gate )
l_input_4_att = conv_layer_2_gate_l.output_tensor3#conv_layer_2_gate_l.masked_conv_out_sigmoid*conv_layer_2_pre_l.masked_conv_out+(1.0-conv_layer_2_gate_l.masked_conv_out_sigmoid)*common_input_l
r_input_4_att = conv_layer_2_gate_r.output_tensor3#conv_layer_2_gate_r.masked_conv_out_sigmoid*conv_layer_2_pre_r.masked_conv_out+(1.0-conv_layer_2_gate_r.masked_conv_out_sigmoid)*common_input_r
conv_layer_2 = Conv_for_Pair(rng,
origin_input_tensor3=common_input_l,
origin_input_tensor3_r = common_input_r,
input_tensor3=l_input_4_att,
input_tensor3_r = r_input_4_att,
mask_matrix = sents_mask_l,
mask_matrix_r = sents_mask_r,
image_shape=(batch_size, 1, hidden_size[0], maxSentLen),
image_shape_r = (batch_size, 1, hidden_size[0], maxSentLen),
filter_shape=(hidden_size[1], 1, hidden_size[0], filter_size[0]),
filter_shape_context=(hidden_size[1], 1,hidden_size[0], 1),
W=drop_conv_W_2, b=conv_b_2,
W_context=drop_conv_W_2_context, b_context=conv_b_2_context)
attentive_sent_embeddings_l_2 = conv_layer_2.attentive_maxpool_vec_l
attentive_sent_embeddings_r_2 = conv_layer_2.attentive_maxpool_vec_r
# attentive_sent_sumpool_l_2 = conv_layer_2.attentive_sumpool_vec_l
# attentive_sent_sumpool_r_2 = conv_layer_2.attentive_sumpool_vec_r
HL_layer_1_input = T.concatenate([attentive_sent_embeddings_l_2,attentive_sent_embeddings_r_2, attentive_sent_embeddings_l_2*attentive_sent_embeddings_r_2],axis=1)
HL_layer_1_input_size = hidden_size[1]*3#+extra_size#+(maxSentLen*2+10*2)#+hidden_size[1]*3+1
HL_layer_1=HiddenLayer(rng, input=HL_layer_1_input, n_in=HL_layer_1_input_size, n_out=hidden_size[0], activation=T.nnet.relu)
HL_layer_2=HiddenLayer(rng, input=HL_layer_1.output, n_in=hidden_size[0], n_out=hidden_size[0], activation=T.nnet.relu)
LR_input_size=HL_layer_1_input_size+2*hidden_size[0]
U_a = create_ensemble_para(rng, 3, LR_input_size) # the weight matrix hidden_size*2
LR_b = theano.shared(value=np.zeros((3,),dtype=theano.config.floatX),name='LR_b', borrow=True) #bias for each target class
LR_para=[U_a, LR_b]
LR_input=T.tanh(T.concatenate([HL_layer_1_input, HL_layer_1.output, HL_layer_2.output],axis=1))
layer_LR=LogisticRegression(rng, input=LR_input, n_in=LR_input_size, n_out=3, W=U_a, b=LR_b) #basically it is a multiplication between weight matrix and input feature vector
loss=layer_LR.negative_log_likelihood(labels) #for classification task, we usually used negative log likelihood as loss, the lower the better.
return loss, LR_para+HL_layer_1.params+HL_layer_2.params, layer_LR.p_y_given_x, layer_LR.errors(labels)
def evaluate_lenet5(learning_rate=0.01, n_epochs=2000, batch_size=100, emb_size=300, char_emb_size=20, hidden_size=300,
L2_weight=0.0001, p_len_limit=400, test_p_len_limit=100, q_len_limit=20, char_len=15, filter_size = [5,5],
char_filter_size=4, margin=0.5, max_EM=50.302743615):
test_batch_size=batch_size*10
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/SQuAD/';
rng = numpy.random.RandomState(23455)
word2id={}
char2id={}
#questions,paragraphs,q_masks,p_masks,labels, word2id
train_Q_list,train_para_list, train_Q_mask, train_para_mask, train_Q_char_list,train_para_char_list, train_Q_char_mask, train_para_char_mask, train_label_list, word2id, char2id=load_squad_cnn_rank_word_train(word2id, char2id, p_len_limit, q_len_limit, char_len)
train_size=len(train_para_list)
test_Q_list, test_para_list, test_Q_mask, test_para_mask,test_Q_char_list, test_para_char_list, test_Q_char_mask, test_para_char_mask, test_label_list, word2id, char2id, test_para_wordlist_list= load_squad_cnn_rank_word_dev(word2id, char2id, test_p_len_limit, q_len_limit, char_len)
test_size=len(test_para_list)
train_Q_list = numpy.asarray(train_Q_list, dtype='int32')
train_para_list = numpy.asarray(train_para_list, dtype='int32')
train_Q_mask = numpy.asarray(train_Q_mask, dtype=theano.config.floatX)
train_para_mask = numpy.asarray(train_para_mask, dtype=theano.config.floatX)
train_Q_char_list = numpy.asarray(train_Q_char_list, dtype='int32')
train_para_char_list = numpy.asarray(train_para_char_list, dtype='int32')
train_Q_char_mask = numpy.asarray(train_Q_char_mask, dtype=theano.config.floatX)
train_para_char_mask = numpy.asarray(train_para_char_mask, dtype=theano.config.floatX)
train_label_list = numpy.asarray(train_label_list, dtype='int32')
test_Q_list = numpy.asarray(test_Q_list, dtype='int32')
test_para_list = numpy.asarray(test_para_list, dtype='int32')
test_Q_mask = numpy.asarray(test_Q_mask, dtype=theano.config.floatX)
test_para_mask = numpy.asarray(test_para_mask, dtype=theano.config.floatX)
test_Q_char_list = numpy.asarray(test_Q_char_list, dtype='int32')
test_para_char_list = numpy.asarray(test_para_char_list, dtype='int32')
test_Q_char_mask = numpy.asarray(test_Q_char_mask, dtype=theano.config.floatX)
test_para_char_mask = numpy.asarray(test_para_char_mask, dtype=theano.config.floatX)
vocab_size = len(word2id)
print 'vocab size: ', vocab_size
rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, rng)
rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
id2word = {y:x for x,y in word2id.iteritems()}
word2vec=load_glove()
rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings=theano.shared(value=rand_values, borrow=True)
char_size = len(char2id)
print 'char size: ', char_size
char_rand_values=random_value_normal((char_size+1, char_emb_size), theano.config.floatX, rng)
char_embeddings=theano.shared(value=char_rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
paragraph = T.imatrix('paragraph')
questions = T.imatrix('questions')
gold_indices= T.ivector() #batch, one gold word for each sample
para_mask=T.fmatrix('para_mask')
q_mask=T.fmatrix('q_mask')
char_paragraph = T.imatrix() #(batch, char_len*p_len)
char_questions = T.imatrix()
char_para_mask=T.fmatrix()
char_q_mask=T.fmatrix()
# true_p_len = T.iscalar()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
true_batch_size = paragraph.shape[0]
true_p_len = paragraph.shape[1]
common_input_p=embeddings[paragraph.flatten()].reshape((true_batch_size,true_p_len, emb_size)) #the input format can be adapted into CNN or GRU or LSTM
common_input_q=embeddings[questions.flatten()].reshape((true_batch_size,q_len_limit, emb_size))
char_common_input_p=char_embeddings[char_paragraph.flatten()].reshape((true_batch_size*true_p_len, char_len, char_emb_size)) #the input format can be adapted into CNN or GRU or LSTM
char_common_input_q=char_embeddings[char_questions.flatten()].reshape((true_batch_size*q_len_limit, char_len, char_emb_size))
char_p_masks = char_para_mask.reshape((true_batch_size*true_p_len, char_len))
char_q_masks = char_q_mask.reshape((true_batch_size*q_len_limit, char_len))
char_LSTM_para_dict=create_LSTM_para(rng, char_emb_size, char_emb_size)
char_LSTM_para_dict_bw=create_LSTM_para(rng, char_emb_size, char_emb_size)
char_lstm_layer_p=Bd_LSTM_Batch_Tensor_Input_with_Mask(char_common_input_p.dimshuffle(0,2,1), char_p_masks, char_emb_size, char_LSTM_para_dict,char_LSTM_para_dict_bw)
char_word_embeddings_p = char_lstm_layer_p.output_sent_rep_conc.reshape((true_batch_size, true_p_len, 2*char_emb_size)).dimshuffle(0, 2,1) #(batch, 2*hidden)
char_lstm_layer_q=Bd_LSTM_Batch_Tensor_Input_with_Mask(char_common_input_q.dimshuffle(0,2,1), char_q_masks, char_emb_size, char_LSTM_para_dict,char_LSTM_para_dict_bw)
char_word_embeddings_q = char_lstm_layer_q.output_sent_rep_conc.reshape((true_batch_size, q_len_limit, 2*char_emb_size)).dimshuffle(0, 2,1) #(batch, 2*hidden)
LSTM_para_dict=create_LSTM_para(rng, 2*char_emb_size+emb_size,hidden_size) #40+300
LSTM_para_dict_bw=create_LSTM_para(rng, 2*char_emb_size+emb_size,hidden_size)
p_input2lstm = T.concatenate([common_input_p.dimshuffle(0,2,1), char_word_embeddings_p], axis=1) #(batch, emb_size+char_emb_size, p_len)
q_input2lstm = T.concatenate([common_input_q.dimshuffle(0,2,1), char_word_embeddings_q], axis=1) #(batch, emb_size+char_emb_size, p_len)
lstm_layer_p=Bd_LSTM_Batch_Tensor_Input_with_Mask(p_input2lstm, para_mask, hidden_size, LSTM_para_dict,LSTM_para_dict_bw)
p_tensor3 = lstm_layer_p.output_tensor_conc #(batch, 2*hidden, p_len)
lstm_layer_q=Bd_LSTM_Batch_Tensor_Input_with_Mask(q_input2lstm, q_mask, hidden_size, LSTM_para_dict,LSTM_para_dict_bw)
q_reps = lstm_layer_q.output_sent_rep_conc #(batch, 2*hidden)
NN_para=char_LSTM_para_dict.values()+char_LSTM_para_dict_bw.values()+LSTM_para_dict.values()+LSTM_para_dict_bw.values()
input4score = T.concatenate([p_tensor3, T.repeat(q_reps.dimshuffle(0,1,'x'), true_p_len, axis=2)], axis=1) #(batch, 4*hidden, p_len)
HL_1_para = create_ensemble_para(rng, hidden_size, 4*hidden_size)
HL_2_para = create_ensemble_para(rng, hidden_size, hidden_size)
HL_3_para = create_ensemble_para(rng, hidden_size, hidden_size)
HL_4_para = create_ensemble_para(rng, hidden_size, hidden_size)
U_a = create_ensemble_para(rng, 1, hidden_size)
norm_U_a=normalize_matrix(U_a)
norm_HL_1_para=normalize_matrix(HL_1_para)
norm_HL_2_para=normalize_matrix(HL_2_para)
norm_HL_3_para=normalize_matrix(HL_3_para)
norm_HL_4_para=normalize_matrix(HL_4_para)
span_scores_matrix = add_HLs_2_tensor3(input4score, norm_HL_1_para,norm_HL_2_para,norm_HL_3_para,norm_HL_4_para, norm_U_a, true_batch_size,true_p_len)
span_scores=T.nnet.softmax(span_scores_matrix) #(batch, para_len)
loss_neg_likelihood=-T.mean(T.log(span_scores[T.arange(true_batch_size), gold_indices]))
#ranking loss
tanh_span_scores_matrix = span_scores#T.tanh(span_scores_matrix) #(batch, gram_size)
index_matrix = T.zeros((true_batch_size, p_len_limit), dtype=theano.config.floatX)
new_index_matrix = T.set_subtensor(index_matrix[T.arange(true_batch_size), gold_indices], 1.0)
prob_batch_posi = tanh_span_scores_matrix[new_index_matrix.nonzero()]
prob_batch_nega = tanh_span_scores_matrix[(1.0-new_index_matrix).nonzero()]
repeat_posi = T.extra_ops.repeat(prob_batch_posi, prob_batch_nega.shape[0], axis=0)
repeat_nega = T.extra_ops.repeat(prob_batch_nega.dimshuffle('x',0), prob_batch_posi.shape[0], axis=0).flatten()
loss_rank = T.mean(T.maximum(0.0, margin-repeat_posi+repeat_nega))
loss = loss_neg_likelihood + loss_rank
#test
mask_test_return=T.argmax(span_scores_matrix*para_mask, axis=1) #batch
params = [embeddings,char_embeddings]+NN_para+[U_a,HL_1_para,HL_2_para,HL_3_para,HL_4_para]
# L2_reg =L2norm_paraList([embeddings,U1, W1, U1_b, W1_b,UQ, WQ , UQ_b, WQ_b, W_a1, W_a2, U_a])
#L2_reg = L2norm_paraList(params)
cost=loss#+ConvGRU_1.error#
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / (T.sqrt(acc)+1e-8))) #AdaGrad
updates.append((acc_i, acc))
# updates=Adam(cost, params, lr=0.0001)
train_model = theano.function([paragraph, questions,gold_indices, para_mask, q_mask, char_paragraph, #(batch, char_len*p_len)
char_questions, char_para_mask, char_q_mask], cost, updates=updates,on_unused_input='ignore')
test_model = theano.function([paragraph, questions,para_mask, q_mask,
char_paragraph,
char_questions,
char_para_mask,
char_q_mask], mask_test_return, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches=train_size/batch_size
# remain_train=train_size%batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)+[train_size-batch_size]
n_test_batches=test_size/test_batch_size
# remain_test=test_size%batch_size
test_batch_start=list(numpy.arange(n_test_batches)*test_batch_size)+[test_size-test_batch_size]
max_F1_acc=0.0
max_exact_acc=0.0
cost_i=0.0
train_ids = range(train_size)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.shuffle(train_ids)
iter_accu=0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
iter_accu+=1
train_id_batch = train_ids[para_id:para_id+batch_size]
cost_i+= train_model(
train_para_list[train_id_batch],
train_Q_list[train_id_batch],
train_label_list[train_id_batch],
train_para_mask[train_id_batch],
train_Q_mask[train_id_batch],
train_para_char_list[train_id_batch],
train_Q_char_list[train_id_batch],
train_para_char_mask[train_id_batch],
train_Q_char_mask[train_id_batch])
#print iter
if iter%100==0:
print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
print 'Testing...'
past_time = time.time()
# pred_dict={}
q_amount=0
p1=0
for test_para_id in test_batch_start:
batch_predict_ids=test_model(
test_para_list[test_para_id:test_para_id+test_batch_size],
test_Q_list[test_para_id:test_para_id+test_batch_size],
test_para_mask[test_para_id:test_para_id+test_batch_size],
test_Q_mask[test_para_id:test_para_id+test_batch_size],
test_para_char_list[test_para_id:test_para_id+test_batch_size],
test_Q_char_list[test_para_id:test_para_id+test_batch_size],
test_para_char_mask[test_para_id:test_para_id+test_batch_size],
test_Q_char_mask[test_para_id:test_para_id+test_batch_size])
# test_para_wordlist_batch=test_para_wordlist_list[test_para_id:test_para_id+test_batch_size]
test_label_batch=test_label_list[test_para_id:test_para_id+test_batch_size]
q_amount+=test_batch_size
for q in range(test_batch_size): #for each question
predict_id = batch_predict_ids[q]
ground_ids=test_label_batch[q]
if predict_id in set(ground_ids):
p1+=1
# print batch_predict_ids[q], mask_batch_predict_ids[q], test_p_len_limit - numpy.sum(test_para_mask[test_para_id+q]), scores_i[q], test_para_mask[test_para_id+q]
exact_acc = p1*100.0/q_amount
if exact_acc> max_exact_acc:
max_exact_acc=exact_acc
# if max_exact_acc > max_EM:
# store_model_to_file(rootPath+'Best_Paras_google_'+str(max_exact_acc), params)
# print 'Finished storing best params at:', max_exact_acc
print '\t\tcurrent exact:', exact_acc, '\t\t\tmax exact_acc:', max_exact_acc
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.01, n_epochs=2000, batch_size=10, test_batch_size=200, emb_size=300, hidden_size=100,
L2_weight=0.0001, para_len_limit=300, q_len_limit=30, max_EM=40.0):
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/SQuAD/';
rng = numpy.random.RandomState(23455)
# glove_vocab=set(word2vec.keys())
train_para_list, train_Q_list, train_start_list,train_end_list, train_para_mask, train_mask, word2id, train_feature_matrixlist=load_train_AI2(para_len_limit, q_len_limit)
train_size=len(train_para_list)
if train_size!=len(train_Q_list) or train_size!=len(train_start_list) or train_size!=len(train_para_mask):
print 'train_size!=len(Q_list) or train_size!=len(label_list) or train_size!=len(para_mask)'
exit(0)
test_para_list, test_Q_list, test_Q_list_word, test_para_mask, test_mask, overall_vocab_size, overall_word2id, test_text_list, q_ansSet_list, test_feature_matrixlist, q_idlist= load_dev_or_test_AI2(word2id, para_len_limit, q_len_limit)
test_size=len(test_para_list)
if test_size!=len(test_Q_list) or test_size!=len(test_mask) or test_size!=len(test_para_mask):
print 'test_size!=len(test_Q_list) or test_size!=len(test_mask) or test_size!=len(test_para_mask)'
exit(0)
rand_values=random_value_normal((overall_vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
id2word = {y:x for x,y in overall_word2id.iteritems()}
word2vec=load_glove()
rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings=theano.shared(value=rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
paragraph = T.imatrix('paragraph')
questions = T.imatrix('questions')
# labels = T.imatrix('labels') #(batch, para_len)
start_indices= T.ivector() #batch
end_indices = T.ivector() #batch
para_mask=T.fmatrix('para_mask')
q_mask=T.fmatrix('q_mask')
extraF=T.ftensor3('extraF') # should be in shape (batch, wordsize, 3)
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
true_batch_size=paragraph.shape[0]
norm_extraF=normalize_matrix(extraF)
fwd_para=create_LSTM_para(rng, emb_size, hidden_size) #create_LSTM_para(rng, word_dim, hidden_dim)
bwd_para=create_LSTM_para(rng, emb_size, hidden_size)
paragraph_para=fwd_para.values()+ bwd_para.values()
fwd_e1=create_LSTM_para(rng, 8*hidden_size, hidden_size) #create_LSTM_para(rng, word_dim, hidden_dim)
bwd_e1=create_LSTM_para(rng, 8*hidden_size, hidden_size)
paragraph_para_e1=fwd_e1.values()+ bwd_e1.values()
fwd_e11=create_LSTM_para(rng, 2*hidden_size, hidden_size) #create_LSTM_para(rng, word_dim, hidden_dim)
bwd_e11=create_LSTM_para(rng, 2*hidden_size, hidden_size)
paragraph_para_e11=fwd_e11.values()+ bwd_e11.values()
fwd_e2=create_LSTM_para(rng, 2*hidden_size, hidden_size) #create_LSTM_para(rng, word_dim, hidden_dim)
bwd_e2=create_LSTM_para(rng, 2*hidden_size, hidden_size)
paragraph_para_e2=fwd_e2.values()+ bwd_e2.values()
# U_e2, W_e2, b_e2=create_GRU_para(rng, hidden_size, hidden_size)
# U_e2_b, W_e2_b, b_e2_b=create_GRU_para(rng, hidden_size, hidden_size)
# paragraph_para_e2=[U_e2, W_e2, b_e2, U_e2_b, W_e2_b, b_e2_b]
# fwd_Q=create_LSTM_para(rng, emb_size, hidden_size) #create_LSTM_para(rng, word_dim, hidden_dim)
# bwd_Q=create_LSTM_para(rng, emb_size, hidden_size)
# Q_para=fwd_Q.values()+ bwd_Q.values()
# W_a1 = create_ensemble_para(rng, hidden_size, hidden_size)# init_weights((2*hidden_size, hidden_size))
# W_a2 = create_ensemble_para(rng, hidden_size, hidden_size)
U_a1 = create_ensemble_para(rng, 1, 10*hidden_size) # 3 extra features
U_a2 = create_ensemble_para(rng, 1, 10*hidden_size) # 3 extra features
U_a3 = create_ensemble_para(rng, 1, 6*hidden_size) # 3 extra features
# LR_b = theano.shared(value=numpy.zeros((2,),
# dtype=theano.config.floatX), # @UndefinedVariable
# name='LR_b', borrow=True)
HL_paras=[U_a1, U_a2, U_a3]
params = [embeddings]+paragraph_para+paragraph_para_e1+paragraph_para_e11+HL_paras+paragraph_para_e2
# load_model_from_file(rootPath+'Best_Paras_AI2_31.210974456', params)
paragraph_input = embeddings[paragraph.flatten()].reshape((true_batch_size, paragraph.shape[1], emb_size)).transpose((0, 2,1)) # (batch_size, emb_size, maxparalen)
#self, X, Mask, hidden_dim, fwd_tparams, bwd_tparams
paragraph_model=Bd_LSTM_Batch_Tensor_Input_with_Mask_Concate(X=paragraph_input, Mask=para_mask, hidden_dim=hidden_size,fwd_tparams=fwd_para, bwd_tparams= bwd_para)
para_reps=paragraph_model.output_tensor #(batch, 2*hidden, para_len)
Qs_emb = embeddings[questions.flatten()].reshape((true_batch_size, questions.shape[1], emb_size)).transpose((0, 2,1)) #(#questions, emb_size, maxsenlength)
questions_model=Bd_LSTM_Batch_Tensor_Input_with_Mask_Concate(X=Qs_emb, Mask=q_mask, hidden_dim=hidden_size, fwd_tparams=fwd_para, bwd_tparams= bwd_para)
questions_reps_tensor=questions_model.output_tensor #(batch, 2*hidden ,q_len)
# questions_reps=questions_model.output_sent_rep_maxpooling.reshape((true_batch_size, 1, hidden_size)) #(batch, 1, hidden)
# questions_reps=T.repeat(questions_reps, para_reps.shape[2], axis=1) #(batch, para_len, hidden)
# #LSTM for questions
# fwd_LSTM_q_dict=create_LSTM_para(rng, emb_size, hidden_size)
# bwd_LSTM_q_dict=create_LSTM_para(rng, emb_size, hidden_size)
# Q_para=fwd_LSTM_q_dict.values()+ bwd_LSTM_q_dict.values()# .values returns a list of parameters
# questions_model=Bd_LSTM_Batch_Tensor_Input_with_Mask(Qs_emb, q_mask, hidden_size, fwd_LSTM_q_dict, bwd_LSTM_q_dict)
# questions_reps_tensor=questions_model.output_tensor
# new_labels=T.gt(labels[:,:-1]+labels[:,1:], 0.0)
# ConvGRU_1=Conv_then_GRU_then_Classify(rng, concate_paragraph_input, Qs_emb, para_len_limit, q_len_limit, emb_size+3, hidden_size, emb_size, 2, batch_size, para_mask, q_mask, new_labels, 2)
# ConvGRU_1_dis=ConvGRU_1.masked_dis_inprediction
# padding_vec = T.zeros((batch_size, 1), dtype=theano.config.floatX)
# ConvGRU_1_dis_leftpad=T.concatenate([padding_vec, ConvGRU_1_dis], axis=1)
# ConvGRU_1_dis_rightpad=T.concatenate([ConvGRU_1_dis, padding_vec], axis=1)
# ConvGRU_1_dis_into_unigram=0.5*(ConvGRU_1_dis_leftpad+ConvGRU_1_dis_rightpad)
norm_U_a3=normalize_matrix(U_a3)
def example_in_batch(para_matrix, q_matrix):
#assume both are (2*hidden, len)
repeat_para_matrix_T=T.repeat(para_matrix.T, q_matrix.shape[1], axis=0) #(para_len*q_len, 2*hidden)
repeat_q_matrix_3D = T.repeat(q_matrix.T.dimshuffle('x',0,1), para_matrix.shape[1], axis=0) #(para_len, q_len, 2*hidden)
repeat_q_matrix_T= repeat_q_matrix_3D.reshape((repeat_q_matrix_3D.shape[0]*repeat_q_matrix_3D.shape[1], repeat_q_matrix_3D.shape[2])) #(para_len*q_len, 2*hidden)
ele_mult =repeat_para_matrix_T*repeat_q_matrix_T #(#(para_len*q_len, 2*hidden))
overall_concv = T.concatenate([repeat_para_matrix_T, repeat_q_matrix_T, ele_mult], axis=1) ##(para_len*q_len, 6*hidden)
scores=T.dot(overall_concv, norm_U_a3) #(para_len*q_len,1)
interaction_matrix=scores.reshape((para_matrix.shape[1], q_matrix.shape[1])) #(para_len, q_len)
# transpose_para_matrix=para_matrix.T
# interaction_matrix=T.dot(transpose_para_matrix, q_matrix) #(para_len, q_len)
norm_interaction_matrix=T.nnet.softmax(interaction_matrix)
# norm_interaction_matrix=T.maximum(0.0, interaction_matrix)
q_by_para = T.dot(q_matrix, norm_interaction_matrix.T)/T.sum(norm_interaction_matrix.T, axis=0).dimshuffle('x',0) #(2*hidden, para_len)
para_by_q = T.repeat(T.dot(para_matrix, T.nnet.softmax(T.max(interaction_matrix, axis=1).dimshuffle('x',0)).T), para_matrix.shape[1], axis=1)
return (q_by_para, para_by_q)
inter_return, updates = theano.scan(fn=example_in_batch,
outputs_info=None,
sequences=[para_reps, questions_reps_tensor]) #batch_q_reps (batch, hidden, para_len)
batch_q_reps=inter_return[0] #(batch, 2*hidden, para_len)
batch_para_reps=inter_return[1] #(batch, 2*hidden , para_len)
#para_reps, batch_q_reps, questions_reps.dimshuffle(0,2,1), all are in (batch, hidden , para_len)
ensemble_para_reps_tensor=T.concatenate([para_reps, batch_q_reps,para_reps*batch_q_reps, para_reps*batch_para_reps], axis=1) #(batch, 4*2*hidden, para_len) questions_reps.dimshuffle(0,2,1)
para_ensemble_model=Bd_LSTM_Batch_Tensor_Input_with_Mask_Concate(X=ensemble_para_reps_tensor, Mask=para_mask, hidden_dim=hidden_size,fwd_tparams=fwd_e1, bwd_tparams= bwd_e1)
para_reps_tensor4score=para_ensemble_model.output_tensor #(batch, 2*hidden ,para_len)
para_ensemble_model1=Bd_LSTM_Batch_Tensor_Input_with_Mask_Concate(X=para_reps_tensor4score, Mask=para_mask, hidden_dim=hidden_size,fwd_tparams=fwd_e11, bwd_tparams= bwd_e11)
para_reps_tensor4score1=para_ensemble_model1.output_tensor #(batch, 2*hidden ,para_len)
Con_G_M=T.concatenate([ensemble_para_reps_tensor, para_reps_tensor4score1], axis=1) #(batch, 10*hidden, para_len)
#score for each para word
norm_U_a=normalize_matrix(U_a1)
start_scores=T.dot(Con_G_M.dimshuffle(0,2,1), norm_U_a) #(batch, para_len, 1)
start_scores=T.nnet.softmax(start_scores.reshape((true_batch_size, paragraph.shape[1]))) #(batch, para_len)
# para_reps_tensor4score = T.concatenate([para_reps_tensor4score, start_scores.dimshuffle(0,'x',1)], axis=1)
para_ensemble_model2=Bd_LSTM_Batch_Tensor_Input_with_Mask_Concate(X=para_reps_tensor4score1, Mask=para_mask, hidden_dim=hidden_size,fwd_tparams=fwd_e2, bwd_tparams= bwd_e2)
para_reps_tensor4score2=para_ensemble_model2.output_tensor #(batch, 2*hidden ,para_len)
Con_G_M2=T.concatenate([ensemble_para_reps_tensor, para_reps_tensor4score2], axis=1) #(batch, 10*hidden, para_len)
norm_U_a2=normalize_matrix(U_a2)
end_scores=T.dot(Con_G_M2.dimshuffle(0,2,1), norm_U_a2) #(batch, para_len, 1)
end_scores=T.nnet.softmax(end_scores.reshape((true_batch_size, paragraph.shape[1]))) #(batch, para_len)
#loss train
loss=-T.mean(T.log(start_scores[T.arange(true_batch_size), start_indices])+T.log(end_scores[T.arange(true_batch_size), end_indices]))
#test
co_simi_batch_matrix=T.batched_dot((para_mask*start_scores).dimshuffle(0,1,'x'), (para_mask*end_scores).dimshuffle(0,'x',1)) #(batch, para_len, para_len)
#reset lower dialgonal
cols = numpy.concatenate([numpy.array(range(i), dtype=numpy.uint) for i in xrange(para_len_limit)])
rows = numpy.concatenate([numpy.array([i]*i, dtype=numpy.uint) for i in xrange(para_len_limit)])
c = T.set_subtensor(co_simi_batch_matrix[:,rows, cols], theano.shared(numpy.zeros(para_len_limit*(para_len_limit-1)/2)))
#reset longer than 7 size
cols2 = numpy.concatenate([numpy.array(range(i+7,para_len_limit), dtype=numpy.uint) for i in xrange(para_len_limit-7)])
rows2 = numpy.concatenate([numpy.array([i]*(para_len_limit-7-i), dtype=numpy.uint) for i in xrange(para_len_limit-7)])
c2 = T.set_subtensor(c[:,rows2, cols2], theano.shared(numpy.zeros((para_len_limit-7)*(para_len_limit-6)/2)))
test_return=T.argmax(c2.reshape((true_batch_size, para_len_limit*para_len_limit)), axis=1) #batch
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
# L2_reg =L2norm_paraList([embeddings,U1, W1, U1_b, W1_b,UQ, WQ , UQ_b, WQ_b, W_a1, W_a2, U_a])
#L2_reg = L2norm_paraList(params)
cost=loss#+ConvGRU_1.error#
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / (T.sqrt(acc)+1e-8))) #AdaGrad
updates.append((acc_i, acc))
# updates=Adam(cost, params, lr=0.0001)
train_model = theano.function([paragraph, questions,start_indices, end_indices,para_mask, q_mask, extraF], cost, updates=updates,on_unused_input='ignore')
test_model = theano.function([paragraph, questions,para_mask, q_mask, extraF], test_return, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches=train_size/batch_size
# remain_train=train_size%batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)+[train_size-batch_size]
n_test_batches=test_size/test_batch_size
# remain_test=test_size%batch_size
test_batch_start=list(numpy.arange(n_test_batches)*test_batch_size)+[test_size-test_batch_size]
max_F1_acc=0.0
max_exact_acc=0.0
cost_i=0.0
train_ids = range(train_size)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.shuffle(train_ids)
iter_accu=0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
iter_accu+=1
# haha=para_mask[para_id:para_id+batch_size]
# print haha
# for i in range(batch_size):
# print len(haha[i])
cost_i+= train_model(
numpy.asarray([train_para_list[id] for id in train_ids[para_id:para_id+batch_size]], dtype='int32'),
numpy.asarray([train_Q_list[id] for id in train_ids[para_id:para_id+batch_size]], dtype='int32'),
numpy.asarray([train_start_list[id] for id in train_ids[para_id:para_id+batch_size]], dtype='int32'),
numpy.asarray([train_end_list[id] for id in train_ids[para_id:para_id+batch_size]], dtype='int32'),
numpy.asarray([train_para_mask[id] for id in train_ids[para_id:para_id+batch_size]], dtype=theano.config.floatX),
numpy.asarray([train_mask[id] for id in train_ids[para_id:para_id+batch_size]], dtype=theano.config.floatX),
numpy.asarray([train_feature_matrixlist[id] for id in train_ids[para_id:para_id+batch_size]], dtype=theano.config.floatX))
#print iter
if iter%10==0:
print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
print 'Testing...'
past_time = time.time()
# writefile=codecs.open(rootPath+'predictions.txt', 'w', 'utf-8')
# writefile.write('{')
pred_dict={}
# exact_match=0.0
# F1_match=0.0
q_amount=0
for test_para_id in test_batch_start:
batch_predict_ids=test_model(
numpy.asarray(test_para_list[test_para_id:test_para_id+test_batch_size], dtype='int32'),
numpy.asarray(test_Q_list[test_para_id:test_para_id+test_batch_size], dtype='int32'),
numpy.asarray(test_para_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
numpy.asarray(test_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
numpy.asarray(test_feature_matrixlist[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX))
# print distribution_matrix
test_para_wordlist_list=test_text_list[test_para_id:test_para_id+test_batch_size]
# para_gold_ansset_list=q_ansSet_list[test_para_id:test_para_id+test_batch_size]
q_ids_batch=q_idlist[test_para_id:test_para_id+test_batch_size]
# print 'q_ids_batch:', q_ids_batch
# paralist_extra_features=test_feature_matrixlist[test_para_id:test_para_id+batch_size]
# sub_para_mask=test_para_mask[test_para_id:test_para_id+batch_size]
# para_len=len(test_para_wordlist_list[0])
# if para_len!=len(distribution_matrix[0]):
# print 'para_len!=len(distribution_matrix[0]):', para_len, len(distribution_matrix[0])
# exit(0)
# q_size=len(distribution_matrix)
q_amount+=test_batch_size
# print q_size
# print test_para_word_list
# Q_list_inword=test_Q_list_word[test_para_id:test_para_id+test_batch_size]
for q in range(test_batch_size): #for each question
# if len(distribution_matrix[q])!=len(test_label_matrix[q]):
# print 'len(distribution_matrix[q])!=len(test_label_matrix[q]):', len(distribution_matrix[q]), len(test_label_matrix[q])
# else:
# ss=len(distribution_matrix[q])
# combine_list=[]
# for ii in range(ss):
# combine_list.append(str(distribution_matrix[q][ii])+'('+str(test_label_matrix[q][ii])+')')
# print combine_list
# exit(0)
# print 'distribution_matrix[q]:',distribution_matrix[q]
pred_ans=decode_predict_id_AI2(batch_predict_ids[q], para_len_limit, test_para_wordlist_list[q])
q_id=q_ids_batch[q]
pred_dict[q_id]=pred_ans
# writefile.write('"'+str(q_id)+'": "'+pred_ans+'", ')
# pred_ans=extract_ansList_attentionList(test_para_wordlist_list[q], distribution_matrix[q], numpy.asarray(paralist_extra_features[q], dtype=theano.config.floatX), sub_para_mask[q], Q_list_inword[q])
# q_gold_ans_set=para_gold_ansset_list[q]
# # print test_para_wordlist_list[q]
# # print Q_list_inword[q]
# # print pred_ans.encode('utf8'), q_gold_ans_set
# if pred_ans in q_gold_ans_set:
# exact_match+=1
# F1=MacroF1(pred_ans, q_gold_ans_set)
# F1_match+=F1
with codecs.open(rootPath+'predictions.txt', 'w', 'utf-8') as outfile:
json.dump(pred_dict, outfile)
F1_acc, exact_acc = standard_eval(rootPath+'dev-v1.1.json', rootPath+'predictions.txt')
# F1_acc=F1_match/q_amount
# exact_acc=exact_match/q_amount
if F1_acc> max_F1_acc:
max_F1_acc=F1_acc
if exact_acc> max_exact_acc:
max_exact_acc=exact_acc
if max_exact_acc > max_EM:
store_model_to_file(rootPath+'Best_Paras_AI2_'+str(max_exact_acc), params)
print 'Finished storing best params at:', max_exact_acc
print 'current average F1:', F1_acc, '\t\tmax F1:', max_F1_acc, 'current exact:', exact_acc, '\t\tmax exact_acc:', max_exact_acc
# os.system('python evaluate-v1.1.py '+rootPath+'dev-v1.1.json '+rootPath+'predictions.txt')
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.02,
n_epochs=100,
emb_size=300,
batch_size=50,
filter_size=[3],
sent_len=40,
claim_len=40,
cand_size=10,
hidden_size=[300, 300],
max_pred_pick=5):
model_options = locals().copy()
print "model options", model_options
pred_id2label = {1: 'SUPPORTS', 0: 'REFUTES', 2: 'NOT ENOUGH INFO'}
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
srng = T.shared_randomstreams.RandomStreams(rng.randint(seed))
"load raw data"
train_sents, train_sent_masks, train_sent_labels, train_claims, train_claim_mask, train_labels, word2id = load_fever_train(
sent_len, claim_len, cand_size)
train_3th_sents, train_3th_sent_masks, train_3th_sent_labels, train_3th_claims, train_3th_claim_mask, train_3th_labels, word2id = load_fever_train_NoEnoughInfo(
sent_len, claim_len, cand_size, word2id)
test_sents, test_sent_masks, test_sent_labels, test_claims, test_claim_mask, test_sent_names, test_ground_names, test_labels, word2id = load_fever_dev(
sent_len, claim_len, cand_size, word2id)
test_3th_sents, test_3th_sent_masks, test_3th_sent_labels, test_3th_claims, test_3th_claim_mask, test_3th_labels, word2id = load_fever_dev_NoEnoughInfo(
sent_len, claim_len, cand_size, word2id)
train_sents = np.asarray(train_sents, dtype='int32')
train_3th_sents = np.asarray(train_3th_sents, dtype='int32')
joint_train_sents = np.concatenate((train_sents, train_3th_sents))
test_sents = np.asarray(test_sents, dtype='int32')
test_3th_sents = np.asarray(test_3th_sents, dtype='int32')
joint_test_sents = np.concatenate((test_sents, test_3th_sents))
train_sent_masks = np.asarray(train_sent_masks, dtype=theano.config.floatX)
train_3th_sent_masks = np.asarray(train_3th_sent_masks,
dtype=theano.config.floatX)
joint_train_sent_masks = np.concatenate(
(train_sent_masks, train_3th_sent_masks))
test_sent_masks = np.asarray(test_sent_masks, dtype=theano.config.floatX)
test_3th_sent_masks = np.asarray(test_3th_sent_masks,
dtype=theano.config.floatX)
joint_test_sent_masks = np.concatenate(
(test_sent_masks, test_3th_sent_masks))
train_sent_labels = np.asarray(train_sent_labels, dtype='int32')
train_3th_sent_labels = np.asarray(train_3th_sent_labels, dtype='int32')
joint_train_sent_labels = np.concatenate(
(train_sent_labels, train_3th_sent_labels))
test_sent_labels = np.asarray(test_sent_labels, dtype='int32')
test_3th_sent_labels = np.asarray(test_3th_sent_labels, dtype='int32')
joint_test_sent_labels = np.concatenate(
(test_sent_labels, test_3th_sent_labels))
train_claims = np.asarray(train_claims, dtype='int32')
train_3th_claims = np.asarray(train_3th_claims, dtype='int32')
joint_train_claims = np.concatenate((train_claims, train_3th_claims))
test_claims = np.asarray(test_claims, dtype='int32')
test_3th_claims = np.asarray(test_3th_claims, dtype='int32')
joint_test_claims = np.concatenate((test_claims, test_3th_claims))
train_claim_mask = np.asarray(train_claim_mask, dtype=theano.config.floatX)
train_3th_claim_mask = np.asarray(train_3th_claim_mask,
dtype=theano.config.floatX)
joint_train_claim_mask = np.concatenate(
(train_claim_mask, train_3th_claim_mask))
test_claim_mask = np.asarray(test_claim_mask, dtype=theano.config.floatX)
test_3th_claim_mask = np.asarray(test_3th_claim_mask,
dtype=theano.config.floatX)
joint_test_claim_mask = np.concatenate(
(test_claim_mask, test_3th_claim_mask))
train_labels = np.asarray(train_labels, dtype='int32')
train_3th_labels = np.asarray(train_3th_labels, dtype='int32')
joint_train_labels = np.concatenate((train_labels, train_3th_labels))
test_labels = np.asarray(test_labels, dtype='int32')
test_3th_labels = np.asarray(test_3th_labels, dtype='int32')
joint_test_labels = np.concatenate((test_labels, test_3th_labels))
joint_train_size = len(joint_train_claims)
joint_test_size = len(joint_test_claims)
train_size = len(train_claims)
test_size = len(test_claims)
test_3th_size = len(test_3th_claims)
vocab_size = len(word2id) + 1
print 'joint_train size: ', joint_train_size, ' joint_test size: ', joint_test_size
print 'train size: ', train_size, ' test size: ', test_size
print 'vocab size: ', vocab_size
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_word2vec()
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
init_embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
"now, start to build the input form of the model"
sents_ids = T.itensor3() #(batch, cand_size, sent_len)
sents_mask = T.ftensor3()
sents_labels = T.imatrix() #(batch, cand_size)
claim_ids = T.imatrix() #(batch, claim_len)
claim_mask = T.fmatrix()
joint_sents_ids = T.itensor3() #(batch, cand_size, sent_len)
joint_sents_mask = T.ftensor3()
joint_sents_labels = T.imatrix() #(batch, cand_size)
joint_claim_ids = T.imatrix() #(batch, claim_len)
joint_claim_mask = T.fmatrix()
joint_labels = T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
embed_input_sents = init_embeddings[sents_ids.flatten(
)].reshape((batch_size * cand_size, sent_len, emb_size)).dimshuffle(
0, 2, 1
) #embed_input(init_embeddings, sents_ids_l)#embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
embed_input_claim = init_embeddings[claim_ids.flatten()].reshape(
(batch_size, claim_len, emb_size)).dimshuffle(0, 2, 1)
conv_W, conv_b = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size, filter_size[0]))
att_conv_W, att_conv_b = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size,
filter_size[0]))
conv_W_context, conv_b_context = create_conv_para(
rng, filter_shape=(hidden_size[0], 1, emb_size, 1))
NN_para = [conv_W, conv_b, att_conv_W, att_conv_b, conv_W_context]
conv_model_sents = Conv_with_Mask(
rng,
input_tensor3=embed_input_sents,
mask_matrix=sents_mask.reshape(
(sents_mask.shape[0] * sents_mask.shape[1], sents_mask.shape[2])),
image_shape=(batch_size * cand_size, 1, emb_size, sent_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
W=conv_W,
b=conv_b
) #mutiple mask with the conv_out to set the features by UNK to zero
sent_embeddings = conv_model_sents.maxpool_vec #(batch_size*cand_size, hidden_size) # each sentence then have an embedding of length hidden_size
batch_sent_emb = sent_embeddings.reshape(
(batch_size, cand_size, hidden_size[0]))
conv_model_claims = Conv_with_Mask(
rng,
input_tensor3=embed_input_claim,
mask_matrix=claim_mask,
image_shape=(batch_size, 1, emb_size, claim_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
W=conv_W,
b=conv_b
) #mutiple mask with the conv_out to set the features by UNK to zero
claim_embeddings = conv_model_claims.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
batch_claim_emb = T.repeat(claim_embeddings.dimshuffle(0, 'x', 1),
cand_size,
axis=1)
# concate_claim_sent = T.concatenate([batch_claim_emb,batch_sent_emb ], axis=2)
# concate_2_matrix = concate_claim_sent.reshape((batch_size*cand_size, hidden_size[0]*2))
concate_claim_sent = T.concatenate([
batch_claim_emb, batch_sent_emb,
T.sum(batch_claim_emb * batch_sent_emb, axis=2).dimshuffle(0, 1, 'x')
],
axis=2)
concate_2_matrix = concate_claim_sent.reshape(
(batch_size * cand_size, hidden_size[0] * 2 + 1))
LR_input = concate_2_matrix
LR_input_size = hidden_size[0] * 2 + 1
#classification layer, it is just mapping from a feature vector of size "hidden_size" to a vector of only two values: positive, negative
U_a = create_ensemble_para(
rng, 1, LR_input_size) # the weight matrix hidden_size*2
# LR_b = theano.shared(value=np.zeros((8,),dtype=theano.config.floatX),name='LR_b', borrow=True) #bias for each target class
LR_para = [U_a]
# layer_LR=LogisticRegression(rng, input=LR_input, n_in=LR_input_size, n_out=8, W=U_a, b=LR_b) #basically it is a multiplication between weight matrix and input feature vector
score_matrix = T.nnet.sigmoid(LR_input.dot(U_a)) #batch * 12
inter_matrix = score_matrix.reshape((batch_size, cand_size))
# inter_sent_claim = T.batched_dot(batch_sent_emb, batch_claim_emb) #(batch_size, cand_size, 1)
# inter_matrix = T.nnet.sigmoid(inter_sent_claim.reshape((batch_size, cand_size)))
'''
maybe 1.0-inter_matrix can be rewritten into 1/e^(inter_matrix)
'''
# prob_pos = T.where( sents_labels < 1, 1.0-inter_matrix, inter_matrix)
# loss = -T.mean(T.log(prob_pos))
#f1 as loss
batch_overlap = T.sum(sents_labels * inter_matrix, axis=1)
batch_recall = batch_overlap / T.sum(sents_labels, axis=1)
batch_precision = batch_overlap / T.sum(inter_matrix, axis=1)
batch_f1 = 2.0 * batch_recall * batch_precision / (batch_recall +
batch_precision)
loss = -T.mean(T.log(batch_f1))
# loss = T.nnet.nnet.binary_crossentropy(inter_matrix, sents_labels).mean()
'''
training task2, predict 3 labels
'''
joint_embed_input_sents = init_embeddings[joint_sents_ids.flatten(
)].reshape((batch_size * cand_size, sent_len, emb_size)).dimshuffle(
0, 2, 1
) #embed_input(init_embeddings, sents_ids_l)#embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
joint_embed_input_claim = init_embeddings[
joint_claim_ids.flatten()].reshape(
(batch_size, claim_len, emb_size)).dimshuffle(0, 2, 1)
joint_conv_model_sents = Conv_with_Mask(
rng,
input_tensor3=joint_embed_input_sents,
mask_matrix=joint_sents_mask.reshape(
(joint_sents_mask.shape[0] * joint_sents_mask.shape[1],
joint_sents_mask.shape[2])),
image_shape=(batch_size * cand_size, 1, emb_size, sent_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
W=conv_W,
b=conv_b
) #mutiple mask with the conv_out to set the features by UNK to zero
joint_sent_embeddings = joint_conv_model_sents.maxpool_vec #(batch_size*cand_size, hidden_size) # each sentence then have an embedding of length hidden_size
joint_batch_sent_emb = joint_sent_embeddings.reshape(
(batch_size, cand_size, hidden_size[0]))
joint_premise_emb = T.sum(joint_batch_sent_emb *
joint_sents_labels.dimshuffle(0, 1, 'x'),
axis=1) #(batch, hidden_size)
joint_conv_model_claims = Conv_with_Mask(
rng,
input_tensor3=joint_embed_input_claim,
mask_matrix=joint_claim_mask,
image_shape=(batch_size, 1, emb_size, claim_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
W=conv_W,
b=conv_b
) #mutiple mask with the conv_out to set the features by UNK to zero
joint_claim_embeddings = joint_conv_model_claims.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
joint_premise_hypo_emb = T.concatenate(
[joint_premise_emb, joint_claim_embeddings],
axis=1) #(batch, 2*hidden_size)
'''
attentive conv in task2
'''
joint_sents_tensor3 = joint_embed_input_sents.dimshuffle(0, 2, 1).reshape(
(batch_size, cand_size * sent_len, emb_size))
joint_sents_dot = T.batched_dot(
joint_sents_tensor3, joint_sents_tensor3.dimshuffle(
0, 2, 1)) #(batch_size, cand_size*sent_len, cand_size*sent_len)
joint_sents_dot_2_matrix = T.nnet.softmax(
joint_sents_dot.reshape(
(batch_size * cand_size * sent_len, cand_size * sent_len)))
joint_sents_context = T.batched_dot(
joint_sents_dot_2_matrix.reshape(
(batch_size, cand_size * sent_len, cand_size * sent_len)),
joint_sents_tensor3) #(batch_size, cand_size*sent_len, emb_size)
joint_add_sents_context = joint_embed_input_sents + joint_sents_context.reshape(
(batch_size * cand_size, sent_len, emb_size)
).dimshuffle(
0, 2, 1
) #T.concatenate([joint_embed_input_sents, joint_sents_context.reshape((batch_size*cand_size, sent_len, emb_size)).dimshuffle(0,2,1)], axis=1) #(batch_size*cand_size, 2*emb_size, sent_len)
attentive_conv_layer = Attentive_Conv_for_Pair_easy_version(
rng,
input_tensor3=
joint_add_sents_context, #batch_size*cand_size, 2*emb_size, sent_len
input_tensor3_r=T.repeat(joint_embed_input_claim, cand_size, axis=0),
mask_matrix=joint_sents_mask.reshape(
(joint_sents_mask.shape[0] * joint_sents_mask.shape[1],
joint_sents_mask.shape[2])),
mask_matrix_r=T.repeat(joint_claim_mask, cand_size, axis=0),
image_shape=(batch_size * cand_size, 1, emb_size, sent_len),
image_shape_r=(batch_size * cand_size, 1, emb_size, claim_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[0], 1, emb_size, 1),
W=att_conv_W,
b=att_conv_b,
W_context=conv_W_context,
b_context=conv_b_context)
attentive_sent_embeddings_l = attentive_conv_layer.attentive_maxpool_vec_l.reshape(
(batch_size, cand_size,
hidden_size[0])) #(batch_size*cand_size, hidden_size)
attentive_sent_embeddings_r = attentive_conv_layer.attentive_maxpool_vec_r.reshape(
(batch_size, cand_size, hidden_size[0]))
masked_sents_attconv = attentive_sent_embeddings_l * joint_sents_labels.dimshuffle(
0, 1, 'x')
masked_claim_attconv = attentive_sent_embeddings_r * joint_sents_labels.dimshuffle(
0, 1, 'x')
fine_max = T.concatenate([
T.max(masked_sents_attconv, axis=1),
T.max(masked_claim_attconv, axis=1)
],
axis=1) #(batch, 2*hidden)
# fine_sum = T.concatenate([T.sum(masked_sents_attconv, axis=1),T.sum(masked_claim_attconv, axis=1)],axis=1) #(batch, 2*hidden)
"Logistic Regression layer"
joint_LR_input = T.concatenate([joint_premise_hypo_emb, fine_max], axis=1)
joint_LR_input_size = 2 * hidden_size[0] + 2 * hidden_size[0]
joint_U_a = create_ensemble_para(rng, 3,
joint_LR_input_size) # (input_size, 3)
joint_LR_b = theano.shared(value=np.zeros((3, ),
dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
joint_LR_para = [joint_U_a, joint_LR_b]
joint_layer_LR = LogisticRegression(
rng,
input=joint_LR_input,
n_in=joint_LR_input_size,
n_out=3,
W=joint_U_a,
b=joint_LR_b
) #basically it is a multiplication between weight matrix and input feature vector
joint_loss = joint_layer_LR.negative_log_likelihood(
joint_labels
) #for classification task, we usually used negative log likelihood as loss, the lower the better.
'''
testing
'''
# binarize_prob = T.where( inter_matrix > 0.5, 1, 0) #(batch_size, cand_size
masked_inter_matrix = inter_matrix * sents_labels #(batch, cand_size)
test_premise_emb = T.sum(batch_sent_emb *
masked_inter_matrix.dimshuffle(0, 1, 'x'),
axis=1)
test_premise_hypo_emb = T.concatenate([test_premise_emb, claim_embeddings],
axis=1)
#fine-maxsum
sents_tensor3 = embed_input_sents.dimshuffle(0, 2, 1).reshape(
(batch_size, cand_size * sent_len, emb_size))
sents_dot = T.batched_dot(sents_tensor3, sents_tensor3.dimshuffle(
0, 2, 1)) #(batch_size, cand_size*sent_len, cand_size*sent_len)
sents_dot_2_matrix = T.nnet.softmax(
sents_dot.reshape(
(batch_size * cand_size * sent_len, cand_size * sent_len)))
sents_context = T.batched_dot(
sents_dot_2_matrix.reshape(
(batch_size, cand_size * sent_len, cand_size * sent_len)),
sents_tensor3) #(batch_size, cand_size*sent_len, emb_size)
add_sents_context = embed_input_sents + sents_context.reshape(
(batch_size * cand_size, sent_len, emb_size)
).dimshuffle(
0, 2, 1
) #T.concatenate([embed_input_sents, sents_context.reshape((batch_size*cand_size, sent_len, emb_size)).dimshuffle(0,2,1)], axis=1) #(batch_size*cand_size, 2*emb_size, sent_len)
test_attentive_conv_layer = Attentive_Conv_for_Pair_easy_version(
rng,
input_tensor3=
add_sents_context, #batch_size*cand_size, 2*emb_size, sent_len
input_tensor3_r=T.repeat(embed_input_claim, cand_size, axis=0),
mask_matrix=sents_mask.reshape(
(sents_mask.shape[0] * sents_mask.shape[1], sents_mask.shape[2])),
mask_matrix_r=T.repeat(claim_mask, cand_size, axis=0),
image_shape=(batch_size * cand_size, 1, emb_size, sent_len),
image_shape_r=(batch_size * cand_size, 1, emb_size, claim_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[0], 1, emb_size, 1),
W=att_conv_W,
b=att_conv_b,
W_context=conv_W_context,
b_context=conv_b_context)
# attentive_sent_embeddings_l = attentive_conv_layer.attentive_maxpool_vec_l #(batch_size*cand_size, hidden_size)
# attentive_sent_embeddings_r = attentive_conv_layer.attentive_maxpool_vec_r
test_attentive_sent_embeddings_l = test_attentive_conv_layer.attentive_maxpool_vec_l.reshape(
(batch_size, cand_size,
hidden_size[0])) #(batch_size*cand_size, hidden_size)
test_attentive_sent_embeddings_r = test_attentive_conv_layer.attentive_maxpool_vec_r.reshape(
(batch_size, cand_size, hidden_size[0]))
test_masked_sents_attconv = test_attentive_sent_embeddings_l * masked_inter_matrix.dimshuffle(
0, 1, 'x')
test_masked_claim_attconv = test_attentive_sent_embeddings_r * masked_inter_matrix.dimshuffle(
0, 1, 'x')
test_fine_max = T.concatenate([
T.max(test_masked_sents_attconv, axis=1),
T.max(test_masked_claim_attconv, axis=1)
],
axis=1) #(batch, 2*hidden)
# test_fine_sum = T.concatenate([T.sum(test_masked_sents_attconv, axis=1),T.sum(test_masked_claim_attconv, axis=1)],axis=1) #(batch, 2*hidden)
test_LR_input = T.concatenate([test_premise_hypo_emb, test_fine_max],
axis=1)
test_LR_input_size = joint_LR_input_size
test_layer_LR = LogisticRegression(
rng,
input=test_LR_input,
n_in=test_LR_input_size,
n_out=3,
W=joint_U_a,
b=joint_LR_b
) #basically it is a multiplication between weight matrix and input feature vector
params = [init_embeddings] + NN_para + LR_para + joint_LR_para
cost = loss + joint_loss
"Use AdaGrad to update parameters"
updates = Gradient_Cost_Para(cost, params, learning_rate)
train_model = theano.function([
sents_ids, sents_mask, sents_labels, claim_ids, claim_mask,
joint_sents_ids, joint_sents_mask, joint_sents_labels, joint_claim_ids,
joint_claim_mask, joint_labels
],
cost,
updates=updates,
allow_input_downcast=True,
on_unused_input='ignore')
# dev_model = theano.function([sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels], layer_LR.errors(labels), allow_input_downcast=True, on_unused_input='ignore')
test_model = theano.function([
sents_ids, sents_mask, sents_labels, claim_ids, claim_mask,
joint_labels
], [
inter_matrix,
test_layer_LR.errors(joint_labels), test_layer_LR.y_pred
],
allow_input_downcast=True,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
joint_n_train_batches = joint_train_size / batch_size
joint_train_batch_start = list(
np.arange(joint_n_train_batches) *
batch_size) + [joint_train_size - batch_size]
n_train_batches = train_size / batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
n_test_batches = test_size / batch_size
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
n_test_3th_batches = test_3th_size / batch_size
test_3th_batch_start = list(np.arange(n_test_3th_batches) *
batch_size) + [test_3th_size - batch_size]
max_acc = 0.0
max_test_f1 = 0.0
max_acc_full_evi = 0.0
cost_i = 0.0
joint_train_indices = range(joint_train_size)
train_indices = range(train_size)
while epoch < n_epochs:
epoch = epoch + 1
random.Random(100).shuffle(
joint_train_indices
) #shuffle training set for each new epoch, is supposed to promote performance, but not garrenteed
random.Random(100).shuffle(train_indices)
iter_accu = 0
for joint_batch_id in joint_train_batch_start: #for each batch
# iter means how many batches have been run, taking into loop
iter = (epoch - 1) * joint_n_train_batches + iter_accu + 1
iter_accu += 1
joint_train_id_batch = joint_train_indices[
joint_batch_id:joint_batch_id + batch_size]
for i in range(3):
batch_id = random.choice(train_batch_start)
train_id_batch = train_indices[batch_id:batch_id + batch_size]
cost_i += train_model(
train_sents[train_id_batch],
train_sent_masks[train_id_batch],
train_sent_labels[train_id_batch],
train_claims[train_id_batch],
train_claim_mask[train_id_batch],
#joint_sents_ids,joint_sents_mask,joint_sents_labels, joint_claim_ids, joint_claim_mask, joint_labels
joint_train_sents[joint_train_id_batch],
joint_train_sent_masks[joint_train_id_batch],
joint_train_sent_labels[joint_train_id_batch],
joint_train_claims[joint_train_id_batch],
joint_train_claim_mask[joint_train_id_batch],
joint_train_labels[joint_train_id_batch])
#after each 1000 batches, we test the performance of the model on all test data
# if (epoch==1 and iter%1000==0) or (epoch>=2 and iter%5==0):
if iter % 100 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
past_time = time.time()
f1_sum = 0.0
error_sum = 0.0
full_evi = 0
predictions = []
for test_batch_id in test_batch_start: # for each test batch
batch_prob, error_i, pred_i = test_model(
test_sents[test_batch_id:test_batch_id + batch_size],
test_sent_masks[test_batch_id:test_batch_id +
batch_size],
test_sent_labels[test_batch_id:test_batch_id +
batch_size],
test_claims[test_batch_id:test_batch_id + batch_size],
test_claim_mask[test_batch_id:test_batch_id +
batch_size],
test_labels[test_batch_id:test_batch_id + batch_size])
error_sum += error_i
batch_sent_labels = test_sent_labels[
test_batch_id:test_batch_id + batch_size]
batch_sent_names = test_sent_names[
test_batch_id:test_batch_id + batch_size]
batch_ground_names = test_ground_names[
test_batch_id:test_batch_id + batch_size]
batch_ground_labels = test_labels[
test_batch_id:test_batch_id + batch_size]
for i in range(batch_size):
instance_i = {}
instance_i['label'] = pred_id2label.get(
batch_ground_labels[i])
instance_i['predicted_label'] = pred_id2label.get(
pred_i[i])
pred_sent_names = []
gold_sent_names = batch_ground_names[i]
zipped = [(batch_prob[i, k], batch_sent_labels[i][k],
batch_sent_names[i][k])
for k in range(cand_size)]
sorted_zip = sorted(zipped,
key=lambda x: x[0],
reverse=True)
for j in range(cand_size):
triple = sorted_zip[j]
if triple[1] == 1.0:
'''
we should consider a rank, instead of binary
if triple[0] >0.5: can control the recall, influence the strict_acc
'''
if triple[0] > 0.5:
# pred_sent_names.append(batch_sent_names[i][j])
pred_sent_names.append(triple[2])
# if len(pred_sent_names) == max_pred_pick:
# break
instance_i['predicted_evidence'] = pred_sent_names
# print 'pred_sent_names:',pred_sent_names
# print 'gold_sent_names:',gold_sent_names
new_gold_names = []
for gold_name in gold_sent_names:
new_gold_names.append([None, None] + gold_name)
instance_i['evidence'] = [new_gold_names]
predictions.append(instance_i)
strict_score, label_accuracy, precision, recall, f1 = fever_score(
predictions)
print 'strict_score, label_accuracy, precision, recall, f1: ', strict_score, label_accuracy, precision, recall, f1
# test_f1=f1_sum/(len(test_batch_start)*batch_size)
for test_batch_id in test_3th_batch_start: # for each test batch
_, error_i, pred_i = test_model(
test_3th_sents[test_batch_id:test_batch_id +
batch_size],
test_3th_sent_masks[test_batch_id:test_batch_id +
batch_size],
test_3th_sent_labels[test_batch_id:test_batch_id +
batch_size],
test_3th_claims[test_batch_id:test_batch_id +
batch_size],
test_3th_claim_mask[test_batch_id:test_batch_id +
batch_size],
test_3th_labels[test_batch_id:test_batch_id +
batch_size])
for i in range(batch_size):
instance_i = {}
instance_i['label'] = pred_id2label.get(2)
instance_i['predicted_label'] = pred_id2label.get(
pred_i[i])
instance_i['predicted_evidence'] = []
instance_i['evidence'] = []
predictions.append(instance_i)
strict_score, label_accuracy, precision, recall, f1 = fever_score(
predictions)
print 'strict_score, label_accuracy, precision, recall, f1: ', strict_score, label_accuracy, precision, recall, f1
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
return max_acc_test
def evaluate_lenet5(learning_rate=0.01,
n_epochs=10,
L2_weight=0.000001,
extra_size=4,
emb_size=300,
posi_emb_size=50,
batch_size=50,
filter_size=[3, 3],
maxSentLen=50,
hidden_size=300):
model_options = locals().copy()
print "model options", model_options
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
all_sentences_l, all_masks_l, all_sentences_r, all_masks_r, all_labels, word2id = load_SciTailV1_dataset(
maxlen=maxSentLen
) #minlen, include one label, at least one word in the sentence
# test_sents_l, test_masks_l, test_sents_r, test_masks_r, test_labels, word2id =load_ACE05_dataset(maxSentLen, word2id)
train_sents_l = np.asarray(all_sentences_l[0], dtype='int32')
dev_sents_l = np.asarray(all_sentences_l[1], dtype='int32')
test_sents_l = np.asarray(all_sentences_l[2], dtype='int32')
train_masks_l = np.asarray(all_masks_l[0], dtype=theano.config.floatX)
dev_masks_l = np.asarray(all_masks_l[1], dtype=theano.config.floatX)
test_masks_l = np.asarray(all_masks_l[2], dtype=theano.config.floatX)
train_sents_r = np.asarray(all_sentences_r[0], dtype='int32')
dev_sents_r = np.asarray(all_sentences_r[1], dtype='int32')
test_sents_r = np.asarray(all_sentences_r[2], dtype='int32')
train_masks_r = np.asarray(all_masks_r[0], dtype=theano.config.floatX)
dev_masks_r = np.asarray(all_masks_r[1], dtype=theano.config.floatX)
test_masks_r = np.asarray(all_masks_r[2], dtype=theano.config.floatX)
train_labels_store = np.asarray(all_labels[0], dtype='int32')
dev_labels_store = np.asarray(all_labels[1], dtype='int32')
test_labels_store = np.asarray(all_labels[2], dtype='int32')
train_size = len(train_labels_store)
dev_size = len(dev_labels_store)
test_size = len(test_labels_store)
print 'train size: ', train_size, ' dev size: ', dev_size, ' test size: ', test_size
vocab_size = len(word2id) + 1
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
#here, we leave code for loading word2vec to initialize words
rand_values[0] = np.array(np.zeros(emb_size), dtype=theano.config.floatX)
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_word2vec()
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
init_embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
posi_rand_values = rng.normal(
0.0, 0.01,
(maxSentLen,
posi_emb_size)) #generate a matrix by Gaussian distribution
posi_embeddings = theano.shared(
value=np.array(posi_rand_values, dtype=theano.config.floatX),
borrow=True
) #wrap up the python variable "rand_values" into theano variable
#now, start to build the input form of the model
sents_ids_l = T.imatrix()
sents_mask_l = T.fmatrix()
sents_ids_r = T.imatrix()
sents_mask_r = T.fmatrix()
labels = T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
def embed_input(emb_matrix, sent_ids):
return emb_matrix[sent_ids.flatten()].reshape(
(batch_size, maxSentLen, emb_size)).dimshuffle(0, 2, 1)
embed_input_l = embed_input(
init_embeddings, sents_ids_l
) #embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
embed_input_r = embed_input(
init_embeddings, sents_ids_r
) #embeddings[sents_ids_r.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1)
'''create_AttentiveConv_params '''
conv_W, conv_b = create_conv_para(rng,
filter_shape=(hidden_size, 1, emb_size,
filter_size[0]))
conv_W_posi, conv_b_posi = create_conv_para(
rng,
filter_shape=(hidden_size, 1, emb_size + posi_emb_size,
filter_size[0]))
conv_W_context, conv_b_context = create_conv_para(
rng, filter_shape=(hidden_size, 1, emb_size, 1))
NN_para = [conv_W, conv_b, conv_W_posi, conv_b_posi, conv_W_context]
'''
attentive convolution function
'''
attentive_conv_layer = Conv_for_Pair(
rng,
origin_input_tensor3=embed_input_l,
origin_input_tensor3_r=embed_input_r,
input_tensor3=embed_input_l,
input_tensor3_r=embed_input_r,
mask_matrix=sents_mask_l,
mask_matrix_r=sents_mask_r,
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size, 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size, 1, emb_size, 1),
W=conv_W,
b=conv_b,
W_posi=conv_W_posi,
b_posi=conv_b_posi,
W_context=conv_W_context,
b_context=conv_b_context,
posi_emb_matrix=posi_embeddings,
posi_emb_size=posi_emb_size)
attentive_sent_embeddings_l = attentive_conv_layer.attentive_maxpool_vec_l
attentive_sent_embeddings_r = attentive_conv_layer.attentive_maxpool_vec_r
sent_embeddings_l = attentive_conv_layer.maxpool_vec_l
sent_embeddings_r = attentive_conv_layer.maxpool_vec_r
"form input to LR classifier"
LR_input = T.concatenate([
sent_embeddings_l, sent_embeddings_r,
sent_embeddings_l * sent_embeddings_r, attentive_sent_embeddings_l,
attentive_sent_embeddings_r,
attentive_sent_embeddings_l * attentive_sent_embeddings_r
],
axis=1)
LR_input_size = 6 * hidden_size
U_a = create_ensemble_para(
rng, 2, LR_input_size) # the weight matrix hidden_size*2
LR_b = theano.shared(value=np.zeros((2, ), dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
LR_para = [U_a, LR_b]
layer_LR = LogisticRegression(
rng, input=LR_input, n_in=LR_input_size, n_out=2, W=U_a, b=LR_b
) #basically it is a multiplication between weight matrix and input feature vector
loss = layer_LR.negative_log_likelihood(
labels
) #for classification task, we usually used negative log likelihood as loss, the lower the better.
params = [init_embeddings, posi_embeddings] + NN_para + LR_para
# L2_reg = (init_embeddings**2).sum()+(conv_W**2).sum()+(conv_W_context**2).sum()+(U_a**2).sum()
cost = loss #+L2_weight*L2_reg
updates = Gradient_Cost_Para(cost, params, learning_rate)
train_model = theano.function(
[sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels],
cost,
updates=updates,
allow_input_downcast=True,
on_unused_input='ignore')
dev_model = theano.function(
[sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels],
layer_LR.errors(labels),
allow_input_downcast=True,
on_unused_input='ignore')
test_model = theano.function(
[sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels],
layer_LR.errors(labels),
allow_input_downcast=True,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
n_train_batches = train_size / batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
n_dev_batches = dev_size / batch_size
dev_batch_start = list(
np.arange(n_dev_batches) * batch_size) + [dev_size - batch_size]
n_test_batches = test_size / batch_size
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
max_acc_dev = 0.0
max_acc_test = 0.0
max_f1 = 0.0
cost_i = 0.0
train_indices = range(train_size)
while epoch < n_epochs:
epoch = epoch + 1
random.Random(100).shuffle(
train_indices
) #shuffle training set for each new epoch, is supposed to promote performance, but not garrenteed
iter_accu = 0
for batch_id in train_batch_start: #for each batch
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
train_id_batch = train_indices[batch_id:batch_id + batch_size]
cost_i += train_model(train_sents_l[train_id_batch],
train_masks_l[train_id_batch],
train_sents_r[train_id_batch],
train_masks_r[train_id_batch],
train_labels_store[train_id_batch])
#after each 1000 batches, we test the performance of the model on all test data
if iter % 100 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
past_time = time.time()
dev_error_sum = 0.0
for dev_batch_id in dev_batch_start: # for each test batch
dev_error_i = dev_model(
dev_sents_l[dev_batch_id:dev_batch_id + batch_size],
dev_masks_l[dev_batch_id:dev_batch_id + batch_size],
dev_sents_r[dev_batch_id:dev_batch_id + batch_size],
dev_masks_r[dev_batch_id:dev_batch_id + batch_size],
dev_labels_store[dev_batch_id:dev_batch_id +
batch_size])
dev_error_sum += dev_error_i
dev_acc = 1.0 - dev_error_sum / (len(dev_batch_start))
if dev_acc > max_acc_dev:
max_acc_dev = dev_acc
print '\tcurrent dev_acc:', dev_acc, ' ; ', '\tmax_dev_acc:', max_acc_dev
error_sum = 0.0
for idd, test_batch_id in enumerate(
test_batch_start): # for each test batch
error_i = test_model(
test_sents_l[test_batch_id:test_batch_id +
batch_size],
test_masks_l[test_batch_id:test_batch_id +
batch_size],
test_sents_r[test_batch_id:test_batch_id +
batch_size],
test_masks_r[test_batch_id:test_batch_id +
batch_size],
test_labels_store[test_batch_id:test_batch_id +
batch_size])
error_sum += error_i
test_acc = 1.0 - error_sum / (len(test_batch_start))
if test_acc > max_acc_test:
max_acc_test = test_acc
store_model_to_file(
'/home/wenpeng/workspace/SciTail/src/model_para_' +
str(max_acc_test), params)
print '\t\tcurrent acc:', test_acc, ' ; ', '\t\tmax_acc:', max_acc_test
else:
print '\tcurrent dev_acc:', dev_acc, ' ; ', '\tmax_dev_acc:', max_acc_dev
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
return max_acc_test
def evaluate_lenet5(learning_rate=0.01, n_epochs=2000, batch_size=500, test_batch_size=1000, emb_size=300, hidden_size=300, HL_hidden_size=200,
L2_weight=0.0001, train_size=None, test_size=None, batch_size_pred=1000,
para_len=60, question_len=20, c_len=7, e_len=2):
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/work/hs/yin/20161219/';
storePath='/mounts/data/proj/wenpeng/Dataset/SQuAD/'
rng = np.random.RandomState(23455)
word2id={}
word2id['UNK']=0 # use it to pad
word2id, train_questions,train_questions_mask,train_paras,train_paras_mask,train_e_ids,train_e_masks,train_c_ids,train_c_masks, train_c_heads,train_c_tails,train_l_heads,train_l_tails,train_e_heads,train_e_tails,train_labels, train_labels_3c=load_SQUAD_hinrich_v2(train_size, para_len, question_len, e_len, c_len, word2id, rootPath+'squadnewtrn.txt')
word2id, test_questions,test_questions_mask,test_paras,test_paras_mask,test_e_ids,test_e_masks,test_c_ids,test_c_masks, test_c_heads,test_c_tails,test_l_heads,test_l_tails,test_e_heads,test_e_tails,test_labels, test_labels_3c=load_SQUAD_hinrich_v2(test_size, para_len, question_len, e_len, c_len,word2id, rootPath+'squadnewdev.txt')
print 'word2id size for bigger dataset:', len(word2id)
word2id, train_questions,train_questions_mask,train_paras,train_paras_mask,train_e_ids,train_e_masks,train_c_ids,train_c_masks, train_c_heads,train_c_tails,train_l_heads,train_l_tails,train_e_heads,train_e_tails,train_labels, train_labels_3c=load_SQUAD_hinrich_v2(train_size, para_len, question_len,e_len, c_len, word2id, rootPath+'squadnewtrn,subset.000.txt')
word2id, test_questions,test_questions_mask,test_paras,test_paras_mask,test_e_ids,test_e_masks,test_c_ids,test_c_masks, test_c_heads,test_c_tails,test_l_heads,test_l_tails,test_e_heads,test_e_tails,test_labels, test_labels_3c=load_SQUAD_hinrich_v2(test_size, para_len, question_len, e_len, c_len,word2id, rootPath+'squadnewdev,subset.000.txt')
print 'word2id size for smaller dataset:', len(word2id)
# if len(train_questions)!=train_size or len(test_questions)!=test_size:
# print 'len(questions)!=train_size or len(test_questions)!=test_size:', len(train_questions),train_size,len(test_questions),test_size
# exit(0)
train_size=len(train_questions)
test_size = len(test_questions)
train_questions = np.asarray(train_questions, dtype='int32')
# print train_questions[:10,:]
# exit(0)
train_questions_mask = np.asarray(train_questions_mask, dtype=theano.config.floatX)
train_paras = np.asarray(train_paras, dtype='int32')
train_paras_mask = np.asarray(train_paras_mask, dtype=theano.config.floatX)
train_e_ids = np.asarray(train_e_ids, dtype='int32')
train_e_masks = np.asarray(train_e_masks, dtype=theano.config.floatX)
train_c_ids = np.asarray(train_c_ids, dtype='int32')
train_c_masks = np.asarray(train_c_masks, dtype=theano.config.floatX)
train_c_heads = np.asarray(train_c_heads, dtype='int32')
train_c_tails = np.asarray(train_c_tails, dtype='int32')
train_l_heads = np.asarray(train_l_heads, dtype='int32')
train_l_tails = np.asarray(train_l_tails, dtype='int32')
train_e_heads = np.asarray(train_e_heads, dtype='int32')
train_e_tails = np.asarray(train_e_tails, dtype='int32')
train_labels = np.asarray(train_labels, dtype='int32')
train_labels_3c = np.asarray(train_labels_3c, dtype='int32')
test_questions = np.asarray(test_questions, dtype='int32')
test_questions_mask = np.asarray(test_questions_mask, dtype=theano.config.floatX)
test_paras = np.asarray(test_paras, dtype='int32')
test_paras_mask = np.asarray(test_paras_mask, dtype=theano.config.floatX)
test_e_ids = np.asarray(test_e_ids, dtype='int32')
test_e_masks = np.asarray(test_e_masks, dtype=theano.config.floatX)
test_c_ids = np.asarray(test_c_ids, dtype='int32')
test_c_masks = np.asarray(test_c_masks, dtype=theano.config.floatX)
test_c_heads = np.asarray(test_c_heads, dtype='int32')
test_c_tails = np.asarray(test_c_tails, dtype='int32')
test_l_heads = np.asarray(test_l_heads, dtype='int32')
test_l_tails = np.asarray(test_l_tails, dtype='int32')
test_e_heads = np.asarray(test_e_heads, dtype='int32')
test_e_tails = np.asarray(test_e_tails, dtype='int32')
test_labels = np.asarray(test_labels, dtype='int32')
overall_vocab_size=len(word2id)
print 'train size:', train_size, 'test size:', test_size, 'vocab size:', overall_vocab_size
rand_values=random_value_normal((overall_vocab_size+1, emb_size), theano.config.floatX, rng)
rand_values[0]=np.array(np.zeros(emb_size),dtype=theano.config.floatX)
id2word = {y:x for x,y in word2id.iteritems()}
word2vec=load_word2vec()
rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings=theano.shared(value=rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
para=T.imatrix() #(2*batch, len)
para_mask=T.fmatrix() #(2*batch, len)
c_ids=T.imatrix() #(2*batch, len)
c_mask=T.fmatrix() #(2*batch, len)
e_ids=T.imatrix() #(2*batch, len)
e_mask=T.fmatrix() #(2*batch, len)
c_heads=T.ivector() #batch
c_tails=T.ivector() #batch
l_heads=T.ivector() #batch
l_tails=T.ivector() #batch
e_heads=T.ivector() #batch
e_tails=T.ivector() #batch
q=T.imatrix() #(2*batch, len_q)
q_mask=T.fmatrix() #(2*batch, len_q)
labels=T.ivector() #batch
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
true_batch_size = para.shape[0]
# U_p, W_p, b_p=create_GRU_para(rng, emb_size, hidden_size)
# U_p_b, W_p_b, b_p_b=create_GRU_para(rng, emb_size, hidden_size)
# GRU_p_para=[U_p, W_p, b_p, U_p_b, W_p_b, b_p_b]
#
# U_q, W_q, b_q=create_GRU_para(rng, emb_size, hidden_size)
# U_q_b, W_q_b, b_q_b=create_GRU_para(rng, emb_size, hidden_size)
# GRU_q_para=[U_q, W_q, b_q, U_q_b, W_q_b, b_q_b]
paragraph_input = embeddings[para.flatten()].reshape((true_batch_size, para_len, emb_size)).transpose((0, 2,1)) #(batch, emb_size, para_len)
q_input = embeddings[q.flatten()].reshape((true_batch_size, question_len, emb_size)).transpose((0, 2,1)) # (batch, emb_size, question_len)
fwd_LSTM_para_dict=create_LSTM_para(rng, emb_size, hidden_size)
bwd_LSTM_para_dict=create_LSTM_para(rng, emb_size, hidden_size)
paragraph_para=fwd_LSTM_para_dict.values()+ bwd_LSTM_para_dict.values()# .values returns a list of parameters
paragraph_model=Bd_LSTM_Batch_Tensor_Input_with_Mask_Concate(paragraph_input, para_mask, hidden_size, fwd_LSTM_para_dict, bwd_LSTM_para_dict)
paragraph_reps_tensor3=paragraph_model.output_tensor #(batch, 2*hidden, paralen)
# paragraph_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=paragraph_input, Mask=para_mask, hidden_dim=hidden_size,U=U_p,W=W_p,b=b_p,Ub=U_p_b,Wb=W_p_b,bb=b_p_b)
# paragraph_reps_tensor3=paragraph_model.output_tensor_conc #(batch, 2*hidden, para_len)
fwd_LSTM_q_dict=create_LSTM_para(rng, emb_size, hidden_size)
bwd_LSTM_q_dict=create_LSTM_para(rng, emb_size, hidden_size)
question_para=fwd_LSTM_q_dict.values()+ bwd_LSTM_q_dict.values()# .values returns a list of parameters
questions_model=Bd_LSTM_Batch_Tensor_Input_with_Mask_Concate(q_input, q_mask, hidden_size, fwd_LSTM_q_dict, bwd_LSTM_q_dict)
q_reps=questions_model.output_sent_rep_maxpooling #(batch, 2*hidden)
# q_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=q_input, Mask=q_mask, hidden_dim=hidden_size,U=U_q,W=W_q,b=b_q,Ub=U_q_b,Wb=W_q_b,bb=b_q_b)
# q_reps=q_model.output_sent_rep_conc #(batch, 2*hidden)
#interaction
batch_ids=T.arange(true_batch_size)
c_heads_reps=paragraph_reps_tensor3[batch_ids,:,c_heads] #(batch, 2*hidden)
c_tails_reps=paragraph_reps_tensor3[batch_ids,:,c_tails] #(batch, 2*hidden)
candididates_reps=T.concatenate([c_heads_reps, c_tails_reps], axis=1) #(batch, 4*hidden)
l_heads_reps=paragraph_reps_tensor3[batch_ids,:,l_heads] #(batch, 2*hidden)
l_tails_reps=paragraph_reps_tensor3[batch_ids,:,l_tails] #(batch, 2*hidden)
longs_reps=T.concatenate([l_heads_reps, l_tails_reps], axis=1) #(batch, 4*hidden)
e_heads_reps=paragraph_reps_tensor3[batch_ids,:,e_heads] #(batch, 2*hidden)
e_tails_reps=paragraph_reps_tensor3[batch_ids,:,e_tails] #(batch, 2*hidden)
extensions_reps=T.concatenate([e_heads_reps, e_tails_reps], axis=1) #(batch, 4*hidden)
#glove level average
c_input = embeddings[c_ids.flatten()].reshape((true_batch_size, c_len, emb_size)).transpose((0, 2,1)) #(batch, emb_size, c_len)
c_sum = T.sum(c_input*c_mask.dimshuffle(0,'x',1), axis=2) #(batch, emb_size)
average_C_batch = c_sum/T.sqrt(T.sum(c_sum**2, axis=1)+1e-20).dimshuffle(0,'x')
e_input = embeddings[e_ids.flatten()].reshape((true_batch_size, e_len, emb_size)).transpose((0, 2,1)) #(batch, emb_size, c_len)
e_sum = T.sum(e_input*e_mask.dimshuffle(0,'x',1), axis=2) #(batch, emb_size)
average_E_batch = e_sum/T.sqrt(T.sum(e_sum**2, axis=1)+1e-20).dimshuffle(0,'x')
# e_input = embeddings[e_ids.flatten()].reshape((true_batch_size, e_len, emb_size)).transpose((0, 2,1)) #(batch, emb_size, c_len)
q_sum = T.sum(q_input*q_mask.dimshuffle(0,'x',1), axis=2) #(batch, emb_size)
average_Q_batch = q_sum/T.sqrt(T.sum(q_sum**2, axis=1)+1e-20).dimshuffle(0,'x')
# def submatrix_average(matrix, head, tail):
# return T.mean(matrix[:, head:tail+1], axis=1) #emb_size
# def submatrix_average_q(matrix, head):
# return T.mean(matrix[:, head:], axis=1) #emb_size
#
# average_E_batch, _ = theano.scan(fn=submatrix_average,
# sequences=[paragraph_input,e_heads, e_tails]) #(batch, emb_size)
# average_C_batch, _ = theano.scan(fn=submatrix_average,
# sequences=[paragraph_input,c_heads, c_tails]) #(batch, emb_size)
#
# Q_valid_len=T.cast(T.sum(q_mask, axis=1), 'int32')
#
# average_Q_batch, _ = theano.scan(fn=submatrix_average_q,
# sequences=[q_input,-Q_valid_len]) #(batch, emb_size)
#classify
HL_layer_subtask_input=T.concatenate([q_reps, extensions_reps, average_E_batch, average_Q_batch], axis=1) #(batch, 6*hidden+2*emb)
HL_layer_subtask_size= 6*hidden_size+2*emb_size#HL_layer_1_input_size+2*HL_hidden_size
HL_layer_subtask_1=HiddenLayer(rng, input=HL_layer_subtask_input, n_in=HL_layer_subtask_size, n_out=HL_hidden_size, activation=T.tanh)
HL_layer_subtask_2=HiddenLayer(rng, input=HL_layer_subtask_1.output, n_in=HL_hidden_size, n_out=HL_hidden_size, activation=T.tanh)
U_subtask_a = create_ensemble_para(rng, 2, HL_hidden_size) # the weight matrix hidden_size*2
norm_U_subtask_a=normalize_matrix(U_subtask_a)
LR_subtask_b = theano.shared(value=np.zeros((2,),dtype=theano.config.floatX),name='LR_b', borrow=True) #bias for each target class
LR_subtask_para=[U_subtask_a, LR_subtask_b]
layer_LR_subtask=LogisticRegression(rng, input=HL_layer_subtask_2.output, n_in=HL_hidden_size, n_out=2, W=norm_U_subtask_a, b=LR_subtask_b) #basically it is a multiplication between weight matrix and input feature vector
HL_layer_1_input_size=14*hidden_size+3*emb_size+1
#, average_E_batch, average_C_batch, average_Q_batch
HL_layer_1_input = T.concatenate([q_reps, longs_reps, extensions_reps, candididates_reps, average_E_batch, average_C_batch, average_Q_batch, layer_LR_subtask.prop_for_posi.reshape((true_batch_size,1))], axis=1) #(batch, 14*hidden_size+3*emb_size+1)
HL_layer_1=HiddenLayer(rng, input=HL_layer_1_input, n_in=HL_layer_1_input_size, n_out=HL_hidden_size, activation=T.tanh)
HL_layer_2=HiddenLayer(rng, input=HL_layer_1.output, n_in=HL_hidden_size, n_out=HL_hidden_size, activation=T.tanh)
LR_input=HL_layer_2.output #T.concatenate([HL_layer_1_input, HL_layer_1.output, HL_layer_2.output], axis=1) #(batch, 10*hidden)
LR_input_size= HL_hidden_size#HL_layer_1_input_size+2*HL_hidden_size
U_a = create_ensemble_para(rng, 2, LR_input_size) # the weight matrix hidden_size*2
norm_U_a=normalize_matrix(U_a)
LR_b = theano.shared(value=np.zeros((2,),dtype=theano.config.floatX),name='LR_b', borrow=True) #bias for each target class
LR_para=[U_a, LR_b]
layer_LR=LogisticRegression(rng, input=LR_input, n_in=LR_input_size, n_out=2, W=norm_U_a, b=LR_b) #basically it is a multiplication between weight matrix and input feature vector
loss=layer_LR.negative_log_likelihood(labels)+layer_LR_subtask.negative_log_likelihood(labels) #for classification task, we usually used negative log likelihood as loss, the lower the better.
params = LR_para+[embeddings]+paragraph_para+question_para+HL_layer_1.params+HL_layer_2.params+LR_subtask_para+HL_layer_subtask_1.params+HL_layer_subtask_2.params
# L2_reg =L2norm_paraList([embeddings,U1, W1, U1_b, W1_b,UQ, WQ , UQ_b, WQ_b, W_a1, W_a2, U_a])
#L2_reg = L2norm_paraList(params)
cost=loss#+0.0005*T.mean(U_a**2)
accumulator=[]
for para_i in params:
eps_p=np.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / (T.sqrt(acc)+1e-20))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([para, para_mask,c_ids,c_mask,e_ids,e_mask, c_heads, c_tails, l_heads, l_tails, e_heads, e_tails, q, q_mask,labels], cost, updates=updates,on_unused_input='ignore')
train_model_pred = theano.function([para, para_mask, c_ids,c_mask,e_ids,e_mask, c_heads, c_tails, l_heads, l_tails, e_heads, e_tails, q, q_mask,labels], layer_LR.y_pred, on_unused_input='ignore')
test_model = theano.function([para, para_mask, c_ids,c_mask,e_ids,e_mask, c_heads, c_tails, l_heads, l_tails, e_heads, e_tails, q, q_mask,labels], [layer_LR.errors(labels),layer_LR.y_pred], on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = np.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches=train_size/batch_size #batch_size means how many pairs
train_batch_start=list(np.arange(n_train_batches)*batch_size)+[train_size-batch_size]
n_train_batches_pred=train_size/batch_size_pred #batch_size means how many pairs
train_batch_start_pred=list(np.arange(n_train_batches_pred)*batch_size_pred)+[train_size-batch_size_pred]
n_test_batches=test_size/test_batch_size #batch_size means how many pairs
test_batch_start=list(np.arange(n_test_batches)*test_batch_size)+[test_size-test_batch_size]
max_acc=0.0
cost_i=0.0
train_ids = range(train_size)
train_ids_pred = range(train_size)
best_test_statistic=defaultdict(int)
# best_train_statistic=defaultdict(int)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.shuffle(train_ids)
# print train_ids[:100]
iter_accu=0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
iter_accu+=1
train_id_list = train_ids[para_id:para_id+batch_size]
# print 'train_labels[train_id_list]:', train_labels[train_id_list]
cost_i+= train_model(
train_paras[train_id_list],
train_paras_mask[train_id_list],
train_c_ids[train_id_list],
train_c_masks[train_id_list],
train_e_ids[train_id_list],
train_e_masks[train_id_list],
train_c_heads[train_id_list],
train_c_tails[train_id_list],
train_l_heads[train_id_list],
train_l_tails[train_id_list],
train_e_heads[train_id_list],
train_e_tails[train_id_list],
train_questions[train_id_list],
train_questions_mask[train_id_list],
train_labels[train_id_list])
#print iter
if iter%10==0: #iter>=200 and
print 'Epoch ', epoch, 'iter '+str(iter)+'/'+str(len(train_batch_start))+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
past_time = time.time()
# print 'Training Pred...'
# train_statistic=defaultdict(int)
# for para_id in train_batch_start_pred:
# train_id_list = train_ids_pred[para_id:para_id+batch_size_pred]
# gold_train_labels_list = train_labels_3c[train_id_list]
# # print 'train_id_list:', train_id_list
# # print 'train_c_heads[train_id_list]:', train_c_heads[train_id_list]
# train_preds_i= train_model_pred(
# train_paras[train_id_list],
# train_paras_mask[train_id_list],
# train_c_ids[train_id_list],
# train_c_masks[train_id_list],
# train_e_ids[train_id_list],
# train_e_masks[train_id_list],
# train_c_heads[train_id_list],
# train_c_tails[train_id_list],
# train_l_heads[train_id_list],
# train_l_tails[train_id_list],
# train_e_heads[train_id_list],
# train_e_tails[train_id_list],
# train_questions[train_id_list],
# train_questions_mask[train_id_list],
# train_labels[train_id_list])
#
# for ind, gold_label in enumerate(gold_train_labels_list):
# train_statistic[(gold_label, train_preds_i[ind])]+=1
# train_acc= (train_statistic.get((1,1),0)+train_statistic.get((0,0),0))*1.0/(train_statistic.get((1,1),0)+train_statistic.get((0,0),0)+train_statistic.get((1,0),0)+train_statistic.get((0,1),0))
#
# print '\t\tcurrnt train acc:', train_acc, ' train_statistic:', train_statistic
print 'Testing...'
error=0
test_statistic=defaultdict(int)
for test_para_id in test_batch_start:
test_id_list = range(test_para_id, test_para_id+test_batch_size)
# print 'test_id_list:',test_id_list
# print 'test_c_heads[test_id_list]', test_c_heads[test_id_list]
gold_labels_list = test_labels_3c[test_para_id:test_para_id+test_batch_size]
error_i, preds_i= test_model(
test_paras[test_id_list],
test_paras_mask[test_id_list],
test_c_ids[test_id_list],
test_c_masks[test_id_list],
test_e_ids[test_id_list],
test_e_masks[test_id_list],
test_c_heads[test_id_list],
test_c_tails[test_id_list],
test_l_heads[test_id_list],
test_l_tails[test_id_list],
test_e_heads[test_id_list],
test_e_tails[test_id_list],
test_questions[test_id_list],
test_questions_mask[test_id_list],
test_labels[test_id_list])
error+=error_i
for ind, gold_label in enumerate(gold_labels_list):
test_statistic[(gold_label, preds_i[ind])]+=1
# acc=1.0-error*1.0/len(test_batch_start)
acc= (test_statistic.get((1,1),0)+test_statistic.get((0,0),0))*1.0/(test_statistic.get((1,1),0)+test_statistic.get((0,0),0)+test_statistic.get((1,0),0)+test_statistic.get((0,1),0))
if acc> max_acc:
max_acc=acc
best_test_statistic=test_statistic
store_model_to_file(storePath+'Best_Paras_HS_v2_000_subtask_'+str(max_acc), params)
print 'Finished storing best params at:', max_acc
print 'current average acc:', acc, '\t\tmax acc:', max_acc, '\ttest_statistic:', test_statistic
print '\t\t\t\tbest statistic:', best_test_statistic
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(claim, title2sentlist, title2wordlist, word2id):
learning_rate = 0.02
n_epochs = 100
emb_size = 300
batch_size = 1 #50
filter_size = [3]
sent_len = 40
claim_len = 40
cand_size = 10
hidden_size = [300, 300]
max_pred_pick = 5
# model_options = locals().copy()
# print("model options", model_options)
# print('title2sentlist len', len(title2sentlist))
# print('title2wordlist len', len(title2wordlist))
pred_id2label = {1: 'SUPPORTS', 0: 'REFUTES', 2: 'NOT ENOUGH INFO'}
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
srng = T.shared_randomstreams.RandomStreams(rng.randint(seed))
claim_idlist, claim_masklist, sent_ins_ids, sent_ins_mask, sent_cand_list = claim_input_2_theano_input(
claim, word2id, claim_len, sent_len, cand_size, title2sentlist,
title2wordlist)
test_claims = np.asarray([claim_idlist], dtype='int32')
test_claim_mask = np.asarray([claim_masklist], dtype=theano.config.floatX)
test_sents = np.asarray([sent_ins_ids], dtype='int32')
test_sent_masks = np.asarray([sent_ins_mask], dtype=theano.config.floatX)
vocab_size = len(word2id) + 1
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
# id2word = {y:x for x,y in word2id.items()}
# word2vec=load_word2vec()
# rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
init_embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
'''
the first block for evidence identification in two classes (support & reject)
the second block for textual entailment: given evidence labels, predict the claim labels
'''
sents_ids = T.itensor3() #(batch, cand_size, sent_len)
sents_mask = T.ftensor3()
# sents_labels=T.imatrix() #(batch, cand_size)
claim_ids = T.imatrix() #(batch, claim_len)
claim_mask = T.fmatrix()
# joint_sents_ids=T.itensor3() #(batch, cand_size, sent_len)
# joint_sents_mask=T.ftensor3()
# # joint_sents_labels=T.imatrix() #(batch, cand_size)
# joint_claim_ids = T.imatrix() #(batch, claim_len)
# joint_claim_mask = T.fmatrix()
# joint_labels=T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print('... building the model')
embed_input_sents = init_embeddings[sents_ids.flatten(
)].reshape((batch_size * cand_size, sent_len, emb_size)).dimshuffle(
0, 2, 1
) #embed_input(init_embeddings, sents_ids_l)#embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
embed_input_claim = init_embeddings[claim_ids.flatten()].reshape(
(batch_size, claim_len, emb_size)).dimshuffle(0, 2, 1)
"shared parameters"
conv_W, conv_b = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size, filter_size[0]))
"tasl 1 parameters"
task1_att_conv_W, task1_att_conv_b = create_conv_para(
rng, filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]))
task1_conv_W_context, task1_conv_b_context = create_conv_para(
rng, filter_shape=(hidden_size[0], 1, emb_size, 1))
"task 2 parameters"
att_conv_W, att_conv_b = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size,
filter_size[0]))
conv_W_context, conv_b_context = create_conv_para(
rng, filter_shape=(hidden_size[0], 1, emb_size, 1))
NN_para = [
conv_W, conv_b, task1_att_conv_W, task1_att_conv_b, att_conv_W,
att_conv_b, task1_conv_W_context, conv_W_context
]
conv_model_sents = Conv_with_Mask(
rng,
input_tensor3=embed_input_sents,
mask_matrix=sents_mask.reshape(
(sents_mask.shape[0] * sents_mask.shape[1], sents_mask.shape[2])),
image_shape=(batch_size * cand_size, 1, emb_size, sent_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
W=conv_W,
b=conv_b
) #mutiple mask with the conv_out to set the features by UNK to zero
sent_embeddings = conv_model_sents.maxpool_vec #(batch_size*cand_size, hidden_size) # each sentence then have an embedding of length hidden_size
batch_sent_emb = sent_embeddings.reshape(
(batch_size, cand_size, hidden_size[0]))
conv_model_claims = Conv_with_Mask(
rng,
input_tensor3=embed_input_claim,
mask_matrix=claim_mask,
image_shape=(batch_size, 1, emb_size, claim_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
W=conv_W,
b=conv_b
) #mutiple mask with the conv_out to set the features by UNK to zero
claim_embeddings = conv_model_claims.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
batch_claim_emb = T.repeat(claim_embeddings.dimshuffle(0, 'x', 1),
cand_size,
axis=1)
'''
attentive conv for task1
'''
task1_attentive_conv_layer = Attentive_Conv_for_Pair_easy_version(
rng,
input_tensor3=
embed_input_sents, #batch_size*cand_size, emb_size, sent_len
input_tensor3_r=T.repeat(embed_input_claim, cand_size, axis=0),
mask_matrix=sents_mask.reshape(
(sents_mask.shape[0] * sents_mask.shape[1], sents_mask.shape[2])),
mask_matrix_r=T.repeat(claim_mask, cand_size, axis=0),
image_shape=(batch_size * cand_size, 1, emb_size, sent_len),
image_shape_r=(batch_size * cand_size, 1, emb_size, claim_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[0], 1, emb_size, 1),
W=task1_att_conv_W,
b=task1_att_conv_b,
W_context=task1_conv_W_context,
b_context=task1_conv_b_context)
task1_attentive_sent_embeddings_l = task1_attentive_conv_layer.attentive_maxpool_vec_l #(batch_size*cand_size, hidden_size)
task1_attentive_sent_embeddings_r = task1_attentive_conv_layer.attentive_maxpool_vec_r
concate_claim_sent = T.concatenate([
batch_claim_emb, batch_sent_emb,
T.sum(batch_claim_emb * batch_sent_emb, axis=2).dimshuffle(0, 1, 'x')
],
axis=2)
concate_2_matrix = concate_claim_sent.reshape(
(batch_size * cand_size, hidden_size[0] * 2 + 1))
"to score each evidence sentence, we use the output of attentiveConv, as well as the output of standard CNN"
LR_input = T.concatenate([
concate_2_matrix, task1_attentive_sent_embeddings_l,
task1_attentive_sent_embeddings_r
],
axis=1)
LR_input_size = hidden_size[0] * 2 + 1 + hidden_size[0] * 2
# LR_input = concate_2_matrix
# LR_input_size = hidden_size[0]*2+1
#classification layer, it is just mapping from a feature vector of size "hidden_size" to a vector of only two values: positive, negative
U_a = create_ensemble_para(
rng, 1, LR_input_size) # the weight matrix hidden_size*2
# LR_b = theano.shared(value=np.zeros((8,),dtype=theano.config.floatX),name='LR_b', borrow=True) #bias for each target class
LR_para = [U_a]
# layer_LR=LogisticRegression(rng, input=LR_input, n_in=LR_input_size, n_out=8, W=U_a, b=LR_b) #basically it is a multiplication between weight matrix and input feature vector
score_matrix = T.nnet.sigmoid(LR_input.dot(U_a)) #batch * 12
inter_matrix = score_matrix.reshape((batch_size, cand_size))
# inter_sent_claim = T.batched_dot(batch_sent_emb, batch_claim_emb) #(batch_size, cand_size, 1)
# inter_matrix = T.nnet.sigmoid(inter_sent_claim.reshape((batch_size, cand_size)))
'''
maybe 1.0-inter_matrix can be rewritten into 1/e^(inter_matrix)
'''
binarize_prob = T.where(inter_matrix > 0.5, 1, 0) #(batch_size, cand_size)
sents_labels = inter_matrix * binarize_prob
'''
training task2, predict 3 labels
'''
# joint_embed_input_sents=init_embeddings[joint_sents_ids.flatten()].reshape((batch_size*cand_size, sent_len, emb_size)).dimshuffle(0,2,1)#embed_input(init_embeddings, sents_ids_l)#embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
# joint_embed_input_claim=init_embeddings[joint_claim_ids.flatten()].reshape((batch_size,claim_len, emb_size)).dimshuffle(0,2,1)
# joint_conv_model_sents = Conv_with_Mask(rng, input_tensor3=joint_embed_input_sents,
# mask_matrix = joint_sents_mask.reshape((joint_sents_mask.shape[0]*joint_sents_mask.shape[1],joint_sents_mask.shape[2])),
# image_shape=(batch_size*cand_size, 1, emb_size, sent_len),
# filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]), W=conv_W, b=conv_b) #mutiple mask with the conv_out to set the features by UNK to zero
# joint_sent_embeddings=joint_conv_model_sents.maxpool_vec #(batch_size*cand_size, hidden_size) # each sentence then have an embedding of length hidden_size
# joint_batch_sent_emb = joint_sent_embeddings.reshape((batch_size, cand_size, hidden_size[0]))
# "??? use joint_sents_labels means the evidence labels are not provided by task 1?"
# joint_premise_emb = T.sum(joint_batch_sent_emb*joint_sents_labels.dimshuffle(0,1,'x'), axis=1) #(batch, hidden_size)
premise_emb = T.sum(batch_sent_emb * sents_labels.dimshuffle(0, 1, 'x'),
axis=1)
# joint_conv_model_claims = Conv_with_Mask(rng, input_tensor3=joint_embed_input_claim,
# mask_matrix = joint_claim_mask,
# image_shape=(batch_size, 1, emb_size, claim_len),
# filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]), W=conv_W, b=conv_b) #mutiple mask with the conv_out to set the features by UNK to zero
# joint_claim_embeddings=joint_conv_model_claims.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
premise_hypo_emb = T.concatenate([premise_emb, claim_embeddings],
axis=1) #(batch, 2*hidden_size)
'''
attentive conv in task2
'''
sents_tensor3 = embed_input_sents.dimshuffle(0, 2, 1).reshape(
(batch_size, cand_size * sent_len, emb_size))
sents_dot = T.batched_dot(sents_tensor3, sents_tensor3.dimshuffle(
0, 2, 1)) #(batch_size, cand_size*sent_len, cand_size*sent_len)
sents_dot_2_matrix = T.nnet.softmax(
sents_dot.reshape(
(batch_size * cand_size * sent_len, cand_size * sent_len)))
sents_context = T.batched_dot(
sents_dot_2_matrix.reshape(
(batch_size, cand_size * sent_len, cand_size * sent_len)),
sents_tensor3) #(batch_size, cand_size*sent_len, emb_size)
add_sents_context = embed_input_sents + sents_context.reshape(
(batch_size * cand_size, sent_len, emb_size)
).dimshuffle(
0, 2, 1
) #T.concatenate([joint_embed_input_sents, joint_sents_context.reshape((batch_size*cand_size, sent_len, emb_size)).dimshuffle(0,2,1)], axis=1) #(batch_size*cand_size, 2*emb_size, sent_len)
attentive_conv_layer = Attentive_Conv_for_Pair_easy_version(
rng,
input_tensor3=
add_sents_context, #batch_size*cand_size, 2*emb_size, sent_len
input_tensor3_r=T.repeat(embed_input_claim, cand_size, axis=0),
mask_matrix=sents_mask.reshape(
(sents_mask.shape[0] * sents_mask.shape[1], sents_mask.shape[2])),
mask_matrix_r=T.repeat(claim_mask, cand_size, axis=0),
image_shape=(batch_size * cand_size, 1, emb_size, sent_len),
image_shape_r=(batch_size * cand_size, 1, emb_size, claim_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[0], 1, emb_size, 1),
W=att_conv_W,
b=att_conv_b,
W_context=conv_W_context,
b_context=conv_b_context)
attentive_sent_embeddings_l = attentive_conv_layer.attentive_maxpool_vec_l.reshape(
(batch_size, cand_size,
hidden_size[0])) #(batch_size*cand_size, hidden_size)
attentive_sent_embeddings_r = attentive_conv_layer.attentive_maxpool_vec_r.reshape(
(batch_size, cand_size, hidden_size[0]))
masked_sents_attconv = attentive_sent_embeddings_l * sents_labels.dimshuffle(
0, 1, 'x')
masked_claim_attconv = attentive_sent_embeddings_r * sents_labels.dimshuffle(
0, 1, 'x')
fine_max = T.concatenate([
T.max(masked_sents_attconv, axis=1),
T.max(masked_claim_attconv, axis=1)
],
axis=1) #(batch, 2*hidden)
# fine_sum = T.concatenate([T.sum(masked_sents_attconv, axis=1),T.sum(masked_claim_attconv, axis=1)],axis=1) #(batch, 2*hidden)
"Logistic Regression layer"
joint_LR_input = T.concatenate([premise_hypo_emb, fine_max], axis=1)
joint_LR_input_size = 2 * hidden_size[0] + 2 * hidden_size[0]
joint_U_a = create_ensemble_para(rng, 3,
joint_LR_input_size) # (input_size, 3)
joint_LR_b = theano.shared(value=np.zeros((3, ),
dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
joint_LR_para = [joint_U_a, joint_LR_b]
joint_layer_LR = LogisticRegression(
rng,
input=joint_LR_input,
n_in=joint_LR_input_size,
n_out=3,
W=joint_U_a,
b=joint_LR_b
) #basically it is a multiplication between weight matrix and input feature vector
# joint_loss=joint_layer_LR.negative_log_likelihood(joint_labels) #for classification task, we usually used negative log likelihood as loss, the lower the better.
params = [init_embeddings] + NN_para + LR_para + joint_LR_para
print('initialze model parameters...')
load_model_from_file(
'/home1/w/wenpeng/dataset/FEVER/model_para_0.9936287838053803', params)
# train_model = theano.function([sents_ids,sents_mask,sents_labels,claim_ids,claim_mask,joint_sents_ids,joint_sents_mask,joint_sents_labels, joint_claim_ids, joint_claim_mask, joint_labels], cost, updates=updates, allow_input_downcast=True, on_unused_input='ignore')
test_model = theano.function(
[sents_ids, sents_mask, claim_ids, claim_mask],
[inter_matrix, binarize_prob, joint_layer_LR.y_pred],
allow_input_downcast=True,
on_unused_input='ignore')
# dev_model = theano.function([sents_ids,sents_mask, claim_ids,claim_mask], [binarize_prob,joint_layer_LR.y_pred], allow_input_downcast=True, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print('... testing')
# early-stopping parameters
batch_score_vec, batch_binary_vec, pred_i = test_model(
test_sents, test_sent_masks, test_claims, test_claim_mask)
sorted_indices = np.argsort(batch_score_vec[0])[::-1] #descending order
selected_sents = []
for index in sorted_indices:
if batch_binary_vec[0][index] == 1:
selected_sents.append(sent_cand_list[index])
if len(selected_sents) == 5:
break
# for i, indicator in enumerate(list(batch_binary_vec[0])):
# if indicator == 1:
# selected_sents.append(sent_cand_list[i])
return pred_id2label.get(
pred_i[0]) + '"<p>"' + '"<br />"'.join(selected_sents) + '"<p/>"'
def evaluate_lenet5(learning_rate=0.01,
n_epochs=2000,
batch_size=100,
emb_size=300,
char_emb_size=20,
hidden_size=300,
L2_weight=0.0001,
p_len_limit=400,
test_p_len_limit=100,
q_len_limit=20,
char_len=15,
filter_size=[5, 5],
char_filter_size=3,
margin=2.0,
max_EM=50.302743615):
test_batch_size = batch_size * 10
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/SQuAD/'
rng = numpy.random.RandomState(23455)
word2id = {}
char2id = {}
#questions,paragraphs,q_masks,p_masks,labels, word2id
train_Q_list, train_para_list, train_Q_mask, train_para_mask, train_Q_char_list, train_para_char_list, train_Q_char_mask, train_para_char_mask, train_label_list, word2id, char2id = load_squad_cnn_rank_word_train(
word2id, char2id, p_len_limit, q_len_limit, char_len)
train_size = len(train_para_list)
test_Q_list, test_para_list, test_Q_mask, test_para_mask, test_Q_char_list, test_para_char_list, test_Q_char_mask, test_para_char_mask, test_label_list, q_idlist, word2id, char2id, test_para_wordlist_list = load_squad_cnn_rank_word_dev(
word2id, char2id, test_p_len_limit, q_len_limit, char_len)
test_size = len(test_para_list)
train_Q_list = numpy.asarray(train_Q_list, dtype='int32')
train_para_list = numpy.asarray(train_para_list, dtype='int32')
train_Q_mask = numpy.asarray(train_Q_mask, dtype=theano.config.floatX)
train_para_mask = numpy.asarray(train_para_mask,
dtype=theano.config.floatX)
train_Q_char_list = numpy.asarray(train_Q_char_list, dtype='int32')
train_para_char_list = numpy.asarray(train_para_char_list, dtype='int32')
train_Q_char_mask = numpy.asarray(train_Q_char_mask,
dtype=theano.config.floatX)
train_para_char_mask = numpy.asarray(train_para_char_mask,
dtype=theano.config.floatX)
train_label_list = numpy.asarray(train_label_list, dtype='int32')
test_Q_list = numpy.asarray(test_Q_list, dtype='int32')
test_para_list = numpy.asarray(test_para_list, dtype='int32')
test_Q_mask = numpy.asarray(test_Q_mask, dtype=theano.config.floatX)
test_para_mask = numpy.asarray(test_para_mask, dtype=theano.config.floatX)
test_Q_char_list = numpy.asarray(test_Q_char_list, dtype='int32')
test_para_char_list = numpy.asarray(test_para_char_list, dtype='int32')
test_Q_char_mask = numpy.asarray(test_Q_char_mask,
dtype=theano.config.floatX)
test_para_char_mask = numpy.asarray(test_para_char_mask,
dtype=theano.config.floatX)
vocab_size = len(word2id)
print 'vocab size: ', vocab_size
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX, rng)
rand_values[0] = numpy.array(numpy.zeros(emb_size),
dtype=theano.config.floatX)
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_glove()
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings = theano.shared(value=rand_values, borrow=True)
char_size = len(char2id)
print 'char size: ', char_size
char_rand_values = random_value_normal((char_size + 1, char_emb_size),
theano.config.floatX, rng)
char_embeddings = theano.shared(value=char_rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
paragraph = T.imatrix('paragraph')
questions = T.imatrix('questions')
gold_indices = T.imatrix() #batch, (start, end) for each sample
para_mask = T.fmatrix('para_mask')
q_mask = T.fmatrix('q_mask')
char_paragraph = T.imatrix() #(batch, char_len*p_len)
char_questions = T.imatrix()
char_para_mask = T.fmatrix()
char_q_mask = T.fmatrix()
true_p_len = T.iscalar()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
true_batch_size = paragraph.shape[0]
common_input_p = embeddings[paragraph.flatten()].reshape(
(true_batch_size, true_p_len,
emb_size)) #the input format can be adapted into CNN or GRU or LSTM
common_input_q = embeddings[questions.flatten()].reshape(
(true_batch_size, q_len_limit, emb_size))
char_common_input_p = char_embeddings[char_paragraph.flatten()].reshape(
(true_batch_size * true_p_len, char_len, char_emb_size
)) #the input format can be adapted into CNN or GRU or LSTM
char_common_input_q = char_embeddings[char_questions.flatten()].reshape(
(true_batch_size * q_len_limit, char_len, char_emb_size))
char_p_masks = char_para_mask.reshape(
(true_batch_size * true_p_len, char_len))
char_q_masks = char_q_mask.reshape(
(true_batch_size * q_len_limit, char_len))
conv_W_char, conv_b_char = create_conv_para(
rng, filter_shape=(char_emb_size, 1, char_emb_size, char_filter_size))
conv_W_1, conv_b_1 = create_conv_para(
rng,
filter_shape=(hidden_size, 1, emb_size + char_emb_size,
filter_size[0]))
conv_W_2, conv_b_2 = create_conv_para(rng,
filter_shape=(hidden_size, 1,
hidden_size,
filter_size[1]))
conv_W_1_q, conv_b_1_q = create_conv_para(
rng,
filter_shape=(hidden_size, 1, emb_size + char_emb_size,
filter_size[0]))
conv_W_2_q, conv_b_2_q = create_conv_para(rng,
filter_shape=(hidden_size, 1,
hidden_size,
filter_size[1]))
NN_para = [
conv_W_1, conv_b_1, conv_W_2, conv_b_2, conv_W_1_q, conv_b_1_q,
conv_W_2_q, conv_b_2_q, conv_W_char, conv_b_char
]
input4score = squad_cnn_rank_word(
rng, common_input_p, common_input_q, char_common_input_p,
char_common_input_q, batch_size, p_len_limit, q_len_limit, emb_size,
char_emb_size, char_len, filter_size, char_filter_size, hidden_size,
conv_W_1, conv_b_1, conv_W_2, conv_b_2, conv_W_1_q, conv_b_1_q,
conv_W_2_q, conv_b_2_q, conv_W_char, conv_b_char, para_mask, q_mask,
char_p_masks, char_q_masks) #(batch, 4*hidden, p_len_limit)
test_input4score = squad_cnn_rank_word(
rng, common_input_p, common_input_q, char_common_input_p,
char_common_input_q, test_batch_size, test_p_len_limit, q_len_limit,
emb_size, char_emb_size, char_len, filter_size, char_filter_size,
hidden_size, conv_W_1, conv_b_1, conv_W_2, conv_b_2, conv_W_1_q,
conv_b_1_q, conv_W_2_q, conv_b_2_q, conv_W_char, conv_b_char,
para_mask, q_mask, char_p_masks,
char_q_masks) #(batch, 4*hidden, p_len_limit)
# gram_size = 5*true_p_len-(0+1+2+3+4)
HL_1_para = create_ensemble_para(rng, hidden_size,
6 * hidden_size + char_emb_size)
HL_2_para = create_ensemble_para(rng, hidden_size, hidden_size)
HL_3_para = create_ensemble_para(rng, hidden_size, hidden_size)
HL_4_para = create_ensemble_para(rng, hidden_size, hidden_size)
U_a = create_ensemble_para(rng, 1, hidden_size)
norm_U_a = normalize_matrix(U_a)
norm_HL_1_para = normalize_matrix(HL_1_para)
norm_HL_2_para = normalize_matrix(HL_2_para)
norm_HL_3_para = normalize_matrix(HL_3_para)
norm_HL_4_para = normalize_matrix(HL_4_para)
end_HL_1_para = create_ensemble_para(rng, hidden_size,
6 * hidden_size + char_emb_size)
end_HL_2_para = create_ensemble_para(rng, hidden_size, hidden_size)
end_HL_3_para = create_ensemble_para(rng, hidden_size, hidden_size)
end_HL_4_para = create_ensemble_para(rng, hidden_size, hidden_size)
end_U_a = create_ensemble_para(rng, 1, hidden_size)
end_norm_U_a = normalize_matrix(end_U_a)
end_norm_HL_1_para = normalize_matrix(end_HL_1_para)
end_norm_HL_2_para = normalize_matrix(end_HL_2_para)
end_norm_HL_3_para = normalize_matrix(end_HL_3_para)
end_norm_HL_4_para = normalize_matrix(end_HL_4_para)
span_scores_matrix = add_HLs_2_tensor3(input4score, norm_HL_1_para,
norm_HL_2_para, norm_HL_3_para,
norm_HL_4_para, norm_U_a,
batch_size, true_p_len)
span_scores = T.nnet.softmax(span_scores_matrix) #(batch, para_len)
end_span_scores_matrix = add_HLs_2_tensor3(input4score, end_norm_HL_1_para,
end_norm_HL_2_para,
end_norm_HL_3_para,
end_norm_HL_4_para,
end_norm_U_a, batch_size,
true_p_len)
end_span_scores = T.nnet.softmax(
end_span_scores_matrix) #(batch, para_len)
loss_neg_likelihood = -T.mean(
T.log(span_scores[T.arange(batch_size), gold_indices[:, 0]]))
end_loss_neg_likelihood = -T.mean(
T.log(span_scores[T.arange(batch_size), gold_indices[:, 1]]))
#ranking loss start
tanh_span_scores_matrix = span_scores #T.tanh(span_scores_matrix) #(batch, gram_size)
index_matrix = T.zeros((batch_size, p_len_limit),
dtype=theano.config.floatX)
new_index_matrix = T.set_subtensor(
index_matrix[T.arange(batch_size), gold_indices[:, 0]], 1.0)
prob_batch_posi = tanh_span_scores_matrix[new_index_matrix.nonzero()]
prob_batch_nega = tanh_span_scores_matrix[(1.0 -
new_index_matrix).nonzero()]
repeat_posi = T.extra_ops.repeat(prob_batch_posi,
prob_batch_nega.shape[0],
axis=0)
repeat_nega = T.extra_ops.repeat(prob_batch_nega.dimshuffle('x', 0),
prob_batch_posi.shape[0],
axis=0).flatten()
loss_rank = T.mean(T.maximum(0.0, margin - repeat_posi + repeat_nega))
#ranking loss END
end_tanh_span_scores_matrix = end_span_scores #T.tanh(span_scores_matrix) #(batch, gram_size)
end_index_matrix = T.zeros((batch_size, p_len_limit),
dtype=theano.config.floatX)
end_new_index_matrix = T.set_subtensor(
end_index_matrix[T.arange(batch_size), gold_indices[:, 1]], 1.0)
end_prob_batch_posi = end_tanh_span_scores_matrix[
end_new_index_matrix.nonzero()]
end_prob_batch_nega = end_tanh_span_scores_matrix[(
1.0 - end_new_index_matrix).nonzero()]
end_repeat_posi = T.extra_ops.repeat(end_prob_batch_posi,
end_prob_batch_nega.shape[0],
axis=0)
end_repeat_nega = T.extra_ops.repeat(end_prob_batch_nega.dimshuffle(
'x', 0),
end_prob_batch_posi.shape[0],
axis=0).flatten()
end_loss_rank = T.mean(
T.maximum(0.0, margin - end_repeat_posi + end_repeat_nega))
loss = loss_neg_likelihood + end_loss_neg_likelihood + loss_rank + end_loss_rank
#test
test_span_scores_matrix = add_HLs_2_tensor3(
test_input4score, norm_HL_1_para, norm_HL_2_para, norm_HL_3_para,
norm_HL_4_para, norm_U_a, true_batch_size,
true_p_len) #(batch, test_p_len)
mask_test_return = T.argmax(test_span_scores_matrix * para_mask,
axis=1) #batch
end_test_span_scores_matrix = add_HLs_2_tensor3(
test_input4score, end_norm_HL_1_para, end_norm_HL_2_para,
end_norm_HL_3_para, end_norm_HL_4_para, end_norm_U_a, true_batch_size,
true_p_len) #(batch, test_p_len)
end_mask_test_return = T.argmax(end_test_span_scores_matrix * para_mask,
axis=1) #batch
params = (
[embeddings, char_embeddings] + NN_para +
[U_a, HL_1_para, HL_2_para, HL_3_para, HL_4_para] +
[end_U_a, end_HL_1_para, end_HL_2_para, end_HL_3_para, end_HL_4_para])
L2_reg = L2norm_paraList([
embeddings, char_embeddings, conv_W_1, conv_W_2, conv_W_1_q,
conv_W_2_q, conv_W_char, U_a, HL_1_para, HL_2_para, HL_3_para,
HL_4_para
])
#L2_reg = L2norm_paraList(params)
cost = loss + L2_weight * L2_reg
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i /
(T.sqrt(acc) + 1e-8))) #AdaGrad
updates.append((acc_i, acc))
# updates=Adam(cost, params, lr=0.0001)
train_model = theano.function(
[
paragraph,
questions,
gold_indices,
para_mask,
q_mask,
char_paragraph, #(batch, char_len*p_len)
char_questions,
char_para_mask,
char_q_mask,
true_p_len
],
cost,
updates=updates,
on_unused_input='ignore')
test_model = theano.function([
paragraph, questions, para_mask, q_mask, char_paragraph,
char_questions, char_para_mask, char_q_mask, true_p_len
], [mask_test_return, end_mask_test_return],
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches = train_size / batch_size
# remain_train=train_size%batch_size
train_batch_start = list(numpy.arange(n_train_batches) *
batch_size) + [train_size - batch_size]
n_test_batches = test_size / test_batch_size
# remain_test=test_size%batch_size
test_batch_start = list(numpy.arange(n_test_batches) *
test_batch_size) + [test_size - test_batch_size]
max_F1_acc = 0.0
max_exact_acc = 0.0
cost_i = 0.0
train_ids = range(train_size)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.Random(4).shuffle(train_ids)
iter_accu = 0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
train_id_batch = train_ids[para_id:para_id + batch_size]
cost_i += train_model(
train_para_list[train_id_batch], train_Q_list[train_id_batch],
train_label_list[train_id_batch],
train_para_mask[train_id_batch], train_Q_mask[train_id_batch],
train_para_char_list[train_id_batch],
train_Q_char_list[train_id_batch],
train_para_char_mask[train_id_batch],
train_Q_char_mask[train_id_batch], p_len_limit)
#print iter
if iter % 100 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
print 'Testing...'
past_time = time.time()
pred_dict = {}
q_amount = 0
p1 = 0
for test_para_id in test_batch_start:
batch_predict_ids, batch_predict_end_ids = test_model(
test_para_list[test_para_id:test_para_id +
test_batch_size],
test_Q_list[test_para_id:test_para_id +
test_batch_size],
test_para_mask[test_para_id:test_para_id +
test_batch_size],
test_Q_mask[test_para_id:test_para_id +
test_batch_size],
test_para_char_list[test_para_id:test_para_id +
test_batch_size],
test_Q_char_list[test_para_id:test_para_id +
test_batch_size],
test_para_char_mask[test_para_id:test_para_id +
test_batch_size],
test_Q_char_mask[test_para_id:test_para_id +
test_batch_size], test_p_len_limit)
test_para_wordlist_batch = test_para_wordlist_list[
test_para_id:test_para_id + test_batch_size]
# test_label_batch=test_label_list[test_para_id:test_para_id+test_batch_size]
# q_amount+=test_batch_size
q_ids_batch = q_idlist[test_para_id:test_para_id +
test_batch_size]
q_amount += test_batch_size
for q in range(test_batch_size): #for each question
# pred_ans=decode_predict_id(batch_predict_ids[q], test_para_wordlist_batch[q])
start = batch_predict_ids[q]
end = batch_predict_end_ids[q]
if end < start:
start, end = end, start
pred_ans = ' '.join(
test_para_wordlist_batch[q][start:end + 1])
q_id = q_ids_batch[q]
pred_dict[q_id] = pred_ans
with codecs.open(rootPath + 'predictions.txt', 'w',
'utf-8') as outfile:
json.dump(pred_dict, outfile)
F1_acc, exact_acc = standard_eval(rootPath + 'dev-v1.1.json',
rootPath + 'predictions.txt')
if F1_acc > max_F1_acc:
max_F1_acc = F1_acc
if exact_acc > max_exact_acc:
max_exact_acc = exact_acc
# if max_exact_acc > max_EM:
# store_model_to_file(rootPath+'Best_Paras_google_'+str(max_exact_acc), params)
# print 'Finished storing best params at:', max_exact_acc
print 'current average F1:', F1_acc, '\t\tmax F1:', max_F1_acc, 'current exact:', exact_acc, '\t\tmax exact_acc:', max_exact_acc
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.02, n_epochs=100, emb_size=300, batch_size=50, filter_size=[3], sent_len=40, claim_len=20, cand_size=10,hidden_size=[300,300], max_pred_pick=5):
model_options = locals().copy()
print "model options", model_options
seed=1234
np.random.seed(seed)
rng = np.random.RandomState(seed) #random seed, control the model generates the same results
srng = T.shared_randomstreams.RandomStreams(rng.randint(seed))
"load raw data"
train_sents, train_sent_masks, train_sent_labels, train_claims, train_claim_mask, _, word2id = load_fever_train(sent_len, claim_len, cand_size)
test_sents, test_sent_masks, test_sent_labels, test_claims, test_claim_mask, test_sent_names,test_ground_names,_, word2id = load_fever_dev(sent_len, claim_len, cand_size, word2id)
train_sents=np.asarray(train_sents, dtype='int32')
# dev_sents_l=np.asarray(all_sentences_l[1], dtype='int32')
test_sents=np.asarray(test_sents, dtype='int32')
train_sent_masks=np.asarray(train_sent_masks, dtype=theano.config.floatX)
# dev_masks_l=np.asarray(all_masks_l[1], dtype=theano.config.floatX)
test_sent_masks=np.asarray(test_sent_masks, dtype=theano.config.floatX)
train_sent_labels=np.asarray(train_sent_labels, dtype='int32')
# dev_sents_r=np.asarray(all_sentences_r[1] , dtype='int32')
# test_sent_labels=np.asarray(test_sent_labels, dtype='int32')
train_claims=np.asarray(train_claims, dtype='int32')
# dev_sents_r=np.asarray(all_sentences_r[1] , dtype='int32')
test_claims=np.asarray(test_claims, dtype='int32')
train_claim_mask=np.asarray(train_claim_mask, dtype=theano.config.floatX)
# dev_masks_r=np.asarray(all_masks_r[1], dtype=theano.config.floatX)
test_claim_mask=np.asarray(test_claim_mask, dtype=theano.config.floatX)
# train_labels_store=np.asarray(all_labels[0], dtype='int32')
# dev_labels_store=np.asarray(all_labels[1], dtype='int32')
# test_labels_store=np.asarray(all_labels[2], dtype='int32')
train_size=len(train_claims)
# dev_size=len(dev_labels_store)
test_size=len(test_claims)
print 'train size: ', train_size, ' test size: ', test_size
vocab_size=len(word2id)+1
rand_values=rng.normal(0.0, 0.01, (vocab_size, emb_size)) #generate a matrix by Gaussian distribution
id2word = {y:x for x,y in word2id.iteritems()}
word2vec=load_word2vec()
rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
init_embeddings=theano.shared(value=np.array(rand_values,dtype=theano.config.floatX), borrow=True) #wrap up the python variable "rand_values" into theano variable
"now, start to build the input form of the model"
sents_ids=T.itensor3() #(batch, cand_size, sent_len)
sents_mask=T.ftensor3()
sents_labels=T.imatrix() #(batch, cand_size)
claim_ids = T.imatrix() #(batch, claim_len)
claim_mask = T.imatrix()
# labels=T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
embed_input_sents=init_embeddings[sents_ids.flatten()].reshape((batch_size*cand_size, sent_len, emb_size)).dimshuffle(0,2,1)#embed_input(init_embeddings, sents_ids_l)#embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
embed_input_claim=init_embeddings[claim_ids.flatten()].reshape((batch_size,claim_len, emb_size)).dimshuffle(0,2,1)
conv_W, conv_b=create_conv_para(rng, filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]))
# conv_W2, conv_b2=create_conv_para(rng, filter_shape=(hidden_size[0], 1, emb_size, filter_size[1]))
NN_para = [conv_W, conv_b]
conv_model_sents = Conv_with_Mask(rng, input_tensor3=embed_input_sents,
mask_matrix = sents_mask.reshape((sents_mask.shape[0]*sents_mask.shape[1],sents_mask.shape[2])),
image_shape=(batch_size*cand_size, 1, emb_size, sent_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]), W=conv_W, b=conv_b) #mutiple mask with the conv_out to set the features by UNK to zero
sent_embeddings=conv_model_sents.maxpool_vec #(batch_size*cand_size, hidden_size) # each sentence then have an embedding of length hidden_size
batch_sent_emb = sent_embeddings.reshape((batch_size, cand_size, hidden_size[0]))
conv_model_claims = Conv_with_Mask(rng, input_tensor3=embed_input_claim,
mask_matrix = claim_mask,
image_shape=(batch_size, 1, emb_size, claim_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]), W=conv_W, b=conv_b) #mutiple mask with the conv_out to set the features by UNK to zero
claim_embeddings=conv_model_claims.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
batch_claim_emb = T.repeat(claim_embeddings.dimshuffle(0,'x', 1), cand_size, axis=1)
concate_claim_sent = T.concatenate([batch_claim_emb,batch_sent_emb ], axis=2)
concate_2_matrix = concate_claim_sent.reshape((batch_size*cand_size, hidden_size[0]*2))
LR_input = concate_2_matrix#T.concatenate([sent_embeddings,sent_embeddings2], axis=1)
LR_input_size = hidden_size[0]*2
#classification layer, it is just mapping from a feature vector of size "hidden_size" to a vector of only two values: positive, negative
U_a = create_ensemble_para(rng, 1, LR_input_size) # the weight matrix hidden_size*2
# LR_b = theano.shared(value=np.zeros((8,),dtype=theano.config.floatX),name='LR_b', borrow=True) #bias for each target class
LR_para=[U_a]
# layer_LR=LogisticRegression(rng, input=LR_input, n_in=LR_input_size, n_out=8, W=U_a, b=LR_b) #basically it is a multiplication between weight matrix and input feature vector
score_matrix = T.nnet.sigmoid(concate_2_matrix.dot(U_a)) #batch * 12
inter_matrix = score_matrix.reshape((batch_size, cand_size))
# inter_sent_claim = T.batched_dot(batch_sent_emb, batch_claim_emb) #(batch_size, cand_size, 1)
# inter_matrix = T.nnet.sigmoid(inter_sent_claim.reshape((batch_size, cand_size)))
'''
maybe 1.0-inter_matrix can be rewritten into 1/e^(inter_matrix)
'''
prob_pos = T.where( sents_labels < 1, 1.0-inter_matrix, inter_matrix)
loss = -T.mean(T.log(prob_pos))
#
# "Logistic Regression layer"
# LR_input = T.concatenate([attentive_sent_embeddings_l,attentive_sent_embeddings_r,attentive_sent_embeddings_l+attentive_sent_embeddings_r,attentive_sent_embeddings_l*attentive_sent_embeddings_r],axis=1)
# LR_input_size=4*hidden_size[0]
#
# U_a = create_ensemble_para(rng, 3, LR_input_size) # (input_size, 3)
# LR_b = theano.shared(value=np.zeros((3,),dtype=theano.config.floatX),name='LR_b', borrow=True) #bias for each target class
# LR_para=[U_a, LR_b]
#
# layer_LR=LogisticRegression(rng, input=normalize_matrix_col_wise(LR_input), n_in=LR_input_size, n_out=3, W=U_a, b=LR_b) #basically it is a multiplication between weight matrix and input feature vector
# loss=layer_LR.negative_log_likelihood(labels) #for classification task, we usually used negative log likelihood as loss, the lower the better.
'''
testing
'''
binarize_prob = T.where( inter_matrix > 0.5, 1, 0) #(batch_size, cand_size
params = [init_embeddings]+NN_para+LR_para
cost=loss
"Use AdaGrad to update parameters"
updates = Gradient_Cost_Para(cost,params, learning_rate)
train_model = theano.function([sents_ids,sents_mask,sents_labels,claim_ids,claim_mask], cost, updates=updates, allow_input_downcast=True, on_unused_input='ignore')
# dev_model = theano.function([sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels], layer_LR.errors(labels), allow_input_downcast=True, on_unused_input='ignore')
test_model = theano.function([sents_ids,sents_mask,claim_ids,claim_mask], inter_matrix, allow_input_downcast=True, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
n_train_batches=train_size/batch_size
train_batch_start=list(np.arange(n_train_batches)*batch_size)+[train_size-batch_size]
# n_dev_batches=dev_size/batch_size
# dev_batch_start=list(np.arange(n_dev_batches)*batch_size)+[dev_size-batch_size]
n_test_batches=test_size/batch_size
test_batch_start=list(np.arange(n_test_batches)*batch_size)+[test_size-batch_size]
max_acc_dev=0.0
max_test_f1=0.0
cost_i=0.0
train_indices = range(train_size)
while epoch < n_epochs:
epoch = epoch + 1
random.Random(100).shuffle(train_indices) #shuffle training set for each new epoch, is supposed to promote performance, but not garrenteed
iter_accu=0
for batch_id in train_batch_start: #for each batch
# iter means how many batches have been run, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
iter_accu+=1
train_id_batch = train_indices[batch_id:batch_id+batch_size]
'''
train_sents, train_sent_masks, train_sent_labels, train_claims, train_claim_mask
sents_ids,sents_mask,sents_labels,claim_ids,claim_mask
'''
cost_i+= train_model(
train_sents[train_id_batch],
train_sent_masks[train_id_batch],
train_sent_labels[train_id_batch],
train_claims[train_id_batch],
train_claim_mask[train_id_batch])
#after each 1000 batches, we test the performance of the model on all test data
# if (epoch==1 and iter%1000==0) or (epoch>=2 and iter%5==0):
if iter%10==0:
print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
past_time = time.time()
'''
test
test_sents, test_sent_masks, test_sent_labels, test_claims, test_claim_mask,
sents_ids,sents_mask,claim_ids,claim_mask
'''
f1_sum=0.0
for test_batch_id in test_batch_start: # for each test batch
batch_prob=test_model(
test_sents[test_batch_id:test_batch_id+batch_size],
test_sent_masks[test_batch_id:test_batch_id+batch_size],
test_claims[test_batch_id:test_batch_id+batch_size],
test_claim_mask[test_batch_id:test_batch_id+batch_size])
batch_sent_labels = test_sent_labels[test_batch_id:test_batch_id+batch_size]
batch_sent_names = test_sent_names[test_batch_id:test_batch_id+batch_size]
batch_ground_names = test_ground_names[test_batch_id:test_batch_id+batch_size]
for i in range(batch_size):
pred_sent_names = []
gold_sent_names = batch_ground_names[i]
zipped=[(batch_prob[i,k],batch_sent_labels[i][k],batch_sent_names[i][k]) for k in range(cand_size)]
sorted_zip = sorted(zipped, key=lambda x: x[0], reverse=True)
# print 'sorted_zip:', sorted_zip
# exit(0)
for j in range(cand_size):
triple = sorted_zip[j]
if triple[1] == 1.0:
'''
we should consider a rank, instead of binary
'''
if triple[0] >0.5:
pred_sent_names.append(batch_sent_names[i][j])
if len(pred_sent_names) == max_pred_pick:
break
f1_i = compute_f1_two_list_names(pred_sent_names, gold_sent_names)
f1_sum+=f1_i
test_f1=f1_sum/(len(test_batch_start)*batch_size)
if test_f1 > max_test_f1:
max_test_f1=test_f1
print '\t\tcurrent test_f1:', test_f1,' ; ','\t\t\t\t\tmax_test_f1:', max_test_f1
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
return max_acc_test
def evaluate_lenet5(learning_rate=0.05, n_epochs=2000, nkerns=[50,50], batch_size=1, window_width=3,
maxSentLength=64, maxDocLength=60, emb_size=50, hidden_size=200,
L2_weight=0.0065, update_freq=1, norm_threshold=5.0, max_s_length=57, max_d_length=59, margin=1.0, decay=0.95):
maxSentLength=max_s_length+2*(window_width-1)
maxDocLength=max_d_length+2*(window_width-1)
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/MCTest/';
rng = numpy.random.RandomState(23455)
train_data,train_size, test_data, test_size, vocab_size=load_MCTest_corpus_DQAAAA(rootPath+'vocab_DQAAAA.txt', rootPath+'mc500.train.tsv_standardlized.txt_DQAAAA.txt', rootPath+'mc500.test.tsv_standardlized.txt_DQAAAA.txt', max_s_length,maxSentLength, maxDocLength)#vocab_size contain train, dev and test
[train_data_D, train_data_Q, train_data_A1, train_data_A2, train_data_A3, train_data_A4, train_Label,
train_Length_D,train_Length_D_s, train_Length_Q, train_Length_A1, train_Length_A2, train_Length_A3, train_Length_A4,
train_leftPad_D,train_leftPad_D_s, train_leftPad_Q, train_leftPad_A1, train_leftPad_A2, train_leftPad_A3, train_leftPad_A4,
train_rightPad_D,train_rightPad_D_s, train_rightPad_Q, train_rightPad_A1, train_rightPad_A2, train_rightPad_A3, train_rightPad_A4]=train_data
[test_data_D, test_data_Q, test_data_A1, test_data_A2, test_data_A3, test_data_A4, test_Label,
test_Length_D,test_Length_D_s, test_Length_Q, test_Length_A1, test_Length_A2, test_Length_A3, test_Length_A4,
test_leftPad_D,test_leftPad_D_s, test_leftPad_Q, test_leftPad_A1, test_leftPad_A2, test_leftPad_A3, test_leftPad_A4,
test_rightPad_D,test_rightPad_D_s, test_rightPad_Q, test_rightPad_A1, test_rightPad_A2, test_rightPad_A3, test_rightPad_A4]=test_data
n_train_batches=train_size/batch_size
n_test_batches=test_size/batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)
test_batch_start=list(numpy.arange(n_test_batches)*batch_size)
# indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
# indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
# indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
# indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
# indices_train_l=T.cast(indices_train_l, 'int64')
# indices_train_r=T.cast(indices_train_r, 'int64')
# indices_test_l=T.cast(indices_test_l, 'int64')
# indices_test_r=T.cast(indices_test_r, 'int64')
rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_DQAAAA_glove_50d.txt')
#rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt')
embeddings=theano.shared(value=rand_values, borrow=True)
#cost_tmp=0
error_sum=0
# allocate symbolic variables for the data
index = T.lscalar()
index_D = T.lmatrix() # now, x is the index matrix, must be integer
index_Q = T.lvector()
index_A1= T.lvector()
index_A2= T.lvector()
index_A3= T.lvector()
index_A4= T.lvector()
# y = T.lvector()
len_D=T.lscalar()
len_D_s=T.lvector()
len_Q=T.lscalar()
len_A1=T.lscalar()
len_A2=T.lscalar()
len_A3=T.lscalar()
len_A4=T.lscalar()
left_D=T.lscalar()
left_D_s=T.lvector()
left_Q=T.lscalar()
left_A1=T.lscalar()
left_A2=T.lscalar()
left_A3=T.lscalar()
left_A4=T.lscalar()
right_D=T.lscalar()
right_D_s=T.lvector()
right_Q=T.lscalar()
right_A1=T.lscalar()
right_A2=T.lscalar()
right_A3=T.lscalar()
right_A4=T.lscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # sentence shape
dshape = (nkerns[0], maxDocLength) # doc shape
filter_words=(emb_size,window_width)
filter_sents=(nkerns[0], window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
# length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_D_input = debug_print(embeddings[index_D.flatten()].reshape((maxDocLength,maxSentLength, emb_size)).transpose(0, 2, 1), 'layer0_D_input')#.dimshuffle(0, 'x', 1, 2)
layer0_Q_input = debug_print(embeddings[index_Q.flatten()].reshape((maxSentLength, emb_size)).transpose(), 'layer0_Q_input')#.dimshuffle(0, 'x', 1, 2)
layer0_A1_input = debug_print(embeddings[index_A1.flatten()].reshape((maxSentLength, emb_size)).transpose(), 'layer0_A1_input')#.dimshuffle(0, 'x', 1, 2)
layer0_A2_input = embeddings[index_A2.flatten()].reshape((maxSentLength, emb_size)).transpose()#.dimshuffle(0, 'x', 1, 2)
layer0_A3_input = embeddings[index_A3.flatten()].reshape((maxSentLength, emb_size)).transpose()#.dimshuffle(0, 'x', 1, 2)
layer0_A4_input = embeddings[index_A4.flatten()].reshape((maxSentLength, emb_size)).transpose()#.dimshuffle(0, 'x', 1, 2)
U, W, b, Ub,Wb,bb=create_Bi_GRU_para(rng, emb_size, nkerns[0])
layer0_para=[U, W, b, Ub,Wb,bb]
# conv2_W, conv2_b=create_conv_para(rng, filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]))
# layer2_para=[conv2_W, conv2_b]
# high_W, high_b=create_highw_para(rng, nkerns[0], nkerns[1])
# highW_para=[high_W, high_b]
#load_model(params)
layer0_D = Bi_GRU_Tensor3_Input(T=layer0_D_input[left_D:-right_D,:,:],
lefts=left_D_s[left_D:-right_D],
rights=right_D_s[left_D:-right_D],
hidden_dim=nkerns[0],
U=U,W=W,b=b, Ub=Ub,Wb=Wb,bb=bb)
layer0_Q = Bi_GRU_Matrix_Input(X=layer0_Q_input[:,left_Q:-right_Q], word_dim=emb_size, hidden_dim=nkerns[0],U=U,W=W,b=b,U_b=Ub,W_b=Wb,b_b=bb, bptt_truncate=-1)
layer0_A1 = Bi_GRU_Matrix_Input(X=layer0_A1_input[:,left_A1:-right_A1], word_dim=emb_size, hidden_dim=nkerns[0],U=U,W=W,b=b,U_b=Ub,W_b=Wb,b_b=bb, bptt_truncate=-1)
layer0_A2 = Bi_GRU_Matrix_Input(X=layer0_A2_input[:,left_A2:-right_A2], word_dim=emb_size, hidden_dim=nkerns[0],U=U,W=W,b=b,U_b=Ub,W_b=Wb,b_b=bb, bptt_truncate=-1)
layer0_A3 = Bi_GRU_Matrix_Input(X=layer0_A3_input[:,left_A3:-right_A3], word_dim=emb_size, hidden_dim=nkerns[0],U=U,W=W,b=b,U_b=Ub,W_b=Wb,b_b=bb, bptt_truncate=-1)
layer0_A4 = Bi_GRU_Matrix_Input(X=layer0_A4_input[:,left_A4:-right_A4], word_dim=emb_size, hidden_dim=nkerns[0],U=U,W=W,b=b,U_b=Ub,W_b=Wb,b_b=bb, bptt_truncate=-1)
layer0_D_output=debug_print(layer0_D.output, 'layer0_D.output') # hidden*2
layer0_Q_output=debug_print(layer0_Q.output_vector_last, 'layer0_Q.output') # hidden*4
layer0_A1_output=debug_print(layer0_A1.output_vector_last, 'layer0_A1.output')
layer0_A2_output=debug_print(layer0_A2.output_vector_last, 'layer0_A2.output')
layer0_A3_output=debug_print(layer0_A3.output_vector_last, 'layer0_A3.output')
layer0_A4_output=debug_print(layer0_A4.output_vector_last, 'layer0_A4.output')
#before reasoning, do a GRU for doc: d
U_d, W_d, b_d, U_db, W_db, b_db=create_Bi_GRU_para(rng, nkerns[0]*2, nkerns[0]*2)
layer_d_para=[U_d, W_d, b_d, U_db, W_db, b_db]
layer_D_GRU = Bi_GRU_Matrix_Input(X=layer0_D_output, word_dim=nkerns[0]*2, hidden_dim=nkerns[0]*2,U=U_d,W=W_d,b=b_d,U_b=U_db,W_b=W_db,b_b=b_db, bptt_truncate=-1)
#Reasoning Layer 1
repeat_Q=debug_print(T.repeat(layer0_Q_output.reshape((layer0_Q_output.shape[0],1)), maxDocLength, axis=1)[:,:layer_D_GRU.output_matrix.shape[1]], 'repeat_Q')
input_DNN=debug_print(T.concatenate([layer_D_GRU.output_matrix,repeat_Q], axis=0).transpose(), 'input_DNN')#each row is an example
output_DNN1=HiddenLayer(rng, input=input_DNN, n_in=nkerns[0]*8, n_out=nkerns[0])
attention_W=create_ensemble_para(rng, nkerns[0], 1)
attention_weights=T.nnet.softmax(T.dot(attention_W,output_DNN1.output.transpose()))
repeat_attentions=T.repeat(attention_weights, layer_D_GRU.output_matrix.shape[0], axis=0)
doc_r=T.sum(layer_D_GRU.output_matrix*repeat_attentions, axis=1)
combine_DQ=T.concatenate([doc_r, layer0_Q_output], axis=0) # dim: hidden*6
output_DNN2=HiddenLayer(rng, input=combine_DQ, n_in=nkerns[0]*8, n_out=nkerns[0]*4)
# DNN_out=debug_print(output_DNN2.output.transpose(), 'DNN_out')
# U_p, W_p, b_p=create_GRU_para(rng, nkerns[0], nkerns[0])
# layer_pooling_para=[U_p, W_p, b_p]
# pooling=GRU_Matrix_Input(X=DNN_out, word_dim=nkerns[0], hidden_dim=nkerns[0],U=U_p,W=W_p,b=b_p,bptt_truncate=-1)
# translated_Q1=debug_print(pooling.output_vector_max, 'translated_Q1')
#
#
# #before reasoning, do a GRU for doc: d2
# U_d2, W_d2, b_d2=create_GRU_para(rng, nkerns[0], nkerns[0])
# layer_d2_para=[U_d2, W_d2, b_d2]
# layer_D2_GRU = GRU_Matrix_Input(X=layer_D_GRU.output_matrix, word_dim=nkerns[0], hidden_dim=nkerns[0],U=U_d2,W=W_d2,b=b_d2,bptt_truncate=-1)
# #Reasoning Layer 2
# repeat_Q1=debug_print(T.repeat(translated_Q1.reshape((translated_Q1.shape[0],1)), maxDocLength, axis=1)[:,:layer_D2_GRU.output_matrix.shape[1]], 'repeat_Q1')
# input_DNN2=debug_print(T.concatenate([layer_D2_GRU.output_matrix,repeat_Q1], axis=0).transpose(), 'input_DNN2')#each row is an example
# output_DNN3=HiddenLayer(rng, input=input_DNN2, n_in=nkerns[0]*2, n_out=nkerns[0])
# output_DNN4=HiddenLayer(rng, input=output_DNN3.output, n_in=nkerns[0], n_out=nkerns[0])
#
# DNN_out2=debug_print(output_DNN4.output.transpose(), 'DNN_out2')
# U_p2, W_p2, b_p2=create_GRU_para(rng, nkerns[0], nkerns[0])
# layer_pooling_para2=[U_p2, W_p2, b_p2]
# pooling2=GRU_Matrix_Input(X=DNN_out2, word_dim=nkerns[0], hidden_dim=nkerns[0],U=U_p2,W=W_p2,b=b_p2,bptt_truncate=-1)
translated_Q2=debug_print(output_DNN2.output, 'translated_Q2')
QA1=T.concatenate([translated_Q2, layer0_A1_output], axis=0)#dim: hidden*5
QA2=T.concatenate([translated_Q2, layer0_A2_output], axis=0)
QA3=T.concatenate([translated_Q2, layer0_A3_output], axis=0)
QA4=T.concatenate([translated_Q2, layer0_A4_output], axis=0)
W_HL,b_HL=create_HiddenLayer_para(rng, n_in=nkerns[0]*8, n_out=1)
match_params=[W_HL,b_HL]
QA1_match=HiddenLayer(rng, input=QA1, n_in=nkerns[0]*8, n_out=1, W=W_HL, b=b_HL)
QA2_match=HiddenLayer(rng, input=QA2, n_in=nkerns[0]*8, n_out=1, W=W_HL, b=b_HL)
QA3_match=HiddenLayer(rng, input=QA3, n_in=nkerns[0]*8, n_out=1, W=W_HL, b=b_HL)
QA4_match=HiddenLayer(rng, input=QA4, n_in=nkerns[0]*8, n_out=1, W=W_HL, b=b_HL)
# simi_overall_level1=debug_print(cosine(translated_Q2, layer0_A1_output), 'simi_overall_level1')
# simi_overall_level2=debug_print(cosine(translated_Q2, layer0_A2_output), 'simi_overall_level2')
# simi_overall_level3=debug_print(cosine(translated_Q2, layer0_A3_output), 'simi_overall_level3')
# simi_overall_level4=debug_print(cosine(translated_Q2, layer0_A4_output), 'simi_overall_level4')
simi_overall_level1=debug_print(QA1_match.output[0], 'simi_overall_level1')
simi_overall_level2=debug_print(QA2_match.output[0], 'simi_overall_level2')
simi_overall_level3=debug_print(QA3_match.output[0], 'simi_overall_level3')
simi_overall_level4=debug_print(QA4_match.output[0], 'simi_overall_level4')
# eucli_1=1.0/(1.0+EUCLID(layer3_DQ.output_D+layer3_DA.output_D, layer3_DQ.output_QA+layer3_DA.output_QA))
#only use overall_simi
cost=T.maximum(0.0, margin+simi_overall_level2-simi_overall_level1)+T.maximum(0.0, margin+simi_overall_level3-simi_overall_level1)+T.maximum(0.0, margin+simi_overall_level4-simi_overall_level1)
# cost=T.maximum(0.0, margin+T.max([simi_overall_level2, simi_overall_level3, simi_overall_level4])-simi_overall_level1) # ranking loss: max(0, margin-nega+posi)
posi_simi=simi_overall_level1
nega_simi=T.max([simi_overall_level2, simi_overall_level3, simi_overall_level4])
# #use ensembled simi
# cost=T.maximum(0.0, margin+T.max([simi_2, simi_3, simi_4])-simi_1) # ranking loss: max(0, margin-nega+posi)
# posi_simi=simi_1
# nega_simi=T.max([simi_2, simi_3, simi_4])
L2_reg =debug_print((U**2).sum()+(W**2).sum()
+(Ub**2).sum()+(Wb**2).sum()
+(output_DNN1.W**2).sum()
+(output_DNN2.W**2).sum()
+(U_d**2).sum()+(W_d**2).sum()
+(U_db**2).sum()+(W_db**2).sum()
+(W_HL**2).sum()
+(attention_W**2).sum()
, 'L2_reg')#+(embeddings**2).sum(), 'L2_reg')#+(layer1.W** 2).sum()++(embeddings**2).sum()
cost=debug_print(cost+L2_weight*L2_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function([index], [cost, posi_simi, nega_simi],
givens={
index_D: test_data_D[index], #a matrix
index_Q: test_data_Q[index],
index_A1: test_data_A1[index],
index_A2: test_data_A2[index],
index_A3: test_data_A3[index],
index_A4: test_data_A4[index],
len_D: test_Length_D[index],
len_D_s: test_Length_D_s[index],
len_Q: test_Length_Q[index],
len_A1: test_Length_A1[index],
len_A2: test_Length_A2[index],
len_A3: test_Length_A3[index],
len_A4: test_Length_A4[index],
left_D: test_leftPad_D[index],
left_D_s: test_leftPad_D_s[index],
left_Q: test_leftPad_Q[index],
left_A1: test_leftPad_A1[index],
left_A2: test_leftPad_A2[index],
left_A3: test_leftPad_A3[index],
left_A4: test_leftPad_A4[index],
right_D: test_rightPad_D[index],
right_D_s: test_rightPad_D_s[index],
right_Q: test_rightPad_Q[index],
right_A1: test_rightPad_A1[index],
right_A2: test_rightPad_A2[index],
right_A3: test_rightPad_A3[index],
right_A4: test_rightPad_A4[index]
}, on_unused_input='ignore')
params = layer0_para+output_DNN1.params+output_DNN2.params+match_params+layer_d_para+[attention_W]
# accumulator=[]
# for para_i in params:
# eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
# accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
# updates = []
# for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# grad_i=debug_print(grad_i,'grad_i')
# acc = decay*acc_i + (1-decay)*T.sqr(grad_i) #rmsprop
# updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc+1e-6)))
# updates.append((acc_i, acc))
def AdaDelta_updates(parameters,gradients,rho,eps):
# create variables to store intermediate updates
gradients_sq = [ theano.shared(numpy.zeros(p.get_value().shape)) for p in parameters ]
deltas_sq = [ theano.shared(numpy.zeros(p.get_value().shape)) for p in parameters ]
# calculates the new "average" delta for the next iteration
gradients_sq_new = [ rho*g_sq + (1-rho)*(g**2) for g_sq,g in zip(gradients_sq,gradients) ]
# calculates the step in direction. The square root is an approximation to getting the RMS for the average value
deltas = [ (T.sqrt(d_sq+eps)/T.sqrt(g_sq+eps))*grad for d_sq,g_sq,grad in zip(deltas_sq,gradients_sq_new,gradients) ]
# calculates the new "average" deltas for the next step.
deltas_sq_new = [ rho*d_sq + (1-rho)*(d**2) for d_sq,d in zip(deltas_sq,deltas) ]
# Prepare it as a list f
gradient_sq_updates = zip(gradients_sq,gradients_sq_new)
deltas_sq_updates = zip(deltas_sq,deltas_sq_new)
parameters_updates = [ (p,p - d) for p,d in zip(parameters,deltas) ]
return gradient_sq_updates + deltas_sq_updates + parameters_updates
updates=AdaDelta_updates(params, grads, decay, 1e-6)
train_model = theano.function([index], [cost, posi_simi, nega_simi], updates=updates,
givens={
index_D: train_data_D[index],
index_Q: train_data_Q[index],
index_A1: train_data_A1[index],
index_A2: train_data_A2[index],
index_A3: train_data_A3[index],
index_A4: train_data_A4[index],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
len_Q: train_Length_Q[index],
len_A1: train_Length_A1[index],
len_A2: train_Length_A2[index],
len_A3: train_Length_A3[index],
len_A4: train_Length_A4[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
left_Q: train_leftPad_Q[index],
left_A1: train_leftPad_A1[index],
left_A2: train_leftPad_A2[index],
left_A3: train_leftPad_A3[index],
left_A4: train_leftPad_A4[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
right_Q: train_rightPad_Q[index],
right_A1: train_rightPad_A1[index],
right_A2: train_rightPad_A2[index],
right_A3: train_rightPad_A3[index],
right_A4: train_rightPad_A4[index]
}, on_unused_input='ignore')
train_model_predict = theano.function([index], [cost, posi_simi, nega_simi],
givens={
index_D: train_data_D[index],
index_Q: train_data_Q[index],
index_A1: train_data_A1[index],
index_A2: train_data_A2[index],
index_A3: train_data_A3[index],
index_A4: train_data_A4[index],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
len_Q: train_Length_Q[index],
len_A1: train_Length_A1[index],
len_A2: train_Length_A2[index],
len_A3: train_Length_A3[index],
len_A4: train_Length_A4[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
left_Q: train_leftPad_Q[index],
left_A1: train_leftPad_A1[index],
left_A2: train_leftPad_A2[index],
left_A3: train_leftPad_A3[index],
left_A4: train_leftPad_A4[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
right_Q: train_rightPad_Q[index],
right_A1: train_rightPad_A1[index],
right_A2: train_rightPad_A2[index],
right_A3: train_rightPad_A3[index],
right_A4: train_rightPad_A4[index]
}, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
mid_time = start_time
epoch = 0
done_looping = False
max_acc=0.0
best_epoch=0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index=0
# shuffle(train_batch_start)#shuffle training data
corr_train=0
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index +1
sys.stdout.write( "Training :[%6f] %% complete!\r" % ((iter%train_size)*100.0/train_size) )
sys.stdout.flush()
minibatch_index=minibatch_index+1
cost_average, posi_simi, nega_simi= train_model(batch_start)
if posi_simi>nega_simi:
corr_train+=1
if iter % n_train_batches == 0:
print 'training @ iter = '+str(iter)+' average cost: '+str(cost_average)+'corr rate:'+str(corr_train*100.0/train_size)
if iter % validation_frequency == 0:
corr_test=0
for i in test_batch_start:
cost, posi_simi, nega_simi=test_model(i)
if posi_simi>nega_simi:
corr_test+=1
#write_file.close()
#test_score = numpy.mean(test_losses)
test_acc=corr_test*1.0/test_size
#test_acc=1-test_score
print(('\t\t\tepoch %i, minibatch %i/%i, test acc of best '
'model %f %%') %
(epoch, minibatch_index, n_train_batches,test_acc * 100.))
#now, see the results of LR
#write_feature=open(rootPath+'feature_check.txt', 'w')
find_better=False
if test_acc > max_acc:
max_acc=test_acc
best_epoch=epoch
find_better=True
print '\t\t\ttest_acc:', test_acc, 'max:', max_acc,'(at',best_epoch,')'
if find_better==True:
store_model_to_file(params, best_epoch, max_acc)
print 'Finished storing best params'
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.clock()-mid_time)/60.0, 'min'
mid_time = time.clock()
#writefile.close()
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.05,
n_epochs=2000,
nkerns=[50, 50],
batch_size=1,
window_width=3,
maxSentLength=64,
maxDocLength=60,
emb_size=50,
hidden_size=200,
L2_weight=0.0065,
update_freq=1,
norm_threshold=5.0,
max_s_length=57,
max_d_length=59,
margin=1.0,
decay=0.95):
maxSentLength = max_s_length + 2 * (window_width - 1)
maxDocLength = max_d_length + 2 * (window_width - 1)
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/MCTest/'
rng = numpy.random.RandomState(23455)
train_data, train_size, test_data, test_size, vocab_size = load_MCTest_corpus_DQAAAA(
rootPath + 'vocab_DQAAAA.txt',
rootPath + 'mc500.train.tsv_standardlized.txt_DQAAAA.txt',
rootPath + 'mc500.test.tsv_standardlized.txt_DQAAAA.txt', max_s_length,
maxSentLength, maxDocLength) #vocab_size contain train, dev and test
[
train_data_D, train_data_Q, train_data_A1, train_data_A2,
train_data_A3, train_data_A4, train_Label, train_Length_D,
train_Length_D_s, train_Length_Q, train_Length_A1, train_Length_A2,
train_Length_A3, train_Length_A4, train_leftPad_D, train_leftPad_D_s,
train_leftPad_Q, train_leftPad_A1, train_leftPad_A2, train_leftPad_A3,
train_leftPad_A4, train_rightPad_D, train_rightPad_D_s,
train_rightPad_Q, train_rightPad_A1, train_rightPad_A2,
train_rightPad_A3, train_rightPad_A4
] = train_data
[
test_data_D, test_data_Q, test_data_A1, test_data_A2, test_data_A3,
test_data_A4, test_Label, test_Length_D, test_Length_D_s,
test_Length_Q, test_Length_A1, test_Length_A2, test_Length_A3,
test_Length_A4, test_leftPad_D, test_leftPad_D_s, test_leftPad_Q,
test_leftPad_A1, test_leftPad_A2, test_leftPad_A3, test_leftPad_A4,
test_rightPad_D, test_rightPad_D_s, test_rightPad_Q, test_rightPad_A1,
test_rightPad_A2, test_rightPad_A3, test_rightPad_A4
] = test_data
n_train_batches = train_size / batch_size
n_test_batches = test_size / batch_size
train_batch_start = list(numpy.arange(n_train_batches) * batch_size)
test_batch_start = list(numpy.arange(n_test_batches) * batch_size)
# indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
# indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
# indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
# indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
# indices_train_l=T.cast(indices_train_l, 'int64')
# indices_train_r=T.cast(indices_train_r, 'int64')
# indices_test_l=T.cast(indices_test_l, 'int64')
# indices_test_r=T.cast(indices_test_r, 'int64')
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
rand_values[0] = numpy.array(numpy.zeros(emb_size),
dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values = load_word2vec_to_init(
rand_values, rootPath + 'vocab_DQAAAA_glove_50d.txt')
#rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt')
embeddings = theano.shared(value=rand_values, borrow=True)
#cost_tmp=0
error_sum = 0
# allocate symbolic variables for the data
index = T.lscalar()
index_D = T.lmatrix() # now, x is the index matrix, must be integer
index_Q = T.lvector()
index_A1 = T.lvector()
index_A2 = T.lvector()
index_A3 = T.lvector()
index_A4 = T.lvector()
# y = T.lvector()
len_D = T.lscalar()
len_D_s = T.lvector()
len_Q = T.lscalar()
len_A1 = T.lscalar()
len_A2 = T.lscalar()
len_A3 = T.lscalar()
len_A4 = T.lscalar()
left_D = T.lscalar()
left_D_s = T.lvector()
left_Q = T.lscalar()
left_A1 = T.lscalar()
left_A2 = T.lscalar()
left_A3 = T.lscalar()
left_A4 = T.lscalar()
right_D = T.lscalar()
right_D_s = T.lvector()
right_Q = T.lscalar()
right_A1 = T.lscalar()
right_A2 = T.lscalar()
right_A3 = T.lscalar()
right_A4 = T.lscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # sentence shape
dshape = (nkerns[0], maxDocLength) # doc shape
filter_words = (emb_size, window_width)
filter_sents = (nkerns[0], window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
# length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_D_input = debug_print(embeddings[index_D.flatten()].reshape(
(maxDocLength, maxSentLength, emb_size)).transpose(0, 2, 1),
'layer0_D_input') #.dimshuffle(0, 'x', 1, 2)
layer0_Q_input = debug_print(embeddings[index_Q.flatten()].reshape(
(maxSentLength, emb_size)).transpose(),
'layer0_Q_input') #.dimshuffle(0, 'x', 1, 2)
layer0_A1_input = debug_print(embeddings[index_A1.flatten()].reshape(
(maxSentLength,
emb_size)).transpose(), 'layer0_A1_input') #.dimshuffle(0, 'x', 1, 2)
layer0_A2_input = embeddings[index_A2.flatten()].reshape(
(maxSentLength, emb_size)).transpose() #.dimshuffle(0, 'x', 1, 2)
layer0_A3_input = embeddings[index_A3.flatten()].reshape(
(maxSentLength, emb_size)).transpose() #.dimshuffle(0, 'x', 1, 2)
layer0_A4_input = embeddings[index_A4.flatten()].reshape(
(maxSentLength, emb_size)).transpose() #.dimshuffle(0, 'x', 1, 2)
U, W, b, Ub, Wb, bb = create_Bi_GRU_para(rng, emb_size, nkerns[0])
layer0_para = [U, W, b, Ub, Wb, bb]
# conv2_W, conv2_b=create_conv_para(rng, filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]))
# layer2_para=[conv2_W, conv2_b]
# high_W, high_b=create_highw_para(rng, nkerns[0], nkerns[1])
# highW_para=[high_W, high_b]
#load_model(params)
layer0_D = Bi_GRU_Tensor3_Input(T=layer0_D_input[left_D:-right_D, :, :],
lefts=left_D_s[left_D:-right_D],
rights=right_D_s[left_D:-right_D],
hidden_dim=nkerns[0],
U=U,
W=W,
b=b,
Ub=Ub,
Wb=Wb,
bb=bb)
layer0_Q = Bi_GRU_Matrix_Input(X=layer0_Q_input[:, left_Q:-right_Q],
word_dim=emb_size,
hidden_dim=nkerns[0],
U=U,
W=W,
b=b,
U_b=Ub,
W_b=Wb,
b_b=bb,
bptt_truncate=-1)
layer0_A1 = Bi_GRU_Matrix_Input(X=layer0_A1_input[:, left_A1:-right_A1],
word_dim=emb_size,
hidden_dim=nkerns[0],
U=U,
W=W,
b=b,
U_b=Ub,
W_b=Wb,
b_b=bb,
bptt_truncate=-1)
layer0_A2 = Bi_GRU_Matrix_Input(X=layer0_A2_input[:, left_A2:-right_A2],
word_dim=emb_size,
hidden_dim=nkerns[0],
U=U,
W=W,
b=b,
U_b=Ub,
W_b=Wb,
b_b=bb,
bptt_truncate=-1)
layer0_A3 = Bi_GRU_Matrix_Input(X=layer0_A3_input[:, left_A3:-right_A3],
word_dim=emb_size,
hidden_dim=nkerns[0],
U=U,
W=W,
b=b,
U_b=Ub,
W_b=Wb,
b_b=bb,
bptt_truncate=-1)
layer0_A4 = Bi_GRU_Matrix_Input(X=layer0_A4_input[:, left_A4:-right_A4],
word_dim=emb_size,
hidden_dim=nkerns[0],
U=U,
W=W,
b=b,
U_b=Ub,
W_b=Wb,
b_b=bb,
bptt_truncate=-1)
layer0_D_output = debug_print(layer0_D.output,
'layer0_D.output') # hidden*2
layer0_Q_output = debug_print(layer0_Q.output_vector_last,
'layer0_Q.output') # hidden*4
layer0_A1_output = debug_print(layer0_A1.output_vector_last,
'layer0_A1.output')
layer0_A2_output = debug_print(layer0_A2.output_vector_last,
'layer0_A2.output')
layer0_A3_output = debug_print(layer0_A3.output_vector_last,
'layer0_A3.output')
layer0_A4_output = debug_print(layer0_A4.output_vector_last,
'layer0_A4.output')
#before reasoning, do a GRU for doc: d
U_d, W_d, b_d, U_db, W_db, b_db = create_Bi_GRU_para(
rng, nkerns[0] * 2, nkerns[0] * 2)
layer_d_para = [U_d, W_d, b_d, U_db, W_db, b_db]
layer_D_GRU = Bi_GRU_Matrix_Input(X=layer0_D_output,
word_dim=nkerns[0] * 2,
hidden_dim=nkerns[0] * 2,
U=U_d,
W=W_d,
b=b_d,
U_b=U_db,
W_b=W_db,
b_b=b_db,
bptt_truncate=-1)
#Reasoning Layer 1
repeat_Q = debug_print(
T.repeat(layer0_Q_output.reshape((layer0_Q_output.shape[0], 1)),
maxDocLength,
axis=1)[:, :layer_D_GRU.output_matrix.shape[1]], 'repeat_Q')
input_DNN = debug_print(
T.concatenate([layer_D_GRU.output_matrix, repeat_Q],
axis=0).transpose(),
'input_DNN') #each row is an example
output_DNN1 = HiddenLayer(rng,
input=input_DNN,
n_in=nkerns[0] * 8,
n_out=nkerns[0])
attention_W = create_ensemble_para(rng, nkerns[0], 1)
attention_weights = T.nnet.softmax(
T.dot(attention_W, output_DNN1.output.transpose()))
repeat_attentions = T.repeat(attention_weights,
layer_D_GRU.output_matrix.shape[0],
axis=0)
doc_r = T.sum(layer_D_GRU.output_matrix * repeat_attentions, axis=1)
combine_DQ = T.concatenate([doc_r, layer0_Q_output],
axis=0) # dim: hidden*6
output_DNN2 = HiddenLayer(rng,
input=combine_DQ,
n_in=nkerns[0] * 8,
n_out=nkerns[0] * 4)
# DNN_out=debug_print(output_DNN2.output.transpose(), 'DNN_out')
# U_p, W_p, b_p=create_GRU_para(rng, nkerns[0], nkerns[0])
# layer_pooling_para=[U_p, W_p, b_p]
# pooling=GRU_Matrix_Input(X=DNN_out, word_dim=nkerns[0], hidden_dim=nkerns[0],U=U_p,W=W_p,b=b_p,bptt_truncate=-1)
# translated_Q1=debug_print(pooling.output_vector_max, 'translated_Q1')
#
#
# #before reasoning, do a GRU for doc: d2
# U_d2, W_d2, b_d2=create_GRU_para(rng, nkerns[0], nkerns[0])
# layer_d2_para=[U_d2, W_d2, b_d2]
# layer_D2_GRU = GRU_Matrix_Input(X=layer_D_GRU.output_matrix, word_dim=nkerns[0], hidden_dim=nkerns[0],U=U_d2,W=W_d2,b=b_d2,bptt_truncate=-1)
# #Reasoning Layer 2
# repeat_Q1=debug_print(T.repeat(translated_Q1.reshape((translated_Q1.shape[0],1)), maxDocLength, axis=1)[:,:layer_D2_GRU.output_matrix.shape[1]], 'repeat_Q1')
# input_DNN2=debug_print(T.concatenate([layer_D2_GRU.output_matrix,repeat_Q1], axis=0).transpose(), 'input_DNN2')#each row is an example
# output_DNN3=HiddenLayer(rng, input=input_DNN2, n_in=nkerns[0]*2, n_out=nkerns[0])
# output_DNN4=HiddenLayer(rng, input=output_DNN3.output, n_in=nkerns[0], n_out=nkerns[0])
#
# DNN_out2=debug_print(output_DNN4.output.transpose(), 'DNN_out2')
# U_p2, W_p2, b_p2=create_GRU_para(rng, nkerns[0], nkerns[0])
# layer_pooling_para2=[U_p2, W_p2, b_p2]
# pooling2=GRU_Matrix_Input(X=DNN_out2, word_dim=nkerns[0], hidden_dim=nkerns[0],U=U_p2,W=W_p2,b=b_p2,bptt_truncate=-1)
translated_Q2 = debug_print(output_DNN2.output, 'translated_Q2')
QA1 = T.concatenate([translated_Q2, layer0_A1_output],
axis=0) #dim: hidden*5
QA2 = T.concatenate([translated_Q2, layer0_A2_output], axis=0)
QA3 = T.concatenate([translated_Q2, layer0_A3_output], axis=0)
QA4 = T.concatenate([translated_Q2, layer0_A4_output], axis=0)
W_HL, b_HL = create_HiddenLayer_para(rng, n_in=nkerns[0] * 8, n_out=1)
match_params = [W_HL, b_HL]
QA1_match = HiddenLayer(rng,
input=QA1,
n_in=nkerns[0] * 8,
n_out=1,
W=W_HL,
b=b_HL)
QA2_match = HiddenLayer(rng,
input=QA2,
n_in=nkerns[0] * 8,
n_out=1,
W=W_HL,
b=b_HL)
QA3_match = HiddenLayer(rng,
input=QA3,
n_in=nkerns[0] * 8,
n_out=1,
W=W_HL,
b=b_HL)
QA4_match = HiddenLayer(rng,
input=QA4,
n_in=nkerns[0] * 8,
n_out=1,
W=W_HL,
b=b_HL)
# simi_overall_level1=debug_print(cosine(translated_Q2, layer0_A1_output), 'simi_overall_level1')
# simi_overall_level2=debug_print(cosine(translated_Q2, layer0_A2_output), 'simi_overall_level2')
# simi_overall_level3=debug_print(cosine(translated_Q2, layer0_A3_output), 'simi_overall_level3')
# simi_overall_level4=debug_print(cosine(translated_Q2, layer0_A4_output), 'simi_overall_level4')
simi_overall_level1 = debug_print(QA1_match.output[0],
'simi_overall_level1')
simi_overall_level2 = debug_print(QA2_match.output[0],
'simi_overall_level2')
simi_overall_level3 = debug_print(QA3_match.output[0],
'simi_overall_level3')
simi_overall_level4 = debug_print(QA4_match.output[0],
'simi_overall_level4')
# eucli_1=1.0/(1.0+EUCLID(layer3_DQ.output_D+layer3_DA.output_D, layer3_DQ.output_QA+layer3_DA.output_QA))
#only use overall_simi
cost = T.maximum(
0.0, margin + simi_overall_level2 - simi_overall_level1) + T.maximum(
0.0,
margin + simi_overall_level3 - simi_overall_level1) + T.maximum(
0.0, margin + simi_overall_level4 - simi_overall_level1)
# cost=T.maximum(0.0, margin+T.max([simi_overall_level2, simi_overall_level3, simi_overall_level4])-simi_overall_level1) # ranking loss: max(0, margin-nega+posi)
posi_simi = simi_overall_level1
nega_simi = T.max(
[simi_overall_level2, simi_overall_level3, simi_overall_level4])
# #use ensembled simi
# cost=T.maximum(0.0, margin+T.max([simi_2, simi_3, simi_4])-simi_1) # ranking loss: max(0, margin-nega+posi)
# posi_simi=simi_1
# nega_simi=T.max([simi_2, simi_3, simi_4])
L2_reg = debug_print(
(U**2).sum() + (W**2).sum() + (Ub**2).sum() + (Wb**2).sum() +
(output_DNN1.W**2).sum() + (output_DNN2.W**2).sum() + (U_d**2).sum() +
(W_d**2).sum() + (U_db**2).sum() + (W_db**2).sum() + (W_HL**2).sum() +
(attention_W**2).sum(), 'L2_reg'
) #+(embeddings**2).sum(), 'L2_reg')#+(layer1.W** 2).sum()++(embeddings**2).sum()
cost = debug_print(cost + L2_weight * L2_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function(
[index],
[cost, posi_simi, nega_simi],
givens={
index_D: test_data_D[index], #a matrix
index_Q: test_data_Q[index],
index_A1: test_data_A1[index],
index_A2: test_data_A2[index],
index_A3: test_data_A3[index],
index_A4: test_data_A4[index],
len_D: test_Length_D[index],
len_D_s: test_Length_D_s[index],
len_Q: test_Length_Q[index],
len_A1: test_Length_A1[index],
len_A2: test_Length_A2[index],
len_A3: test_Length_A3[index],
len_A4: test_Length_A4[index],
left_D: test_leftPad_D[index],
left_D_s: test_leftPad_D_s[index],
left_Q: test_leftPad_Q[index],
left_A1: test_leftPad_A1[index],
left_A2: test_leftPad_A2[index],
left_A3: test_leftPad_A3[index],
left_A4: test_leftPad_A4[index],
right_D: test_rightPad_D[index],
right_D_s: test_rightPad_D_s[index],
right_Q: test_rightPad_Q[index],
right_A1: test_rightPad_A1[index],
right_A2: test_rightPad_A2[index],
right_A3: test_rightPad_A3[index],
right_A4: test_rightPad_A4[index]
},
on_unused_input='ignore')
params = layer0_para + output_DNN1.params + output_DNN2.params + match_params + layer_d_para + [
attention_W
]
# accumulator=[]
# for para_i in params:
# eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
# accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
# updates = []
# for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# grad_i=debug_print(grad_i,'grad_i')
# acc = decay*acc_i + (1-decay)*T.sqr(grad_i) #rmsprop
# updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc+1e-6)))
# updates.append((acc_i, acc))
def AdaDelta_updates(parameters, gradients, rho, eps):
# create variables to store intermediate updates
gradients_sq = [
theano.shared(numpy.zeros(p.get_value().shape)) for p in parameters
]
deltas_sq = [
theano.shared(numpy.zeros(p.get_value().shape)) for p in parameters
]
# calculates the new "average" delta for the next iteration
gradients_sq_new = [
rho * g_sq + (1 - rho) * (g**2)
for g_sq, g in zip(gradients_sq, gradients)
]
# calculates the step in direction. The square root is an approximation to getting the RMS for the average value
deltas = [
(T.sqrt(d_sq + eps) / T.sqrt(g_sq + eps)) * grad
for d_sq, g_sq, grad in zip(deltas_sq, gradients_sq_new, gradients)
]
# calculates the new "average" deltas for the next step.
deltas_sq_new = [
rho * d_sq + (1 - rho) * (d**2)
for d_sq, d in zip(deltas_sq, deltas)
]
# Prepare it as a list f
gradient_sq_updates = zip(gradients_sq, gradients_sq_new)
deltas_sq_updates = zip(deltas_sq, deltas_sq_new)
parameters_updates = [(p, p - d) for p, d in zip(parameters, deltas)]
return gradient_sq_updates + deltas_sq_updates + parameters_updates
updates = AdaDelta_updates(params, grads, decay, 1e-6)
train_model = theano.function(
[index], [cost, posi_simi, nega_simi],
updates=updates,
givens={
index_D: train_data_D[index],
index_Q: train_data_Q[index],
index_A1: train_data_A1[index],
index_A2: train_data_A2[index],
index_A3: train_data_A3[index],
index_A4: train_data_A4[index],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
len_Q: train_Length_Q[index],
len_A1: train_Length_A1[index],
len_A2: train_Length_A2[index],
len_A3: train_Length_A3[index],
len_A4: train_Length_A4[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
left_Q: train_leftPad_Q[index],
left_A1: train_leftPad_A1[index],
left_A2: train_leftPad_A2[index],
left_A3: train_leftPad_A3[index],
left_A4: train_leftPad_A4[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
right_Q: train_rightPad_Q[index],
right_A1: train_rightPad_A1[index],
right_A2: train_rightPad_A2[index],
right_A3: train_rightPad_A3[index],
right_A4: train_rightPad_A4[index]
},
on_unused_input='ignore')
train_model_predict = theano.function(
[index], [cost, posi_simi, nega_simi],
givens={
index_D: train_data_D[index],
index_Q: train_data_Q[index],
index_A1: train_data_A1[index],
index_A2: train_data_A2[index],
index_A3: train_data_A3[index],
index_A4: train_data_A4[index],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
len_Q: train_Length_Q[index],
len_A1: train_Length_A1[index],
len_A2: train_Length_A2[index],
len_A3: train_Length_A3[index],
len_A4: train_Length_A4[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
left_Q: train_leftPad_Q[index],
left_A1: train_leftPad_A1[index],
left_A2: train_leftPad_A2[index],
left_A3: train_leftPad_A3[index],
left_A4: train_leftPad_A4[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
right_Q: train_rightPad_Q[index],
right_A1: train_rightPad_A1[index],
right_A2: train_rightPad_A2[index],
right_A3: train_rightPad_A3[index],
right_A4: train_rightPad_A4[index]
},
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
mid_time = start_time
epoch = 0
done_looping = False
max_acc = 0.0
best_epoch = 0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index = 0
# shuffle(train_batch_start)#shuffle training data
corr_train = 0
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index + 1
sys.stdout.write("Training :[%6f] %% complete!\r" %
((iter % train_size) * 100.0 / train_size))
sys.stdout.flush()
minibatch_index = minibatch_index + 1
cost_average, posi_simi, nega_simi = train_model(batch_start)
if posi_simi > nega_simi:
corr_train += 1
if iter % n_train_batches == 0:
print 'training @ iter = ' + str(
iter) + ' average cost: ' + str(
cost_average) + 'corr rate:' + str(
corr_train * 100.0 / train_size)
if iter % validation_frequency == 0:
corr_test = 0
for i in test_batch_start:
cost, posi_simi, nega_simi = test_model(i)
if posi_simi > nega_simi:
corr_test += 1
#write_file.close()
#test_score = numpy.mean(test_losses)
test_acc = corr_test * 1.0 / test_size
#test_acc=1-test_score
print(
('\t\t\tepoch %i, minibatch %i/%i, test acc of best '
'model %f %%') %
(epoch, minibatch_index, n_train_batches, test_acc * 100.))
#now, see the results of LR
#write_feature=open(rootPath+'feature_check.txt', 'w')
find_better = False
if test_acc > max_acc:
max_acc = test_acc
best_epoch = epoch
find_better = True
print '\t\t\ttest_acc:', test_acc, 'max:', max_acc, '(at', best_epoch, ')'
if find_better == True:
store_model_to_file(params, best_epoch, max_acc)
print 'Finished storing best params'
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.clock() - mid_time) / 60.0, 'min'
mid_time = time.clock()
#writefile.close()
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.5,
n_epochs=2000,
batch_size=500,
emb_size=300,
hidden_size=300,
L2_weight=0.0001,
para_len_limit=700,
q_len_limit=40):
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/SQuAD/'
rng = numpy.random.RandomState(23455)
train_para_list, train_Q_list, train_label_list, train_para_mask, train_mask, word2id, train_feature_matrixlist = load_train(
para_len_limit, q_len_limit)
train_size = len(train_para_list)
if train_size != len(train_Q_list) or train_size != len(
train_label_list) or train_size != len(train_para_mask):
print 'train_size!=len(Q_list) or train_size!=len(label_list) or train_size!=len(para_mask)'
exit(0)
test_para_list, test_Q_list, test_para_mask, test_mask, overall_vocab_size, overall_word2id, test_text_list, q_ansSet_list, test_feature_matrixlist = load_dev_or_test(
word2id, para_len_limit, q_len_limit)
test_size = len(test_para_list)
if test_size != len(test_Q_list) or test_size != len(
test_mask) or test_size != len(test_para_mask):
print 'test_size!=len(test_Q_list) or test_size!=len(test_mask) or test_size!=len(test_para_mask)'
exit(0)
id2word = {y: x for x, y in overall_word2id.iteritems()}
word2vec = load_word2vec()
rand_values = random_value_normal((overall_vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
# rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings = theano.shared(value=rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
paragraph = T.imatrix('paragraph')
questions = T.imatrix('questions')
labels = T.imatrix('labels')
para_mask = T.fmatrix('para_mask')
q_mask = T.fmatrix('q_mask')
extraF = T.ftensor3('extraF') # should be in shape (batch, wordsize, 3)
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
paragraph_input = embeddings[paragraph.flatten()].reshape(
(paragraph.shape[0], paragraph.shape[1], emb_size)).transpose(
(0, 2, 1)) # (batch_size, emb_size, maxparalen)
#
# # BdGRU(rng, str(0), shape, X, mask, is_train = 1, batch_size = 1, p = 0.5)
#
U1, W1, b1 = create_GRU_para(rng, emb_size, hidden_size)
U1_b, W1_b, b1_b = create_GRU_para(rng, emb_size, hidden_size)
paragraph_para = [U1, W1, b1, U1_b, W1_b, b1_b]
paragraph_model = Bd_GRU_Batch_Tensor_Input_with_Mask(
X=paragraph_input,
Mask=para_mask,
hidden_dim=hidden_size,
U=U1,
W=W1,
b=b1,
Ub=U1_b,
Wb=W1_b,
bb=b1_b)
para_reps = paragraph_model.output_tensor #(batch, emb, para_len)
Qs_emb = embeddings[questions.flatten()].reshape(
(questions.shape[0], questions.shape[1], emb_size)).transpose(
(0, 2, 1)) #(#questions, emb_size, maxsenlength)
UQ, WQ, bQ = create_GRU_para(rng, emb_size, hidden_size)
UQ_b, WQ_b, bQ_b = create_GRU_para(rng, emb_size, hidden_size)
Q_para = [UQ, WQ, bQ, UQ_b, WQ_b, bQ_b]
questions_model = Bd_GRU_Batch_Tensor_Input_with_Mask(
X=Qs_emb,
Mask=q_mask,
hidden_dim=hidden_size,
U=UQ,
W=WQ,
b=bQ,
Ub=UQ_b,
Wb=WQ_b,
bb=bQ_b)
questions_reps = questions_model.output_sent_rep_maxpooling.reshape(
(batch_size, 1, hidden_size)) #(batch, 2*out_size)
#questions_reps=T.repeat(questions_reps, para_reps.shape[2], axis=1)
#attention distributions
W_a1 = create_ensemble_para(
rng, hidden_size,
hidden_size) # init_weights((2*hidden_size, hidden_size))
W_a2 = create_ensemble_para(rng, hidden_size, hidden_size)
U_a = create_ensemble_para(rng, 2, hidden_size + 3) # 3 extra features
norm_W_a1 = normalize_matrix(W_a1)
norm_W_a2 = normalize_matrix(W_a2)
norm_U_a = normalize_matrix(U_a)
LR_b = theano.shared(
value=numpy.zeros((2, ),
dtype=theano.config.floatX), # @UndefinedVariable
name='LR_b',
borrow=True)
attention_paras = [W_a1, W_a2, U_a, LR_b]
transformed_para_reps = T.tanh(
T.dot(para_reps.transpose((0, 2, 1)), norm_W_a2))
transformed_q_reps = T.tanh(T.dot(questions_reps, norm_W_a1))
#transformed_q_reps=T.repeat(transformed_q_reps, transformed_para_reps.shape[1], axis=1)
add_both = 0.5 * (transformed_para_reps + transformed_q_reps)
prior_att = T.concatenate([add_both, normalize_matrix(extraF)], axis=2)
#prior_att=T.concatenate([transformed_para_reps, transformed_q_reps], axis=2)
valid_indices = para_mask.flatten().nonzero()[0]
layer3 = LogisticRegression(rng,
input=prior_att.reshape(
(batch_size * prior_att.shape[1],
hidden_size + 3)),
n_in=hidden_size + 3,
n_out=2,
W=norm_U_a,
b=LR_b)
#error =layer3.negative_log_likelihood(labels.flatten()[valid_indices])
error = -T.mean(
T.log(layer3.p_y_given_x)
[valid_indices,
labels.flatten()[valid_indices]]) #[T.arange(y.shape[0]), y])
distributions = layer3.p_y_given_x[:, -1].reshape(
(batch_size, para_mask.shape[1]))
#distributions=layer3.y_pred.reshape((batch_size, para_mask.shape[1]))
masked_dis = distributions * para_mask
'''
strength = T.tanh(T.dot(prior_att, norm_U_a)) #(batch, #word, 1)
distributions=debug_print(strength.reshape((batch_size, paragraph.shape[1])), 'distributions')
para_mask=para_mask
masked_dis=distributions*para_mask
# masked_label=debug_print(labels*para_mask, 'masked_label')
# error=((masked_dis-masked_label)**2).mean()
label_mask=T.gt(labels,0.0)
neg_label_mask=T.lt(labels,0.0)
dis_masked=distributions*label_mask
remain_dis_masked=distributions*neg_label_mask
ans_size=T.sum(label_mask)
non_ans_size=T.sum(neg_label_mask)
pos_error=T.sum((dis_masked-label_mask)**2)/ans_size
neg_error=T.sum((remain_dis_masked-(-neg_label_mask))**2)/non_ans_size
error=pos_error+0.5*neg_error #(ans_size*1.0/non_ans_size)*
'''
# def AttentionLayer(q_rep, ext_M):
# theano_U_a=debug_print(norm_U_a, 'norm_U_a')
# prior_att=debug_print(T.nnet.sigmoid(T.dot(q_rep, norm_W_a1).reshape((1, hidden_size)) + T.dot(paragraph_model.output_matrix.transpose(), norm_W_a2)), 'prior_att')
# f __name__ == '__main__':
# prior_att=T.concatenate([prior_att, ext_M], axis=1)
#
# strength = debug_print(T.tanh(T.dot(prior_att, theano_U_a)), 'strength') #(#word, 1)
# return strength.transpose() #(1, #words)
# distributions, updates = theano.scan(
# AttentionLayer,
# sequences=[questions_reps,extraF] )
# distributions=debug_print(distributions.reshape((questions.shape[0],paragraph.shape[0])), 'distributions')
# labels=debug_print(labels, 'labels')
# label_mask=T.gt(labels,0.0)
# neg_label_mask=T.lt(labels,0.0)
# dis_masked=distributions*label_mask
# remain_dis_masked=distributions*neg_label_mask
# pos_error=((dis_masked-1)**2).mean()
# neg_error=((remain_dis_masked-(-1))**2).mean()
# error=pos_error+(T.sum(label_mask)*1.0/T.sum(neg_label_mask))*neg_error
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = [embeddings] + paragraph_para + Q_para + attention_paras
L2_reg = L2norm_paraList(
[embeddings, U1, W1, U1_b, W1_b, UQ, WQ, UQ_b, WQ_b, W_a1, W_a2, U_a])
#L2_reg = L2norm_paraList(params)
cost = error #+L2_weight*L2_reg
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i /
(T.sqrt(acc) + 1e-8))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function(
[paragraph, questions, labels, para_mask, q_mask, extraF],
error,
updates=updates,
on_unused_input='ignore')
test_model = theano.function(
[paragraph, questions, para_mask, q_mask, extraF],
masked_dis,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches = train_size / batch_size
# remain_train=train_size%batch_size
train_batch_start = list(numpy.arange(n_train_batches) *
batch_size) + [train_size - batch_size]
n_test_batches = test_size / batch_size
# remain_test=test_size%batch_size
test_batch_start = list(
numpy.arange(n_test_batches) * batch_size) + [test_size - batch_size]
max_exact_acc = 0.0
cost_i = 0.0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#shuffle(train_batch_start)
iter_accu = 0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
# haha=para_mask[para_id:para_id+batch_size]
# print haha
# for i in range(batch_size):
# print len(haha[i])
cost_i += train_model(
np.asarray(train_para_list[para_id:para_id + batch_size],
dtype='int32'),
np.asarray(train_Q_list[para_id:para_id + batch_size],
dtype='int32'),
np.asarray(train_label_list[para_id:para_id + batch_size],
dtype='int32'),
np.asarray(train_para_mask[para_id:para_id + batch_size],
dtype=theano.config.floatX),
np.asarray(train_mask[para_id:para_id + batch_size],
dtype=theano.config.floatX),
np.asarray(train_feature_matrixlist[para_id:para_id +
batch_size],
dtype=theano.config.floatX))
#print iter
if iter % 10 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
print 'Testing...'
past_time = time.time()
exact_match = 0.0
q_amount = 0
for test_para_id in test_batch_start:
distribution_matrix = test_model(
np.asarray(test_para_list[test_para_id:test_para_id +
batch_size],
dtype='int32'),
np.asarray(test_Q_list[test_para_id:test_para_id +
batch_size],
dtype='int32'),
np.asarray(test_para_mask[test_para_id:test_para_id +
batch_size],
dtype=theano.config.floatX),
np.asarray(test_mask[test_para_id:test_para_id +
batch_size],
dtype=theano.config.floatX),
np.asarray(
test_feature_matrixlist[test_para_id:test_para_id +
batch_size],
dtype=theano.config.floatX))
# print distribution_matrix
test_para_wordlist_list = test_text_list[
test_para_id:test_para_id + batch_size]
para_gold_ansset_list = q_ansSet_list[
test_para_id:test_para_id + batch_size]
paralist_extra_features = test_feature_matrixlist[
test_para_id:test_para_id + batch_size]
sub_para_mask = test_para_mask[test_para_id:test_para_id +
batch_size]
para_len = len(test_para_wordlist_list[0])
if para_len != len(distribution_matrix[0]):
print 'para_len!=len(distribution_matrix[0]):', para_len, len(
distribution_matrix[0])
exit(0)
# q_size=len(distribution_matrix)
q_amount += batch_size
# print q_size
# print test_para_word_list
for q in range(batch_size): #for each question
# if len(distribution_matrix[q])!=len(test_label_matrix[q]):
# print 'len(distribution_matrix[q])!=len(test_label_matrix[q]):', len(distribution_matrix[q]), len(test_label_matrix[q])
# else:
# ss=len(distribution_matrix[q])
# combine_list=[]
# for ii in range(ss):
# combine_list.append(str(distribution_matrix[q][ii])+'('+str(test_label_matrix[q][ii])+')')
# print combine_list
# exit(0)
# print 'distribution_matrix[q]:',distribution_matrix[q]
pred_ans = extract_ansList_attentionList(
test_para_wordlist_list[q], distribution_matrix[q],
np.asarray(paralist_extra_features[q],
dtype=theano.config.floatX),
sub_para_mask[q])
q_gold_ans_set = para_gold_ansset_list[q]
F1 = MacroF1(pred_ans, q_gold_ans_set)
exact_match += F1
# match_amount=len(pred_ans_set & q_gold_ans_set)
# # print 'q_gold_ans_set:', q_gold_ans_set
# # print 'pred_ans_set:', pred_ans_set
# if match_amount>0:
# exact_match+=match_amount*1.0/len(pred_ans_set)
exact_acc = exact_match / q_amount
if exact_acc > max_exact_acc:
max_exact_acc = exact_acc
print 'current average F1:', exact_acc, '\t\tmax F1:', max_exact_acc
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.005, n_epochs=2000, batch_size=300, test_batch_size=400, emb_size=50, hidden_size=300, HL_hidden_size=200,
L2_weight=0.0001, train_size=None, test_size=None, batch_size_pred=400, trichar_len=15,char_emb_size=50,
para_len=101, question_len=20, c_len=1, model_type='train'):
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/Users/cisintern/hs/l/workhs/yin/20170320/';
storePath='/mounts/data/proj/wenpeng/Dataset/SQuAD/'
rng = np.random.RandomState(23455)
word2id={}
trichar2id={}
word2id['UNK']=0 # use it to pad
#word2id, trichar2id, questions,questions_mask,paras,paras_mask,labels, isInQ_para, paras_shape, questions_shape, types, types_shape,question_trichar_ids,question_trichar_masks,para_trichar_ids,para_trichar_masks,type_trichar_ids,type_trichar_masks
word2id, trichar2id,train_questions,train_questions_mask,train_paras,train_paras_mask,train_labels, train_islabels, train_paras_shape, train_questions_shape, train_types, train_types_shape,train_question_trichar_ids,train_question_trichar_masks,train_para_trichar_ids,train_para_trichar_masks,train_type_trichar_ids,train_type_trichar_masks=load_SQUAD_hinrich_v4(train_size, para_len, question_len, trichar_len, word2id,trichar2id, rootPath+'trn20170320.txt')
word2id, trichar2id,test_questions,test_questions_mask,test_paras,test_paras_mask,test_labels, test_islabels, test_paras_shape, test_questions_shape, test_types, test_types_shape,test_question_trichar_ids,test_question_trichar_masks,test_para_trichar_ids,test_para_trichar_masks,test_type_trichar_ids,test_type_trichar_masks=load_SQUAD_hinrich_v4(test_size, para_len, question_len, trichar_len,word2id, trichar2id, rootPath+'dev.big.20170320.txt')
word2id, trichar2id,test_questions,test_questions_mask,test_paras,test_paras_mask,test_labels, test_islabels, test_paras_shape, test_questions_shape, test_types, test_types_shape,test_question_trichar_ids,test_question_trichar_masks,test_para_trichar_ids,test_para_trichar_masks,test_type_trichar_ids,test_type_trichar_masks=load_SQUAD_hinrich_v4(test_size, para_len, question_len, trichar_len,word2id, trichar2id, rootPath+'dev20170320.txt')
print 'word2id size for bigger dataset:', len(word2id), 'trichar size:', len(trichar2id)
train_size=len(train_questions)
test_size = len(test_questions) #50010#
train_questions = np.asarray(train_questions, dtype='int32')
train_questions_shape = np.asarray(train_questions_shape, dtype='int32')
train_questions_mask = np.asarray(train_questions_mask, dtype=theano.config.floatX)
train_paras = np.asarray(train_paras, dtype='int32')
train_paras_shape = np.asarray(train_paras_shape, dtype='int32')
train_paras_mask = np.asarray(train_paras_mask, dtype=theano.config.floatX)
train_types = np.asarray(train_types, dtype='int32')
train_types_shape = np.asarray(train_types_shape, dtype='int32')
# train_c_ids = np.asarray(train_c_ids, dtype='int32')
# train_c_ids_shape = np.asarray(train_c_ids_shape, dtype='int32')
# train_c_masks = np.asarray(train_c_masks, dtype=theano.config.floatX)
train_islabels = np.asarray(train_islabels, dtype=theano.config.floatX)
# train_c_heads = np.asarray(train_c_heads, dtype='int32')
# train_c_tails = np.asarray(train_c_tails, dtype='int32')
train_labels = np.asarray(train_labels, dtype='int32')
#train_question_trichar_ids,train_question_trichar_masks,train_para_trichar_ids,train_para_trichar_masks,train_type_trichar_ids,train_type_trichar_masks
train_question_trichar_ids = np.asarray(train_question_trichar_ids, dtype='int32')
train_question_trichar_masks = np.asarray(train_question_trichar_masks, dtype=theano.config.floatX)
train_para_trichar_ids = np.asarray(train_para_trichar_ids, dtype='int32')
train_para_trichar_masks = np.asarray(train_para_trichar_masks, dtype=theano.config.floatX)
train_type_trichar_ids = np.asarray(train_type_trichar_ids, dtype='int32')
train_type_trichar_masks = np.asarray(train_type_trichar_masks, dtype=theano.config.floatX)
test_questions = np.asarray(test_questions, dtype='int32')
test_questions_shape = np.asarray(test_questions_shape, dtype='int32')
test_questions_mask = np.asarray(test_questions_mask, dtype=theano.config.floatX)
test_paras = np.asarray(test_paras, dtype='int32')
test_paras_shape = np.asarray(test_paras_shape, dtype='int32')
test_paras_mask = np.asarray(test_paras_mask, dtype=theano.config.floatX)
test_types = np.asarray(test_types, dtype='int32')
test_types_shape = np.asarray(test_types_shape, dtype='int32')
# test_c_ids = np.asarray(test_c_ids, dtype='int32')
# test_c_ids_shape = np.asarray(test_c_ids_shape, dtype='int32')
# test_c_masks = np.asarray(test_c_masks, dtype=theano.config.floatX)
test_islabels = np.asarray(test_islabels, dtype=theano.config.floatX)
# test_c_heads = np.asarray(test_c_heads, dtype='int32')
# test_c_tails = np.asarray(test_c_tails, dtype='int32')
test_labels = np.asarray(test_labels, dtype='int32')
test_question_trichar_ids = np.asarray(test_question_trichar_ids, dtype='int32')
test_question_trichar_masks = np.asarray(test_question_trichar_masks, dtype=theano.config.floatX)
test_para_trichar_ids = np.asarray(test_para_trichar_ids, dtype='int32')
test_para_trichar_masks = np.asarray(test_para_trichar_masks, dtype=theano.config.floatX)
test_type_trichar_ids = np.asarray(test_type_trichar_ids, dtype='int32')
test_type_trichar_masks = np.asarray(test_type_trichar_masks, dtype=theano.config.floatX)
overall_vocab_size=len(word2id)
print 'train size:', train_size, 'test size:', test_size, 'vocab size:', overall_vocab_size
rand_values=random_value_normal((overall_vocab_size, emb_size), theano.config.floatX, rng)
rand_values[0]=np.array(np.zeros(emb_size),dtype=theano.config.floatX)
id2word = {y:x for x,y in word2id.iteritems()}
word2vec=load_word2vec()
rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings=theano.shared(value=rand_values, borrow=True)
overall_trichar_size = len(trichar2id)
char_rand_values=random_value_normal((overall_trichar_size, char_emb_size), theano.config.floatX, rng)
char_embeddings=theano.shared(value=char_rand_values, borrow=True)
para=T.imatrix() #(2*batch, len)
para_shape = T.imatrix()
para_mask=T.fmatrix() #(2*batch, len)
q=T.imatrix() #(2*batch, len_q)
q_shape = T.imatrix()
q_mask=T.fmatrix() #(2*batch, len_q)
islabels = T.fmatrix()
labels=T.ivector() #batch
types=T.imatrix()
types_shape=T.imatrix()
q_trichar_ids = T.imatrix()
q_trichar_masks =T.fmatrix()
para_trichar_ids = T.imatrix()
para_trichar_masks =T.fmatrix()
type_trichar_ids = T.imatrix()
type_trichar_masks =T.fmatrix()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
true_batch_size = para.shape[0]
paragraph_input = embeddings[para.flatten()].reshape((true_batch_size, para_len, emb_size)).transpose((0, 2,1)) #(batch, emb_size, para_len)
q_input = embeddings[q.flatten()].reshape((true_batch_size, question_len, emb_size)).transpose((0, 2,1)) # (batch, emb_size, question_len)
q_types = embeddings[types.flatten()].reshape((true_batch_size, 2, emb_size)).transpose((0, 2,1))
paragraph_input_shape = embeddings[para_shape.flatten()].reshape((true_batch_size, para_len, emb_size)).transpose((0, 2,1)) #(batch, emb_size, para_len)
q_input_shape = embeddings[q_shape.flatten()].reshape((true_batch_size, question_len, emb_size)).transpose((0, 2,1)) # (batch, emb_size, question_len)
q_types_shape = embeddings[types_shape.flatten()].reshape((true_batch_size, 2, emb_size)).transpose((0, 2,1))
paragraph_input_trichar = char_embeddings[para_trichar_ids.flatten()].reshape((true_batch_size, para_len*trichar_len, char_emb_size)) #(batch, char_emb_size, para_len*trichar_len)
q_input_trichar = char_embeddings[q_trichar_ids.flatten()].reshape((true_batch_size, question_len*trichar_len, char_emb_size)) # (batch, emb_size, question_len)
q_types_trichar = char_embeddings[type_trichar_ids.flatten()].reshape((true_batch_size, 2*trichar_len, char_emb_size))
#sum up trichar emb as word level embs
paragraph_input_trichar=T.sum((paragraph_input_trichar*para_trichar_masks.dimshuffle(0,1,'x')).reshape((true_batch_size, para_len, trichar_len,char_emb_size)),axis=2).dimshuffle(0,2,1) #(true_batch_size, char_emb_size,para_len)
q_input_trichar=T.sum((q_input_trichar*q_trichar_masks.dimshuffle(0,1,'x')).reshape((true_batch_size, question_len, trichar_len,char_emb_size)),axis=2).dimshuffle(0,2,1) #(true_batch_size, char_emb_size,q_len)
q_types_trichar=T.sum((q_types_trichar*type_trichar_masks.dimshuffle(0,1,'x')).reshape((true_batch_size, 2, trichar_len,char_emb_size)),axis=2).dimshuffle(0,2,1) #(true_batch_size, char_emb_size,2)
#concatenate word emb with shape emb
q_input = T.concatenate([q_input,q_input_shape, q_input_trichar],axis=1) #(batch, 2*emb_size+char_emb_size, q_len)
paragraph_input = T.concatenate([paragraph_input,paragraph_input_shape, paragraph_input_trichar,islabels.dimshuffle(0,'x',1)],axis=1)#(batch, 2*emb_size+char_emb_size+1, para_len)
q_types_input = T.sum(T.concatenate([q_types,q_types_shape,q_types_trichar],axis=1), axis=2) #(batch, 2*emb+char_emb_size)
fwd_LSTM_para_dict=create_LSTM_para(rng, 2*emb_size+char_emb_size+1, hidden_size)
bwd_LSTM_para_dict=create_LSTM_para(rng, 2*emb_size+char_emb_size+1, hidden_size)
paragraph_para=fwd_LSTM_para_dict.values()+ bwd_LSTM_para_dict.values()# .values returns a list of parameters
paragraph_model=Bd_LSTM_Batch_Tensor_Input_with_Mask_Concate(paragraph_input, para_mask, hidden_size, fwd_LSTM_para_dict, bwd_LSTM_para_dict)
paragraph_reps_tensor3=paragraph_model.output_tensor #(batch, 2*hidden, paralen)
fwd_LSTM_q_dict=create_LSTM_para(rng, 2*emb_size+char_emb_size, hidden_size)
bwd_LSTM_q_dict=create_LSTM_para(rng, 2*emb_size+char_emb_size, hidden_size)
question_para=fwd_LSTM_q_dict.values()+ bwd_LSTM_q_dict.values()# .values returns a list of parameters
questions_model=Bd_LSTM_Batch_Tensor_Input_with_Mask_Concate(q_input, q_mask, hidden_size, fwd_LSTM_q_dict, bwd_LSTM_q_dict)
q_reps=questions_model.output_sent_rep_maxpooling #(batch, 2*hidden)
#interaction
batch_ids=T.arange(true_batch_size)
# c_heads=theano.shared(value=np.asarray([(para_len-1)/2]*batch_size, dtype='int32'), borrow=True)
c_heads = T.repeat(theano.shared(value=np.asarray([(para_len-1)/2], dtype='int32'), borrow=True), true_batch_size)
c_tails=c_heads+1
c_heads_reps=paragraph_reps_tensor3[batch_ids,:,c_heads] #(batch, 2*hidden)
c_tails_reps=paragraph_reps_tensor3[batch_ids,:,c_tails] #(batch, 2*hidden)
candididates_reps=T.concatenate([c_heads_reps, c_tails_reps], axis=1) #(batch, 4*hidden)
context_l=paragraph_model.forward_output[batch_ids,:,c_heads-1] #(batch, hidden)
context_r=paragraph_model.backward_output[batch_ids,:,c_tails+1]#(batch, hidden)
#glove level average
# c_input = embeddings[c_ids.flatten()].reshape((true_batch_size, c_len, emb_size)).transpose((0, 2,1)) #(batch, emb_size, c_len)
# c_input_shape = embeddings[c_ids_shape.flatten()].reshape((true_batch_size, c_len, emb_size)).transpose((0, 2,1)) #(batch, emb_size, c_len)
# c_input = T.concatenate([c_input,c_input_shape],axis=1)
c_sum = paragraph_input[:,:-1,(para_len-1)/2]#(batch, 2*emb_size+char_emb)
c_sum_with_isInQLabel = paragraph_input[:,:,(para_len-1)/2]
# e_input = embeddings[e_ids.flatten()].reshape((true_batch_size, e_len, emb_size)).transpose((0, 2,1)) #(batch, emb_size, c_len)
q_sum = T.sum(q_input*q_mask.dimshuffle(0,'x',1), axis=2) #(batch, 2*emb_size+char_emb_size)
# average_Q_batch = q_sum/T.sqrt(T.sum(q_sum**2, axis=1)+1e-20).dimshuffle(0,'x')
HL_layer_1_input_size=2*hidden_size+4*hidden_size+(2*emb_size+char_emb_size+1)+(2*emb_size+char_emb_size)+1+hidden_size+hidden_size+(2*emb_size+char_emb_size)+1
cosine_Qtype_cand = cosine_row_wise_twoMatrix(q_types_input, c_sum).dimshuffle(0,'x') #(batch, 1)
#, average_E_batch, average_C_batch, average_Q_batch
HL_layer_1_input = T.concatenate([q_reps, candididates_reps, c_sum_with_isInQLabel, q_sum, islabels[:,(para_len-1)/2:(para_len-1)/2+1], context_l, context_r,
q_types_input,
cosine_Qtype_cand], axis=1)
HL_layer_1=HiddenLayer(rng, input=HL_layer_1_input, n_in=HL_layer_1_input_size, n_out=HL_hidden_size, activation=T.tanh)
HL_layer_2=HiddenLayer(rng, input=HL_layer_1.output, n_in=HL_hidden_size, n_out=HL_hidden_size, activation=T.tanh)
LR_input= T.concatenate([HL_layer_1.output, HL_layer_2.output, islabels[:,(para_len-1)/2:(para_len-1)/2+1], cosine_Qtype_cand], axis=1) #(batch, char_HL_hidden_size+HL_hidden_size)
LR_input_size= HL_hidden_size+HL_hidden_size+1+1#HL_layer_1_input_size+2*HL_hidden_size
U_a = create_ensemble_para(rng, 2, LR_input_size) # the weight matrix hidden_size*2
norm_U_a=normalize_matrix(U_a)
LR_b = theano.shared(value=np.zeros((2,),dtype=theano.config.floatX),name='char_LR_b', borrow=True) #bias for each target class
LR_para=[U_a, LR_b]
layer_LR=LogisticRegression(rng, input=LR_input, n_in=LR_input_size, n_out=2, W=norm_U_a, b=LR_b) #basically it is a multiplication between weight matrix and input feature vector
loss=layer_LR.negative_log_likelihood(labels) #for classification task, we usually used negative log likelihood as loss, the lower the better.
params = LR_para+[embeddings,char_embeddings]+paragraph_para+question_para+HL_layer_1.params+HL_layer_2.params
# load_model_from_file(storePath+'Best_Paras_HS_20170316_0.760357142857', params)
# L2_reg =L2norm_paraList([embeddings,U1, W1, U1_b, W1_b,UQ, WQ , UQ_b, WQ_b, W_a1, W_a2, U_a])
# L2_reg = L2norm_paraList(params)
cost=loss#+1e-6*L2_reg
accumulator=[]
for para_i in params:
eps_p=np.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / (T.sqrt(acc)+1e-20))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([para, para_shape, para_mask,q,q_shape, q_mask,islabels, labels, types, types_shape, q_trichar_ids,q_trichar_masks,para_trichar_ids,para_trichar_masks,type_trichar_ids,type_trichar_masks], cost, updates=updates,on_unused_input='ignore')
# train_model_pred = theano.function([para, para_mask, c_ids,c_mask,e_ids,e_mask, c_heads, c_tails, l_heads, l_tails, e_heads, e_tails, q, q_mask,labels], layer_LR.y_pred, on_unused_input='ignore')
test_model = theano.function([para, para_shape, para_mask, q,q_shape, q_mask,islabels, labels, types, types_shape,q_trichar_ids,q_trichar_masks,para_trichar_ids,para_trichar_masks,type_trichar_ids,type_trichar_masks], [layer_LR.errors(labels),layer_LR.prop_for_posi], on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = np.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches=train_size/batch_size #batch_size means how many pairs
train_batch_start=list(np.arange(n_train_batches)*batch_size)+[train_size-batch_size]
# n_train_batches_pred=train_size/batch_size_pred #batch_size means how many pairs
# train_batch_start_pred=list(np.arange(n_train_batches_pred)*batch_size_pred)+[train_size-batch_size_pred]
n_test_batches=test_size/test_batch_size #batch_size means how many pairs
n_test_remain=test_size%test_batch_size #batch_size means how many pairs
test_batch_start=list(np.arange(n_test_batches)*test_batch_size)+[test_size-test_batch_size]
max_acc=0.0
cost_i=0.0
train_ids = range(train_size)
# train_ids_pred = range(train_size)
best_test_statistic=defaultdict(int)
# best_train_statistic=defaultdict(int)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.shuffle(train_ids)
# print train_ids[:100]
iter_accu=0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
iter_accu+=1
train_id_list = train_ids[para_id:para_id+batch_size]
# print 'train_labels[train_id_list]:', train_labels[train_id_list]
if model_type=='train':
#para, para_shape, para_mask,q,q_shape, q_mask,islabels, labels, types, types_shape, q_trichar_ids,q_trichar_masks,para_trichar_ids,para_trichar_masks,type_trichar_ids,type_trichar_masks
cost_i+= train_model(
train_paras[train_id_list],
train_paras_shape[train_id_list],
train_paras_mask[train_id_list],
train_questions[train_id_list],
train_questions_shape[train_id_list],
train_questions_mask[train_id_list],
train_islabels[train_id_list],
train_labels[train_id_list],
train_types[train_id_list],
train_types_shape[train_id_list],
train_question_trichar_ids[train_id_list],
train_question_trichar_masks[train_id_list],
train_para_trichar_ids[train_id_list],
train_para_trichar_masks[train_id_list],
train_type_trichar_ids[train_id_list],
train_type_trichar_masks[train_id_list])
#print iter
if iter%10 ==0:
print 'Epoch ', epoch, 'iter '+str(iter)+'/'+str(len(train_batch_start))+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
past_time = time.time()
print 'Testing...'
error=0
test_statistic=defaultdict(int)
if model_type=='test':
writefile=open(storePath+'predictions_20170317.txt', 'w')
for id, test_para_id in enumerate(test_batch_start):
test_id_list = range(test_para_id, test_para_id+test_batch_size)
# print 'test_id_list:',test_id_list
# print 'test_c_heads[test_id_list]', test_c_heads[test_id_list]
# gold_labels_list = test_labels_3c[test_para_id:test_para_id+test_batch_size]
error_i, preds_i= test_model(
test_paras[test_id_list],
test_paras_shape[test_id_list],
test_paras_mask[test_id_list],
test_questions[test_id_list],
test_questions_shape[test_id_list],
test_questions_mask[test_id_list],
test_islabels[test_id_list],
test_labels[test_id_list],
test_types[test_id_list],
test_types_shape[test_id_list],
test_question_trichar_ids[test_id_list],
test_question_trichar_masks[test_id_list],
test_para_trichar_ids[test_id_list],
test_para_trichar_masks[test_id_list],
test_type_trichar_ids[test_id_list],
test_type_trichar_masks[test_id_list])
if model_type=='test':
if id < len(test_batch_start)-1:
writefile.write('\n'.join(map(str,list(preds_i)))+'\n')
else:
writefile.write('\n'.join(map(str,list(preds_i)[-n_test_remain:]))+'\n')
error+=error_i
# for ind, gold_label in enumerate(gold_labels_list):
# test_statistic[(gold_label, preds_i[ind])]+=1
if model_type=='test':
writefile.close()
acc=1.0-error*1.0/len(test_batch_start)
# acc= (test_statistic.get((1,1),0)+test_statistic.get((0,0),0))*1.0/(test_statistic.get((1,1),0)+test_statistic.get((0,0),0)+test_statistic.get((1,0),0)+test_statistic.get((0,1),0))
if acc> max_acc:
max_acc=acc
# best_test_statistic=test_statistic
if model_type=='train':
store_model_to_file(storePath+'Best_Paras_HS_20170324_'+str(max_acc), params)
print 'Finished storing best params at:', max_acc
print 'current average acc:', acc, '\t\tmax acc:', max_acc#, '\ttest_statistic:', test_statistic
# print '\t\t\t\tbest statistic:', best_test_statistic
if model_type=='test':
exit(0)
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.01,
n_epochs=2000,
batch_size=100,
emb_size=10,
hidden_size=10,
L2_weight=0.0001,
para_len_limit=400,
q_len_limit=40,
max_EM=0.217545454546):
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/SQuAD/'
rng = numpy.random.RandomState(23455)
train_para_list, train_Q_list, train_label_list, train_para_mask, train_mask, word2id, train_feature_matrixlist = load_train(
para_len_limit, q_len_limit)
train_size = len(train_para_list)
if train_size != len(train_Q_list) or train_size != len(
train_label_list) or train_size != len(train_para_mask):
print 'train_size!=len(Q_list) or train_size!=len(label_list) or train_size!=len(para_mask)'
exit(0)
test_para_list, test_Q_list, test_Q_list_word, test_para_mask, test_mask, overall_vocab_size, overall_word2id, test_text_list, q_ansSet_list, test_feature_matrixlist = load_dev_or_test(
word2id, para_len_limit, q_len_limit)
test_size = len(test_para_list)
if test_size != len(test_Q_list) or test_size != len(
test_mask) or test_size != len(test_para_mask):
print 'test_size!=len(test_Q_list) or test_size!=len(test_mask) or test_size!=len(test_para_mask)'
exit(0)
rand_values = random_value_normal((overall_vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
# rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
# id2word = {y:x for x,y in overall_word2id.iteritems()}
# word2vec=load_word2vec()
# rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings = theano.shared(value=rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
paragraph = T.imatrix('paragraph')
questions = T.imatrix('questions')
labels = T.imatrix('labels')
para_mask = T.fmatrix('para_mask')
q_mask = T.fmatrix('q_mask')
extraF = T.ftensor3('extraF') # should be in shape (batch, wordsize, 3)
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
norm_extraF = normalize_matrix(extraF)
U1, W1, b1 = create_GRU_para(rng, emb_size, hidden_size)
U1_b, W1_b, b1_b = create_GRU_para(rng, emb_size, hidden_size)
paragraph_para = [U1, W1, b1, U1_b, W1_b, b1_b]
UQ, WQ, bQ = create_GRU_para(rng, emb_size, hidden_size)
UQ_b, WQ_b, bQ_b = create_GRU_para(rng, emb_size, hidden_size)
Q_para = [UQ, WQ, bQ, UQ_b, WQ_b, bQ_b]
W_a1 = create_ensemble_para(
rng, hidden_size,
hidden_size) # init_weights((2*hidden_size, hidden_size))
W_a2 = create_ensemble_para(rng, hidden_size, hidden_size)
U_a = create_ensemble_para(rng, 2, hidden_size + 3) # 3 extra features
LR_b = theano.shared(
value=numpy.zeros((2, ),
dtype=theano.config.floatX), # @UndefinedVariable
name='LR_b',
borrow=True)
attention_paras = [W_a1, W_a2, U_a, LR_b]
params = [embeddings] + paragraph_para + Q_para + attention_paras
load_model_from_file(rootPath + 'Best_Paras_conv_0.217545454545', params)
paragraph_input = embeddings[paragraph.flatten()].reshape(
(paragraph.shape[0], paragraph.shape[1], emb_size)).transpose(
(0, 2, 1)) # (batch_size, emb_size, maxparalen)
concate_paragraph_input = T.concatenate(
[paragraph_input, norm_extraF.dimshuffle((0, 2, 1))], axis=1)
paragraph_model = Bd_GRU_Batch_Tensor_Input_with_Mask(
X=paragraph_input,
Mask=para_mask,
hidden_dim=hidden_size,
U=U1,
W=W1,
b=b1,
Ub=U1_b,
Wb=W1_b,
bb=b1_b)
para_reps = paragraph_model.output_tensor #(batch, emb, para_len)
# #LSTM
# fwd_LSTM_para_dict=create_LSTM_para(rng, emb_size, hidden_size)
# bwd_LSTM_para_dict=create_LSTM_para(rng, emb_size, hidden_size)
# paragraph_para=fwd_LSTM_para_dict.values()+ bwd_LSTM_para_dict.values()# .values returns a list of parameters
# paragraph_model=Bd_LSTM_Batch_Tensor_Input_with_Mask(paragraph_input, para_mask, hidden_size, fwd_LSTM_para_dict, bwd_LSTM_para_dict)
# para_reps=paragraph_model.output_tensor
Qs_emb = embeddings[questions.flatten()].reshape(
(questions.shape[0], questions.shape[1], emb_size)).transpose(
(0, 2, 1)) #(#questions, emb_size, maxsenlength)
questions_model = Bd_GRU_Batch_Tensor_Input_with_Mask(
X=Qs_emb,
Mask=q_mask,
hidden_dim=hidden_size,
U=UQ,
W=WQ,
b=bQ,
Ub=UQ_b,
Wb=WQ_b,
bb=bQ_b)
# questions_reps=questions_model.output_sent_rep_maxpooling.reshape((batch_size, 1, hidden_size)) #(batch, 2*out_size)
questions_reps_tensor = questions_model.output_tensor
#questions_reps=T.repeat(questions_reps, para_reps.shape[2], axis=1)
# #LSTM for questions
# fwd_LSTM_q_dict=create_LSTM_para(rng, emb_size, hidden_size)
# bwd_LSTM_q_dict=create_LSTM_para(rng, emb_size, hidden_size)
# Q_para=fwd_LSTM_q_dict.values()+ bwd_LSTM_q_dict.values()# .values returns a list of parameters
# questions_model=Bd_LSTM_Batch_Tensor_Input_with_Mask(Qs_emb, q_mask, hidden_size, fwd_LSTM_q_dict, bwd_LSTM_q_dict)
# questions_reps_tensor=questions_model.output_tensor
#use CNN for question modeling
# Qs_emb_tensor4=Qs_emb.dimshuffle((0,'x', 1,2)) #(batch_size, 1, emb+3, maxparalen)
# conv_W, conv_b=create_conv_para(rng, filter_shape=(hidden_size, 1, emb_size, 5))
# Q_conv_para=[conv_W, conv_b]
# conv_model = Conv_with_input_para(rng, input=Qs_emb_tensor4,
# image_shape=(batch_size, 1, emb_size, q_len_limit),
# filter_shape=(hidden_size, 1, emb_size, 5), W=conv_W, b=conv_b)
# conv_output=conv_model.narrow_conv_out.reshape((batch_size, hidden_size, q_len_limit-5+1)) #(batch, 1, hidden_size, maxparalen-1)
# gru_mask=(q_mask[:,:-4]*q_mask[:,1:-3]*q_mask[:,2:-2]*q_mask[:,3:-1]*q_mask[:,4:]).reshape((batch_size, 1, q_len_limit-5+1))
# masked_conv_output=conv_output*gru_mask
# questions_conv_reps=T.max(masked_conv_output, axis=2).reshape((batch_size, 1, hidden_size))
# new_labels=T.gt(labels[:,:-1]+labels[:,1:], 0.0)
# ConvGRU_1=Conv_then_GRU_then_Classify(rng, concate_paragraph_input, Qs_emb, para_len_limit, q_len_limit, emb_size+3, hidden_size, emb_size, 2, batch_size, para_mask, q_mask, new_labels, 2)
# ConvGRU_1_dis=ConvGRU_1.masked_dis_inprediction
# padding_vec = T.zeros((batch_size, 1), dtype=theano.config.floatX)
# ConvGRU_1_dis_leftpad=T.concatenate([padding_vec, ConvGRU_1_dis], axis=1)
# ConvGRU_1_dis_rightpad=T.concatenate([ConvGRU_1_dis, padding_vec], axis=1)
# ConvGRU_1_dis_into_unigram=0.5*(ConvGRU_1_dis_leftpad+ConvGRU_1_dis_rightpad)
#
def example_in_batch(para_matrix, q_matrix):
#assume both are (hidden, len)
transpose_para_matrix = para_matrix.T
interaction_matrix = T.dot(transpose_para_matrix,
q_matrix) #(para_len, q_len)
norm_interaction_matrix = T.nnet.softmax(interaction_matrix)
return T.dot(q_matrix, norm_interaction_matrix.T) #(len, para_len)
batch_q_reps, updates = theano.scan(
fn=example_in_batch,
outputs_info=None,
sequences=[para_reps, questions_reps_tensor
]) #batch_q_reps (batch, hidden, para_len)
#attention distributions
norm_W_a1 = normalize_matrix(W_a1)
norm_W_a2 = normalize_matrix(W_a2)
norm_U_a = normalize_matrix(U_a)
transformed_para_reps = T.maximum(
T.dot(para_reps.transpose((0, 2, 1)), norm_W_a2), 0.0) #relu
transformed_q_reps = T.maximum(
T.dot(batch_q_reps.transpose((0, 2, 1)), norm_W_a1), 0.0)
#transformed_q_reps=T.repeat(transformed_q_reps, transformed_para_reps.shape[1], axis=1)
add_both = transformed_para_reps + transformed_q_reps
# U_c, W_c, b_c=create_GRU_para(rng, hidden_size, hidden_size)
# U_c_b, W_c_b, b_c_b=create_GRU_para(rng, hidden_size, hidden_size)
# accumu_para=[U_c, W_c, b_c, U_c_b, W_c_b, b_c_b]
# accumu_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_both.transpose((0,2,1)), Mask=para_mask, hidden_dim=hidden_size,U=U_c,W=W_c,b=b_c,Ub=U_c_b,Wb=W_c_b,bb=b_c_b)
# accu_both=accumu_model.output_tensor.transpose((0,2,1))
prior_att = T.concatenate([add_both, norm_extraF], axis=2)
#prior_att=T.concatenate([transformed_para_reps, transformed_q_reps], axis=2)
valid_indices = para_mask.flatten().nonzero()[0]
layer3 = LogisticRegression(rng,
input=prior_att.reshape(
(batch_size * prior_att.shape[1],
hidden_size + 3)),
n_in=hidden_size + 3,
n_out=2,
W=norm_U_a,
b=LR_b)
#error =layer3.negative_log_likelihood(labels.flatten()[valid_indices])
error = -T.sum(
T.log(layer3.p_y_given_x)
[valid_indices,
labels.flatten()[valid_indices]]) #[T.arange(y.shape[0]), y])
distributions = layer3.p_y_given_x[:, -1].reshape(
(batch_size, para_mask.shape[1]))
#distributions=layer3.y_pred.reshape((batch_size, para_mask.shape[1]))
# masked_dis=(distributions+ConvGRU_1_dis_into_unigram)*para_mask
masked_dis = distributions * para_mask
'''
strength = T.tanh(T.dot(prior_att, norm_U_a)) #(batch, #word, 1)
distributions=debug_print(strength.reshape((batch_size, paragraph.shape[1])), 'distributions')
para_mask=para_mask
masked_dis=distributions*para_mask
# masked_label=debug_print(labels*para_mask, 'masked_label')
# error=((masked_dis-masked_label)**2).mean()
label_mask=T.gt(labels,0.0)
neg_label_mask=T.lt(labels,0.0)
dis_masked=distributions*label_mask
remain_dis_masked=distributions*neg_label_mask
ans_size=T.sum(label_mask)
non_ans_size=T.sum(neg_label_mask)
pos_error=T.sum((dis_masked-label_mask)**2)/ans_size
neg_error=T.sum((remain_dis_masked-(-neg_label_mask))**2)/non_ans_size
error=pos_error+0.5*neg_error #(ans_size*1.0/non_ans_size)*
'''
# def AttentionLayer(q_rep, ext_M):
# theano_U_a=debug_print(norm_U_a, 'norm_U_a')
# prior_att=debug_print(T.nnet.sigmoid(T.dot(q_rep, norm_W_a1).reshape((1, hidden_size)) + T.dot(paragraph_model.output_matrix.transpose(), norm_W_a2)), 'prior_att')
# f __name__ == '__main__':
# prior_att=T.concatenate([prior_att, ext_M], axis=1)
#
# strength = debug_print(T.tanh(T.dot(prior_att, theano_U_a)), 'strength') #(#word, 1)
# return strength.transpose() #(1, #words)
# distributions, updates = theano.scan(
# AttentionLayer,
# sequences=[questions_reps,extraF] )
# distributions=debug_print(distributions.reshape((questions.shape[0],paragraph.shape[0])), 'distributions')
# labels=debug_print(labels, 'labels')
# label_mask=T.gt(labels,0.0)
# neg_label_mask=T.lt(labels,0.0)
# dis_masked=distributions*label_mask
# remain_dis_masked=distributions*neg_label_mask
# pos_error=((dis_masked-1)**2).mean()
# neg_error=((remain_dis_masked-(-1))**2).mean()
# error=pos_error+(T.sum(label_mask)*1.0/T.sum(neg_label_mask))*neg_error
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
L2_reg = L2norm_paraList(
[embeddings, U1, W1, U1_b, W1_b, UQ, WQ, UQ_b, WQ_b, W_a1, W_a2, U_a])
#L2_reg = L2norm_paraList(params)
cost = error #+ConvGRU_1.error#
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i /
(T.sqrt(acc) + 1e-8))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function(
[paragraph, questions, labels, para_mask, q_mask, extraF],
cost,
updates=updates,
on_unused_input='ignore')
test_model = theano.function(
[paragraph, questions, para_mask, q_mask, extraF],
masked_dis,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches = train_size / batch_size
# remain_train=train_size%batch_size
train_batch_start = list(numpy.arange(n_train_batches) *
batch_size) + [train_size - batch_size]
n_test_batches = test_size / batch_size
# remain_test=test_size%batch_size
test_batch_start = list(
numpy.arange(n_test_batches) * batch_size) + [test_size - batch_size]
max_F1_acc = 0.0
max_exact_acc = 0.0
cost_i = 0.0
train_ids = range(train_size)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.shuffle(train_ids)
iter_accu = 0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
# haha=para_mask[para_id:para_id+batch_size]
# print haha
# for i in range(batch_size):
# print len(haha[i])
cost_i += train_model(
np.asarray([
train_para_list[id]
for id in train_ids[para_id:para_id + batch_size]
],
dtype='int32'),
np.asarray([
train_Q_list[id]
for id in train_ids[para_id:para_id + batch_size]
],
dtype='int32'),
np.asarray([
train_label_list[id]
for id in train_ids[para_id:para_id + batch_size]
],
dtype='int32'),
np.asarray([
train_para_mask[id]
for id in train_ids[para_id:para_id + batch_size]
],
dtype=theano.config.floatX),
np.asarray([
train_mask[id]
for id in train_ids[para_id:para_id + batch_size]
],
dtype=theano.config.floatX),
np.asarray([
train_feature_matrixlist[id]
for id in train_ids[para_id:para_id + batch_size]
],
dtype=theano.config.floatX))
#print iter
if iter % 10 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
print 'Testing...'
past_time = time.time()
exact_match = 0.0
F1_match = 0.0
q_amount = 0
for test_para_id in test_batch_start:
distribution_matrix = test_model(
np.asarray(test_para_list[test_para_id:test_para_id +
batch_size],
dtype='int32'),
np.asarray(test_Q_list[test_para_id:test_para_id +
batch_size],
dtype='int32'),
np.asarray(test_para_mask[test_para_id:test_para_id +
batch_size],
dtype=theano.config.floatX),
np.asarray(test_mask[test_para_id:test_para_id +
batch_size],
dtype=theano.config.floatX),
np.asarray(
test_feature_matrixlist[test_para_id:test_para_id +
batch_size],
dtype=theano.config.floatX))
# print distribution_matrix
test_para_wordlist_list = test_text_list[
test_para_id:test_para_id + batch_size]
para_gold_ansset_list = q_ansSet_list[
test_para_id:test_para_id + batch_size]
paralist_extra_features = test_feature_matrixlist[
test_para_id:test_para_id + batch_size]
sub_para_mask = test_para_mask[test_para_id:test_para_id +
batch_size]
para_len = len(test_para_wordlist_list[0])
if para_len != len(distribution_matrix[0]):
print 'para_len!=len(distribution_matrix[0]):', para_len, len(
distribution_matrix[0])
exit(0)
# q_size=len(distribution_matrix)
q_amount += batch_size
# print q_size
# print test_para_word_list
Q_list_inword = test_Q_list_word[
test_para_id:test_para_id + batch_size]
for q in range(batch_size): #for each question
# if len(distribution_matrix[q])!=len(test_label_matrix[q]):
# print 'len(distribution_matrix[q])!=len(test_label_matrix[q]):', len(distribution_matrix[q]), len(test_label_matrix[q])
# else:
# ss=len(distribution_matrix[q])
# combine_list=[]
# for ii in range(ss):
# combine_list.append(str(distribution_matrix[q][ii])+'('+str(test_label_matrix[q][ii])+')')
# print combine_list
# exit(0)
# print 'distribution_matrix[q]:',distribution_matrix[q]
pred_ans = extract_ansList_attentionList(
test_para_wordlist_list[q], distribution_matrix[q],
np.asarray(paralist_extra_features[q],
dtype=theano.config.floatX),
sub_para_mask[q], Q_list_inword[q])
q_gold_ans_set = para_gold_ansset_list[q]
# print test_para_wordlist_list[q]
# print Q_list_inword[q]
# print pred_ans.encode('utf8'), q_gold_ans_set
if pred_ans in q_gold_ans_set:
exact_match += 1
F1 = MacroF1(pred_ans, q_gold_ans_set)
F1_match += F1
# match_amount=len(pred_ans_set & q_gold_ans_set)
# # print 'q_gold_ans_set:', q_gold_ans_set
# # print 'pred_ans_set:', pred_ans_set
# if match_amount>0:
# exact_match+=match_amount*1.0/len(pred_ans_set)
F1_acc = F1_match / q_amount
exact_acc = exact_match / q_amount
if F1_acc > max_F1_acc:
max_F1_acc = F1_acc
if exact_acc > max_exact_acc:
max_exact_acc = exact_acc
if max_exact_acc > max_EM:
store_model_to_file(
rootPath + 'Best_Paras_conv_' + str(max_exact_acc),
params)
print 'Finished storing best params at:', max_exact_acc
print 'current average F1:', F1_acc, '\t\tmax F1:', max_F1_acc, 'current exact:', exact_acc, '\t\tmax exact_acc:', max_exact_acc
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.02,
n_epochs=100,
emb_size=300,
batch_size=10,
filter_size=[3, 5],
maxSentLen=40,
hidden_size=[300, 300]):
model_options = locals().copy()
print "model options", model_options
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
srng = T.shared_randomstreams.RandomStreams(rng.randint(seed))
all_sentences, all_masks, all_labels, word2id = load_BBN_multi_labels_dataset(
maxlen=maxSentLen
) #minlen, include one label, at least one word in the sentence
train_sents = np.asarray(all_sentences[0], dtype='int32')
train_masks = np.asarray(all_masks[0], dtype=theano.config.floatX)
train_labels = np.asarray(all_labels[0], dtype='int32')
train_size = len(train_labels)
dev_sents = np.asarray(all_sentences[1], dtype='int32')
dev_masks = np.asarray(all_masks[1], dtype=theano.config.floatX)
dev_labels = np.asarray(all_labels[1], dtype='int32')
dev_size = len(dev_labels)
test_sents = np.asarray(all_sentences[2], dtype='int32')
test_masks = np.asarray(all_masks[2], dtype=theano.config.floatX)
test_labels = np.asarray(all_labels[2], dtype='int32')
test_size = len(test_labels)
vocab_size = len(word2id) + 1 # add one zero pad index
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
rand_values[0] = np.array(np.zeros(emb_size), dtype=theano.config.floatX)
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_word2vec()
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
#now, start to build the input form of the model
sents_id_matrix = T.imatrix('sents_id_matrix')
sents_mask = T.fmatrix('sents_mask')
labels = T.imatrix('labels') #batch*12
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
common_input = embeddings[sents_id_matrix.flatten()].reshape(
(batch_size, maxSentLen, emb_size)).dimshuffle(
0, 2, 1) #the input format can be adapted into CNN or GRU or LSTM
conv_W, conv_b = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size, filter_size[0]))
conv_W2, conv_b2 = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size,
filter_size[1]))
NN_para = [conv_W, conv_b, conv_W2, conv_b2]
conv_model = Conv_with_Mask(
rng,
input_tensor3=common_input,
mask_matrix=sents_mask,
image_shape=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
W=conv_W,
b=conv_b
) #mutiple mask with the conv_out to set the features by UNK to zero
sent_embeddings = conv_model.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
conv_model2 = Conv_with_Mask(
rng,
input_tensor3=common_input,
mask_matrix=sents_mask,
image_shape=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[1]),
W=conv_W2,
b=conv_b2
) #mutiple mask with the conv_out to set the features by UNK to zero
sent_embeddings2 = conv_model2.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
LR_input = T.concatenate([sent_embeddings, sent_embeddings2], axis=1)
LR_input_size = hidden_size[0] * 2
#classification layer, it is just mapping from a feature vector of size "hidden_size" to a vector of only two values: positive, negative
U_a = create_ensemble_para(
rng, 12, LR_input_size) # the weight matrix hidden_size*2
LR_b = theano.shared(value=np.zeros((12, ), dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
LR_para = [U_a, LR_b]
layer_LR = LogisticRegression(
rng, input=LR_input, n_in=LR_input_size, n_out=12, W=U_a, b=LR_b
) #basically it is a multiplication between weight matrix and input feature vector
score_matrix = T.nnet.sigmoid(layer_LR.before_softmax) #batch * 12
prob_pos = T.where(labels < 1, 1.0 - score_matrix, score_matrix)
loss = -T.mean(T.log(prob_pos))
# loss=layer_LR.negative_log_likelihood(labels) #for classification task, we usually used negative log likelihood as loss, the lower the better.
params = [embeddings
] + NN_para + LR_para # put all model parameters together
cost = loss #+Div_reg*diversify_reg#+L2_weight*L2_reg
updates = Gradient_Cost_Para(cost, params, learning_rate)
'''
testing
'''
binarize_prob = T.where(score_matrix > 0.5, 1, 0)
#train_model = theano.function([sents_id_matrix, sents_mask, labels], cost, updates=updates, on_unused_input='ignore')
train_model = theano.function([sents_id_matrix, sents_mask, labels],
cost,
updates=updates,
allow_input_downcast=True,
on_unused_input='ignore')
# dev_model = theano.function([sents_id_matrix, sents_mask, labels], layer_LR.errors(labels), allow_input_downcast=True, on_unused_input='ignore')
test_model = theano.function([sents_id_matrix, sents_mask],
binarize_prob,
allow_input_downcast=True,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
n_train_batches = train_size / batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
# n_dev_batches=dev_size/batch_size
# dev_batch_start=list(np.arange(n_dev_batches)*batch_size)+[dev_size-batch_size]
n_test_batches = test_size / batch_size
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
# max_acc_dev=0.0
max_meanf1_test = 0.0
max_weightf1_test = 0.0
train_indices = range(train_size)
while epoch < n_epochs:
epoch = epoch + 1
random.Random(100).shuffle(train_indices)
iter_accu = 0
cost_i = 0.0
for batch_id in train_batch_start: #for each batch
# iter means how many batches have been run, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
train_id_batch = train_indices[batch_id:batch_id + batch_size]
cost_i += train_model(train_sents[train_id_batch],
train_masks[train_id_batch],
train_labels[train_id_batch])
#after each 1000 batches, we test the performance of the model on all test data
if iter % 20 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
past_time = time.time()
error_sum = 0.0
all_pred_labels = []
all_gold_labels = []
for test_batch_id in test_batch_start: # for each test batch
pred_labels = test_model(
test_sents[test_batch_id:test_batch_id + batch_size],
test_masks[test_batch_id:test_batch_id + batch_size])
gold_labels = test_labels[test_batch_id:test_batch_id +
batch_size]
all_pred_labels.append(pred_labels)
all_gold_labels.append(gold_labels)
all_pred_labels = np.concatenate(all_pred_labels)
all_gold_labels = np.concatenate(all_gold_labels)
test_mean_f1, test_weight_f1 = average_f1_two_array_by_col(
all_pred_labels, all_gold_labels)
if test_weight_f1 > max_weightf1_test:
max_weightf1_test = test_weight_f1
if test_mean_f1 > max_meanf1_test:
max_meanf1_test = test_mean_f1
print '\t\t\t\t\t\t\t\tcurrent f1s:', test_mean_f1, test_weight_f1, '\t\tmax_f1:', max_meanf1_test, max_weightf1_test
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
return max_acc_test
def evaluate_lenet5(learning_rate=0.1, n_epochs=4, L2_weight=0.001, emb_size=70, batch_size=50, filter_size=3, maxSentLen=50, nn='CNN'):
hidden_size=emb_size
model_options = locals().copy()
print "model options", model_options
rng = np.random.RandomState(1234) #random seed, control the model generates the same results
all_sentences_l, all_masks_l, all_sentences_r, all_masks_r,all_labels, word2id =load_SNLI_dataset(maxlen=maxSentLen) #minlen, include one label, at least one word in the sentence
train_sents_l=np.asarray(all_sentences_l[0], dtype='int32')
dev_sents_l=np.asarray(all_sentences_l[1], dtype='int32')
test_sents_l=np.asarray(all_sentences_l[2], dtype='int32')
train_masks_l=np.asarray(all_masks_l[0], dtype=theano.config.floatX)
dev_masks_l=np.asarray(all_masks_l[1], dtype=theano.config.floatX)
test_masks_l=np.asarray(all_masks_l[2], dtype=theano.config.floatX)
train_sents_r=np.asarray(all_sentences_r[0], dtype='int32')
dev_sents_r=np.asarray(all_sentences_r[1] , dtype='int32')
test_sents_r=np.asarray(all_sentences_r[2] , dtype='int32')
train_masks_r=np.asarray(all_masks_r[0], dtype=theano.config.floatX)
dev_masks_r=np.asarray(all_masks_r[1], dtype=theano.config.floatX)
test_masks_r=np.asarray(all_masks_r[2], dtype=theano.config.floatX)
train_labels_store=np.asarray(all_labels[0], dtype='int32')
dev_labels_store=np.asarray(all_labels[1], dtype='int32')
test_labels_store=np.asarray(all_labels[2], dtype='int32')
train_size=len(train_labels_store)
dev_size=len(dev_labels_store)
test_size=len(test_labels_store)
vocab_size=len(word2id)+1
rand_values=rng.normal(0.0, 0.01, (vocab_size, emb_size)) #generate a matrix by Gaussian distribution
#here, we leave code for loading word2vec to initialize words
# rand_values[0]=np.array(np.zeros(emb_size),dtype=theano.config.floatX)
# id2word = {y:x for x,y in word2id.iteritems()}
# word2vec=load_word2vec()
# rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings=theano.shared(value=np.array(rand_values,dtype=theano.config.floatX), borrow=True) #wrap up the python variable "rand_values" into theano variable
#now, start to build the input form of the model
sents_ids_l=T.imatrix()
sents_mask_l=T.fmatrix()
sents_ids_r=T.imatrix()
sents_mask_r=T.fmatrix()
labels=T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
common_input_l=embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)) #the input format can be adapted into CNN or GRU or LSTM
common_input_r=embeddings[sents_ids_r.flatten()].reshape((batch_size,maxSentLen, emb_size))
#conv
if nn=='CNN':
conv_W, conv_b=create_conv_para(rng, filter_shape=(hidden_size, 1, emb_size, filter_size))
conv_W_into_matrix=conv_W.reshape((conv_W.shape[0], conv_W.shape[2]*conv_W.shape[3]))
NN_para=[conv_W, conv_b]
conv_input_l = common_input_l.dimshuffle((0,'x', 2,1)) #(batch_size, 1, emb_size, maxsenlen)
conv_model_l = Conv_with_input_para(rng, input=conv_input_l,
image_shape=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size, 1, emb_size, filter_size), W=conv_W, b=conv_b)
conv_output_l=conv_model_l.narrow_conv_out #(batch, 1, hidden_size, maxsenlen-filter_size+1)
conv_output_into_tensor3_l=conv_output_l.reshape((batch_size, hidden_size, maxSentLen-filter_size+1))
mask_for_conv_output_l=T.repeat(sents_mask_l[:,filter_size-1:].reshape((batch_size, 1, maxSentLen-filter_size+1)), hidden_size, axis=1) #(batch_size, emb_size, maxSentLen-filter_size+1)
mask_for_conv_output_l=(1.0-mask_for_conv_output_l)*(mask_for_conv_output_l-10)
masked_conv_output_l=conv_output_into_tensor3_l+mask_for_conv_output_l #mutiple mask with the conv_out to set the features by UNK to zero
sent_embeddings_l=T.max(masked_conv_output_l, axis=2) #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
conv_input_r = common_input_r.dimshuffle((0,'x', 2,1)) #(batch_size, 1, emb_size, maxsenlen)
conv_model_r = Conv_with_input_para(rng, input=conv_input_r,
image_shape=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size, 1, emb_size, filter_size), W=conv_W, b=conv_b)
conv_output_r=conv_model_r.narrow_conv_out #(batch, 1, hidden_size, maxsenlen-filter_size+1)
conv_output_into_tensor3_r=conv_output_r.reshape((batch_size, hidden_size, maxSentLen-filter_size+1))
mask_for_conv_output_r=T.repeat(sents_mask_r[:,filter_size-1:].reshape((batch_size, 1, maxSentLen-filter_size+1)), hidden_size, axis=1) #(batch_size, emb_size, maxSentLen-filter_size+1)
mask_for_conv_output_r=(1.0-mask_for_conv_output_r)*(mask_for_conv_output_r-10)
masked_conv_output_r=conv_output_into_tensor3_r+mask_for_conv_output_r #mutiple mask with the conv_out to set the features by UNK to zero
sent_embeddings_r=T.max(masked_conv_output_r, axis=2) #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
#GRU
if nn=='GRU':
U1, W1, b1=create_GRU_para(rng, emb_size, hidden_size)
NN_para=[U1, W1, b1] #U1 includes 3 matrices, W1 also includes 3 matrices b1 is bias
gru_input_l = common_input_l.dimshuffle((0,2,1)) #gru requires input (batch_size, emb_size, maxSentLen)
gru_layer_l=GRU_Batch_Tensor_Input_with_Mask(gru_input_l, sents_mask_l, hidden_size, U1, W1, b1)
sent_embeddings_l=gru_layer_l.output_sent_rep # (batch_size, hidden_size)
gru_input_r = common_input_r.dimshuffle((0,2,1)) #gru requires input (batch_size, emb_size, maxSentLen)
gru_layer_r=GRU_Batch_Tensor_Input_with_Mask(gru_input_r, sents_mask_r, hidden_size, U1, W1, b1)
sent_embeddings_r=gru_layer_r.output_sent_rep # (batch_size, hidden_size)
#LSTM
if nn=='LSTM':
LSTM_para_dict=create_LSTM_para(rng, emb_size, hidden_size)
NN_para=LSTM_para_dict.values() # .values returns a list of parameters
lstm_input_l = common_input_l.dimshuffle((0,2,1)) #LSTM has the same inpur format with GRU
lstm_layer_l=LSTM_Batch_Tensor_Input_with_Mask(lstm_input_l, sents_mask_l, hidden_size, LSTM_para_dict)
sent_embeddings_l=lstm_layer_l.output_sent_rep # (batch_size, hidden_size)
lstm_input_r = common_input_r.dimshuffle((0,2,1)) #LSTM has the same inpur format with GRU
lstm_layer_r=LSTM_Batch_Tensor_Input_with_Mask(lstm_input_r, sents_mask_r, hidden_size, LSTM_para_dict)
sent_embeddings_r=lstm_layer_r.output_sent_rep # (batch_size, hidden_size)
HL_layer_1_input = T.concatenate([sent_embeddings_l,sent_embeddings_r, sent_embeddings_l*sent_embeddings_r, cosine_matrix1_matrix2_rowwise(sent_embeddings_l,sent_embeddings_r).dimshuffle(0,'x')],axis=1)
HL_layer_1_input_size = hidden_size*3+1
HL_layer_1=HiddenLayer(rng, input=HL_layer_1_input, n_in=HL_layer_1_input_size, n_out=hidden_size, activation=T.tanh)
HL_layer_2=HiddenLayer(rng, input=HL_layer_1.output, n_in=hidden_size, n_out=hidden_size, activation=T.tanh)
#classification layer, it is just mapping from a feature vector of size "hidden_size" to a vector of only two values: positive, negative
LR_input_size=HL_layer_1_input_size+2*hidden_size
U_a = create_ensemble_para(rng, 3, LR_input_size) # the weight matrix hidden_size*2
LR_b = theano.shared(value=np.zeros((3,),dtype=theano.config.floatX),name='LR_b', borrow=True) #bias for each target class
LR_para=[U_a, LR_b]
LR_input=T.concatenate([HL_layer_1_input, HL_layer_1.output, HL_layer_2.output],axis=1)
layer_LR=LogisticRegression(rng, input=T.tanh(LR_input), n_in=LR_input_size, n_out=3, W=U_a, b=LR_b) #basically it is a multiplication between weight matrix and input feature vector
loss=layer_LR.negative_log_likelihood(labels) #for classification task, we usually used negative log likelihood as loss, the lower the better.
params = [embeddings]+NN_para+LR_para+HL_layer_1.params+HL_layer_2.params # put all model parameters together
# L2_reg =L2norm_paraList([embeddings,conv_W, U_a])
# diversify_reg= Diversify_Reg(U_a.T)+Diversify_Reg(conv_W_into_matrix)
cost=loss#+Div_reg*diversify_reg#+L2_weight*L2_reg
grads = T.grad(cost, params) # create a list of gradients for all model parameters
accumulator=[]
for para_i in params:
eps_p=np.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / (T.sqrt(acc)+1e-8))) #1e-8 is add to get rid of zero division
updates.append((acc_i, acc))
#train_model = theano.function([sents_id_matrix, sents_mask, labels], cost, updates=updates, on_unused_input='ignore')
train_model = theano.function([sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels], cost, updates=updates, allow_input_downcast=True, on_unused_input='ignore')
dev_model = theano.function([sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels], layer_LR.errors(labels), allow_input_downcast=True, on_unused_input='ignore')
test_model = theano.function([sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels], layer_LR.errors(labels), allow_input_downcast=True, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
n_train_batches=train_size/batch_size
train_batch_start=list(np.arange(n_train_batches)*batch_size)+[train_size-batch_size]
n_dev_batches=dev_size/batch_size
dev_batch_start=list(np.arange(n_dev_batches)*batch_size)+[dev_size-batch_size]
n_test_batches=test_size/batch_size
test_batch_start=list(np.arange(n_test_batches)*batch_size)+[test_size-batch_size]
max_acc_dev=0.0
max_acc_test=0.0
while epoch < n_epochs:
epoch = epoch + 1
train_indices = range(train_size)
random.Random(200).shuffle(train_indices) #shuffle training set for each new epoch, is supposed to promote performance, but not garrenteed
iter_accu=0
cost_i=0.0
for batch_id in train_batch_start: #for each batch
# iter means how many batches have been run, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
iter_accu+=1
train_id_batch = train_indices[batch_id:batch_id+batch_size]
cost_i+= train_model(
train_sents_l[train_id_batch],
train_masks_l[train_id_batch],
train_sents_r[train_id_batch],
train_masks_r[train_id_batch],
train_labels_store[train_id_batch])
#after each 1000 batches, we test the performance of the model on all test data
if iter%500==0:
print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
past_time = time.time()
# if epoch >=3 and iter >= len(train_batch_start)*2.0/3 and iter%500==0:
# print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
# past_time = time.time()
error_sum=0.0
for dev_batch_id in dev_batch_start: # for each test batch
error_i=dev_model(
dev_sents_l[dev_batch_id:dev_batch_id+batch_size],
dev_masks_l[dev_batch_id:dev_batch_id+batch_size],
dev_sents_r[dev_batch_id:dev_batch_id+batch_size],
dev_masks_r[dev_batch_id:dev_batch_id+batch_size],
dev_labels_store[dev_batch_id:dev_batch_id+batch_size]
)
error_sum+=error_i
dev_accuracy=1.0-error_sum/(len(dev_batch_start))
if dev_accuracy > max_acc_dev:
max_acc_dev=dev_accuracy
print 'current dev_accuracy:', dev_accuracy, '\t\t\t\t\tmax max_acc_dev:', max_acc_dev
#best dev model, do test
error_sum=0.0
for test_batch_id in test_batch_start: # for each test batch
error_i=test_model(
test_sents_l[test_batch_id:test_batch_id+batch_size],
test_masks_l[test_batch_id:test_batch_id+batch_size],
test_sents_r[test_batch_id:test_batch_id+batch_size],
test_masks_r[test_batch_id:test_batch_id+batch_size],
test_labels_store[test_batch_id:test_batch_id+batch_size]
)
error_sum+=error_i
test_accuracy=1.0-error_sum/(len(test_batch_start))
if test_accuracy > max_acc_test:
max_acc_test=test_accuracy
print '\t\tcurrent testbacc:', test_accuracy, '\t\t\t\t\tmax_acc_test:', max_acc_test
else:
print 'current dev_accuracy:', dev_accuracy, '\t\t\t\t\tmax max_acc_dev:', max_acc_dev
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
return max_acc_test
def evaluate_lenet5(learning_rate=0.1, n_epochs=2000, batch_size=500, test_batch_size=500, emb_size=300, hidden_size=300,
L2_weight=0.0001, margin=0.5,
train_size=4000000, test_size=1000,
max_context_len=25, max_span_len=7, max_q_len=40, max_EM=0.052):
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/SQuAD/';
rng = np.random.RandomState(23455)
word2id,train_questions,train_questions_mask,train_lefts,train_lefts_mask,train_spans,train_spans_mask,train_rights,train_rights_mask=load_SQUAD_hinrich(train_size, max_context_len, max_span_len, max_q_len)
test_ground_truth,test_candidates,test_questions,test_questions_mask,test_lefts,test_lefts_mask,test_spans,test_spans_mask,test_rights,test_rights_mask=load_dev_hinrich(word2id, test_size, max_context_len, max_span_len, max_q_len)
overall_vocab_size=len(word2id)
print 'vocab size:', overall_vocab_size
rand_values=random_value_normal((overall_vocab_size+1, emb_size), theano.config.floatX, np.random.RandomState(1234))
# rand_values[0]=np.array(np.zeros(emb_size),dtype=theano.config.floatX)
id2word = {y:x for x,y in word2id.iteritems()}
word2vec=load_word2vec()
rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings=theano.shared(value=rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
left=T.imatrix() #(2*batch, len)
left_mask=T.fmatrix() #(2*batch, len)
span=T.imatrix() #(2*batch, span_len)
span_mask=T.fmatrix() #(2*batch, span_len)
right=T.imatrix() #(2*batch, len)
right_mask=T.fmatrix() #(2*batch, len)
q=T.imatrix() #(2*batch, len_q)
q_mask=T.fmatrix() #(2*batch, len_q)
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
U1, W1, b1=create_GRU_para(rng, emb_size, hidden_size)
U1_b, W1_b, b1_b=create_GRU_para(rng, emb_size, hidden_size)
GRU1_para=[U1, W1, b1, U1_b, W1_b, b1_b]
U2, W2, b2=create_GRU_para(rng, hidden_size, hidden_size)
U2_b, W2_b, b2_b=create_GRU_para(rng, hidden_size, hidden_size)
GRU2_para=[U2, W2, b2, U2_b, W2_b, b2_b]
W_a1 = create_ensemble_para(rng, hidden_size, hidden_size)# init_weights((2*hidden_size, hidden_size))
W_a2 = create_ensemble_para(rng, hidden_size, hidden_size)
attend_para=[W_a1, W_a2]
params = [embeddings]+GRU1_para+attend_para+GRU2_para
# load_model_from_file(rootPath+'Best_Para_dim'+str(emb_size), params)
left_input = embeddings[left.flatten()].reshape((left.shape[0], left.shape[1], emb_size)).transpose((0, 2,1)) # (2*batch_size, emb_size, len_context)
span_input = embeddings[span.flatten()].reshape((span.shape[0], span.shape[1], emb_size)).transpose((0, 2,1)) # (2*batch_size, emb_size, len_span)
right_input = embeddings[right.flatten()].reshape((right.shape[0], right.shape[1], emb_size)).transpose((0, 2,1)) # (2*batch_size, emb_size, len_context)
q_input = embeddings[q.flatten()].reshape((q.shape[0], q.shape[1], emb_size)).transpose((0, 2,1)) # (2*batch_size, emb_size, len_q)
left_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=left_input, Mask=left_mask, hidden_dim=hidden_size,U=U1,W=W1,b=b1,Ub=U1_b,Wb=W1_b,bb=b1_b)
left_reps=left_model.output_tensor #(batch, emb, para_len)
span_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=span_input, Mask=span_mask, hidden_dim=hidden_size,U=U1,W=W1,b=b1,Ub=U1_b,Wb=W1_b,bb=b1_b)
span_reps=span_model.output_tensor #(batch, emb, para_len)
right_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=right_input, Mask=right_mask, hidden_dim=hidden_size,U=U1,W=W1,b=b1,Ub=U1_b,Wb=W1_b,bb=b1_b)
right_reps=right_model.output_tensor #(batch, emb, para_len)
q_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=q_input, Mask=q_mask, hidden_dim=hidden_size,U=U1,W=W1,b=b1,Ub=U1_b,Wb=W1_b,bb=b1_b)
q_reps=q_model.output_tensor #(batch, emb, para_len)
#interaction
left_reps_via_q_reps, q_reps_via_left_reps=attention_dot_prod_between_2tensors(left_reps, q_reps)
span_reps_via_q_reps, q_reps_via_span_reps=attention_dot_prod_between_2tensors(span_reps, q_reps)
right_reps_via_q_reps, q_reps_via_right_reps=attention_dot_prod_between_2tensors(right_reps, q_reps)
# q_reps_via_left_reps=attention_dot_prod_between_2tensors(q_reps, left_reps)
# q_reps_via_span_reps=attention_dot_prod_between_2tensors(q_reps, span_reps)
# q_reps_via_right_reps=attention_dot_prod_between_2tensors(q_reps, right_reps)
#combine
origin_W=normalize_matrix(W_a1)
attend_W=normalize_matrix(W_a2)
left_origin_reps=T.dot(left_reps.dimshuffle(0, 2,1), origin_W)
span_origin_reps=T.dot(span_reps.dimshuffle(0, 2,1), origin_W)
right_origin_reps=T.dot(right_reps.dimshuffle(0, 2,1), origin_W)
q_origin_reps=T.dot(q_reps.dimshuffle(0, 2,1), origin_W)
left_attend_q_reps=T.dot(q_reps_via_left_reps.dimshuffle(0, 2,1), attend_W)
span_attend_q_reps=T.dot(q_reps_via_span_reps.dimshuffle(0, 2,1), attend_W)
right_attend_q_reps=T.dot(q_reps_via_right_reps.dimshuffle(0, 2,1), attend_W)
q_attend_left_reps=T.dot(left_reps_via_q_reps.dimshuffle(0, 2,1), attend_W)
q_attend_span_reps=T.dot(span_reps_via_q_reps.dimshuffle(0, 2,1), attend_W)
q_attend_right_reps=T.dot(right_reps_via_q_reps.dimshuffle(0, 2,1), attend_W)
add_left=left_origin_reps+q_attend_left_reps #(2*batch, len ,hidden)
add_span=span_origin_reps+q_attend_span_reps
add_right=right_origin_reps+q_attend_right_reps
add_q_by_left=q_origin_reps+left_attend_q_reps
add_q_by_span=q_origin_reps+span_attend_q_reps
add_q_by_right=q_origin_reps+right_attend_q_reps
#second GRU
add_left_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_left.dimshuffle(0,2,1), Mask=left_mask, hidden_dim=hidden_size,U=U2,W=W2,b=b2,Ub=U2_b,Wb=W2_b,bb=b2_b)
add_left_reps=add_left_model.output_sent_rep_maxpooling #(batch, hidden_dim)
add_span_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_span.dimshuffle(0,2,1), Mask=span_mask, hidden_dim=hidden_size,U=U2,W=W2,b=b2,Ub=U2_b,Wb=W2_b,bb=b2_b)
add_span_reps=add_span_model.output_sent_rep_maxpooling #(batch, hidden_dim)
add_right_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_right.dimshuffle(0,2,1), Mask=right_mask, hidden_dim=hidden_size,U=U2,W=W2,b=b2,Ub=U2_b,Wb=W2_b,bb=b2_b)
add_right_reps=add_right_model.output_sent_rep_maxpooling #(batch, hidden_dim)
add_q_by_left_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_q_by_left.dimshuffle(0,2,1), Mask=q_mask, hidden_dim=hidden_size,U=U2,W=W2,b=b2,Ub=U2_b,Wb=W2_b,bb=b2_b)
add_q_by_left_reps=add_q_by_left_model.output_sent_rep_maxpooling #(batch, hidden_dim)
add_q_by_span_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_q_by_span.dimshuffle(0,2,1), Mask=q_mask, hidden_dim=hidden_size,U=U2,W=W2,b=b2,Ub=U2_b,Wb=W2_b,bb=b2_b)
add_q_by_span_reps=add_q_by_span_model.output_sent_rep_maxpooling #(batch, hidden_dim)
add_q_by_right_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_q_by_right.dimshuffle(0,2,1), Mask=q_mask, hidden_dim=hidden_size,U=U2,W=W2,b=b2,Ub=U2_b,Wb=W2_b,bb=b2_b)
add_q_by_right_reps=add_q_by_right_model.output_sent_rep_maxpooling #(batch, hidden_dim)
paragraph_concat=T.concatenate([add_left_reps, add_span_reps, add_right_reps], axis=1) #(batch, 3*hidden)
question_concat=T.concatenate([add_q_by_left_reps, add_q_by_span_reps, add_q_by_right_reps], axis=1) #(batch, 3*hidden)
simi_list=cosine_row_wise_twoMatrix(paragraph_concat, question_concat) #(2*batch)
pos_simi_vec=simi_list[::2]
neg_simi_vec=simi_list[1::2]
raw_loss=T.maximum(0.0, margin+neg_simi_vec-pos_simi_vec)
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
# L2_reg =L2norm_paraList([embeddings,U1, W1, U1_b, W1_b,UQ, WQ , UQ_b, WQ_b, W_a1, W_a2, U_a])
#L2_reg = L2norm_paraList(params)
cost=T.sum(raw_loss)#+ConvGRU_1.error#
accumulator=[]
for para_i in params:
eps_p=np.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / (T.sqrt(acc)+1e-8))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([left, left_mask, span, span_mask, right, right_mask, q, q_mask], cost, updates=updates,on_unused_input='ignore')
test_model = theano.function([left, left_mask, span, span_mask, right, right_mask, q, q_mask], simi_list, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = np.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches=train_size/batch_size #batch_size means how many pairs
remain_train=train_size%batch_size
# train_batch_start=list(np.arange(n_train_batches)*batch_size*2)+[train_size*2-batch_size*2] # always ou shu
if remain_train>0:
train_batch_start=list(np.arange(n_train_batches)*batch_size)+[train_size-batch_size]
else:
train_batch_start=list(np.arange(n_train_batches)*batch_size)
max_F1_acc=0.0
max_exact_acc=0.0
cost_i=0.0
train_odd_ids = list(np.arange(train_size)*2)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.shuffle(train_odd_ids)
iter_accu=0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
iter_accu+=1
train_id_list=[[train_odd_id, train_odd_id+1] for train_odd_id in train_odd_ids[para_id:para_id+batch_size]]
train_id_list=sum(train_id_list,[])
# print train_id_list
cost_i+= train_model(
np.asarray([train_lefts[id] for id in train_id_list], dtype='int32'),
np.asarray([train_lefts_mask[id] for id in train_id_list], dtype=theano.config.floatX),
np.asarray([train_spans[id] for id in train_id_list], dtype='int32'),
np.asarray([train_spans_mask[id] for id in train_id_list], dtype=theano.config.floatX),
np.asarray([train_rights[id] for id in train_id_list], dtype='int32'),
np.asarray([train_rights_mask[id] for id in train_id_list], dtype=theano.config.floatX),
np.asarray([train_questions[id] for id in train_id_list], dtype='int32'),
np.asarray([train_questions_mask[id] for id in train_id_list], dtype=theano.config.floatX))
#print iter
if iter%100==0:
print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
print 'Testing...'
past_time = time.time()
exact_match=0.0
F1_match=0.0
for test_pair_id in range(test_size):
test_example_lefts=test_lefts[test_pair_id]
test_example_lefts_mask=test_lefts_mask[test_pair_id]
test_example_spans=test_spans[test_pair_id]
test_example_spans_mask=test_spans_mask[test_pair_id]
test_example_rights=test_rights[test_pair_id]
test_example_rights_mask=test_rights_mask[test_pair_id]
test_example_questions=test_questions[test_pair_id]
test_example_questions_mask=test_questions_mask[test_pair_id]
test_example_candidates=test_candidates[test_pair_id]
test_example_size=len(test_example_lefts)
# print 'test_pair_id, test_example_size:', test_pair_id, test_example_size
if test_example_size < test_batch_size:
#pad
pad_size=test_batch_size-test_example_size
test_example_lefts+=test_example_lefts[-1:]*pad_size
test_example_lefts_mask+=test_example_lefts_mask[-1:]*pad_size
test_example_spans+=test_example_spans[-1:]*pad_size
test_example_spans_mask+=test_example_spans_mask[-1:]*pad_size
test_example_rights+=test_example_rights[-1:]*pad_size
test_example_rights_mask+=test_example_rights_mask[-1:]*pad_size
test_example_questions+=test_example_questions[-1:]*pad_size
test_example_questions_mask+=test_example_questions_mask[-1:]*pad_size
test_example_candidates+=test_example_candidates[-1:]*pad_size
test_example_size=test_batch_size
n_test_batches=test_example_size/test_batch_size
n_test_remain=test_example_size%test_batch_size
if n_test_remain > 0:
test_batch_start=list(np.arange(n_test_batches)*test_batch_size)+[test_example_size-test_batch_size]
else:
test_batch_start=list(np.arange(n_test_batches)*test_batch_size)
all_simi_list=[]
all_cand_list=[]
for test_para_id in test_batch_start:
simi_return_vector=test_model(
np.asarray(test_example_lefts[test_para_id:test_para_id+test_batch_size], dtype='int32'),
np.asarray(test_example_lefts_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
np.asarray(test_example_spans[test_para_id:test_para_id+test_batch_size], dtype='int32'),
np.asarray(test_example_spans_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
np.asarray(test_example_rights[test_para_id:test_para_id+test_batch_size], dtype='int32'),
np.asarray(test_example_rights_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
np.asarray(test_example_questions[test_para_id:test_para_id+test_batch_size], dtype='int32'),
np.asarray(test_example_questions_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX))
candidate_list=test_example_candidates[test_para_id:test_para_id+test_batch_size]
all_simi_list+=list(simi_return_vector)
all_cand_list+=candidate_list
top1_cand=all_cand_list[np.argsort(all_simi_list)[-1]]
# print top1_cand, test_ground_truth[test_pair_id]
if top1_cand == test_ground_truth[test_pair_id]:
exact_match+=1
F1=macrof1(top1_cand, test_ground_truth[test_pair_id])
# print '\t\t\t', F1
F1_match+=F1
# match_amount=len(pred_ans_set & q_gold_ans_set)
# # print 'q_gold_ans_set:', q_gold_ans_set
# # print 'pred_ans_set:', pred_ans_set
# if match_amount>0:
# exact_match+=match_amount*1.0/len(pred_ans_set)
F1_acc=F1_match/test_size
exact_acc=exact_match/test_size
if F1_acc> max_F1_acc:
max_F1_acc=F1_acc
# store_model_to_file(params, emb_size)
if exact_acc> max_exact_acc:
max_exact_acc=exact_acc
if max_exact_acc > max_EM:
store_model_to_file(rootPath+'Best_Para_'+str(max_EM), params)
print 'Finished storing best params at:', max_exact_acc
print 'current average F1:', F1_acc, '\t\tmax F1:', max_F1_acc, 'current exact:', exact_acc, '\t\tmax exact_acc:', max_exact_acc
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.01,
n_epochs=100,
batch_size=100,
emb_size=300,
char_emb_size=20,
hidden_size=10,
L2_weight=0.0001,
p_len_limit=400,
test_p_len_limit=100,
q_len_limit=20,
char_len=15,
filter_size=[5, 5, 5, 5, 5],
char_filter_size=5,
margin=0.85,
extra_size=5 + 11,
extra_emb=10,
distance=10,
distance_emb=10,
comment='add distance embs'): #extra_size=3+46+7
test_batch_size = batch_size
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/SQuAD/'
rng = numpy.random.RandomState(23455)
word2id = {}
char2id = {}
train_Q_list, train_para_list, train_Q_mask, train_para_mask, train_Q_char_list, train_para_char_list, train_Q_char_mask, train_para_char_mask, train_span_label_list, train_word_label_list, train_para_extras, word2id, char2id = load_squad_cnn_rank_span_word_train(
word2id, char2id, p_len_limit, q_len_limit, char_len)
test_Q_list, test_para_list, test_Q_mask, test_para_mask, test_Q_char_list, test_para_char_list, test_Q_char_mask, test_para_char_mask, q_idlist, test_para_extras, word2id, char2id, test_para_wordlist_list = load_squad_cnn_rank_span_word_dev(
word2id, char2id, test_p_len_limit, q_len_limit, char_len)
'''
#store variables into file
'''
# train_variables = [train_Q_list,train_para_list, train_Q_mask, train_para_mask, train_Q_char_list,train_para_char_list, train_Q_char_mask, train_para_char_mask, train_span_label_list, train_word_label_list, train_para_extras]
# test_variables =[test_Q_list, test_para_list, test_Q_mask, test_para_mask,test_Q_char_list, test_para_char_list, test_Q_char_mask, test_para_char_mask, q_idlist, test_para_extras, word2id, char2id, test_para_wordlist_list]
# with open(rootPath+'extra.3.pickle', 'wb') as f: # Python 3: open(..., 'wb')
# cPickle.dump(train_variables+test_variables, f, protocol=cPickle.HIGHEST_PROTOCOL)
# f.close()
# print 'variable stored successfully'
# exit(0)
'''
load variables from file
'''
# before_load_time = time.time()
# with open(rootPath+'extra.3.pickle', 'rb') as f: # Python 3: open(..., 'rb')
# train_Q_list,train_para_list, train_Q_mask, train_para_mask, train_Q_char_list,train_para_char_list, train_Q_char_mask, train_para_char_mask, train_span_label_list, train_word_label_list, train_para_extras,test_Q_list, test_para_list, test_Q_mask, test_para_mask,test_Q_char_list, test_para_char_list, test_Q_char_mask, test_para_char_mask, q_idlist, test_para_extras, word2id, char2id, test_para_wordlist_list = cPickle.load(f)
# f.close()
# print 'load data variables successfully, spend: ', (time.time()-before_load_time)/60.0, ' mins'
train_size = len(train_para_list)
test_size = len(test_para_list)
train_Q_list = numpy.asarray(train_Q_list, dtype='int32')
train_para_list = numpy.asarray(train_para_list, dtype='int32')
train_Q_mask = numpy.asarray(train_Q_mask, dtype=theano.config.floatX)
train_para_mask = numpy.asarray(train_para_mask,
dtype=theano.config.floatX)
train_Q_char_list = numpy.asarray(train_Q_char_list, dtype='int32')
train_para_char_list = numpy.asarray(train_para_char_list, dtype='int32')
train_Q_char_mask = numpy.asarray(train_Q_char_mask,
dtype=theano.config.floatX)
train_para_char_mask = numpy.asarray(train_para_char_mask,
dtype=theano.config.floatX)
train_para_extras = numpy.asarray(train_para_extras,
dtype=theano.config.floatX)
train_span_label_list = numpy.asarray(train_span_label_list, dtype='int32')
train_word_label_list = numpy.asarray(train_word_label_list, dtype='int32')
test_Q_list = numpy.asarray(test_Q_list, dtype='int32')
test_para_list = numpy.asarray(test_para_list, dtype='int32')
test_Q_mask = numpy.asarray(test_Q_mask, dtype=theano.config.floatX)
test_para_mask = numpy.asarray(test_para_mask, dtype=theano.config.floatX)
test_Q_char_list = numpy.asarray(test_Q_char_list, dtype='int32')
test_para_char_list = numpy.asarray(test_para_char_list, dtype='int32')
test_Q_char_mask = numpy.asarray(test_Q_char_mask,
dtype=theano.config.floatX)
test_para_char_mask = numpy.asarray(test_para_char_mask,
dtype=theano.config.floatX)
test_para_extras = numpy.asarray(test_para_extras,
dtype=theano.config.floatX)
vocab_size = len(word2id)
print 'vocab size: ', vocab_size
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX, rng)
rand_values[0] = numpy.array(numpy.zeros(emb_size),
dtype=theano.config.floatX)
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_glove()
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings = theano.shared(value=rand_values, borrow=True)
char_size = len(char2id)
print 'char size: ', char_size
char_rand_values = random_value_normal((char_size + 1, char_emb_size),
theano.config.floatX, rng)
char_embeddings = theano.shared(value=char_rand_values, borrow=True)
extra_rand_values = random_value_normal((extra_size, extra_emb),
theano.config.floatX, rng)
extra_embeddings = theano.shared(value=extra_rand_values, borrow=True)
distance_rand_values = random_value_normal(
(2 * distance + 1, distance_emb), theano.config.floatX, rng)
distance_embeddings = theano.shared(value=distance_rand_values,
borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
paragraph = T.imatrix('paragraph')
questions = T.imatrix('questions')
span_indices = T.ivector() #batch
word_indices = T.imatrix() #(batch, 2)
ans_indices = T.ivector() # for one batch, the length is dynamic
para_mask = T.fmatrix('para_mask')
q_mask = T.fmatrix('q_mask')
extra = T.ftensor3() #(batch, p_len, 3)
char_paragraph = T.imatrix() #(batch, char_len*p_len)
char_questions = T.imatrix()
char_para_mask = T.fmatrix()
char_q_mask = T.fmatrix()
true_p_len = T.iscalar()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
true_batch_size = paragraph.shape[0]
extra_rep_batch = T.concatenate(
[extra.dot(extra_embeddings), extra],
axis=2) #(batch, p_len, extra_emb+extra_size)
zero_pad = T.zeros((true_batch_size, 1, extra_emb + extra_size))
left_context = T.concatenate([zero_pad, extra_rep_batch[:, :-1, :]],
axis=1) #(batch, p_len, extra_emb+extra_size)
right_context = T.concatenate(
[extra_rep_batch[:, 1:, :], zero_pad],
axis=1) #(batch, p_len, extra_emb+extra_size)
left_context_2 = T.concatenate(
[zero_pad, zero_pad, extra_rep_batch[:, :-2, :]],
axis=1) #(batch, p_len, extra_emb+extra_size)
right_context_2 = T.concatenate(
[extra_rep_batch[:, 2:, :], zero_pad, zero_pad],
axis=1) #(batch, p_len, extra_emb+extra_size)
simi2left = T.sum(extra_rep_batch * left_context,
axis=2).dimshuffle(0, 1, 'x') #(batch, p_len, 1)
simi2right = T.sum(extra_rep_batch * right_context,
axis=2).dimshuffle(0, 1, 'x') #(batch, p_len, 1)
cos2left = cosine_tensor3(extra_rep_batch, left_context,
2).dimshuffle(0, 1, 'x')
cos2right = cosine_tensor3(extra_rep_batch, right_context,
2).dimshuffle(0, 1, 'x')
diff2left = extra_rep_batch - left_context
diff2right = extra_rep_batch - right_context #(batch, p_len, extra_emb+extra_size)
extra_rep_batch = T.concatenate(
[
extra_rep_batch, left_context, right_context, left_context_2,
right_context_2, diff2left, diff2right, simi2left, simi2right,
cos2left, cos2right
],
axis=2) #batch, p_len, 7*(extra_emb+extra_size)+4)
true_extra_size = 7 * (extra_emb + extra_size) + 4
common_input_p = embeddings[paragraph.flatten()].reshape(
(true_batch_size, true_p_len,
emb_size)) #the input format can be adapted into CNN or GRU or LSTM
common_input_q = embeddings[questions.flatten()].reshape(
(true_batch_size, q_len_limit, emb_size))
char_common_input_p = char_embeddings[char_paragraph.flatten()].reshape(
(true_batch_size * true_p_len, char_len, char_emb_size
)) #the input format can be adapted into CNN or GRU or LSTM
char_common_input_q = char_embeddings[char_questions.flatten()].reshape(
(true_batch_size * q_len_limit, char_len, char_emb_size))
char_p_masks = char_para_mask.reshape(
(true_batch_size * true_p_len, char_len))
char_q_masks = char_q_mask.reshape(
(true_batch_size * q_len_limit, char_len))
conv_W_char, conv_b_char = create_conv_para(
rng, filter_shape=(char_emb_size, 1, char_emb_size, char_filter_size))
conv_W_1, conv_b_1 = create_conv_para(
rng,
filter_shape=(hidden_size, 1,
emb_size + char_emb_size + true_extra_size,
filter_size[0]))
conv_W_2, conv_b_2 = create_conv_para(rng,
filter_shape=(hidden_size, 1,
hidden_size,
filter_size[1]))
conv_W_3, conv_b_3 = create_conv_para(rng,
filter_shape=(hidden_size, 1,
hidden_size,
filter_size[2]))
# conv_W_4, conv_b_4=create_conv_para(rng, filter_shape=(hidden_size, 1, hidden_size, filter_size[3]))
# conv_W_5, conv_b_5=create_conv_para(rng, filter_shape=(hidden_size, 1, hidden_size, filter_size[4]))
conv_W_1_q, conv_b_1_q = create_conv_para(
rng,
filter_shape=(hidden_size, 1, emb_size + char_emb_size,
filter_size[0]))
conv_W_2_q, conv_b_2_q = create_conv_para(rng,
filter_shape=(hidden_size, 1,
hidden_size,
filter_size[1]))
conv_W_3_q, conv_b_3_q = create_conv_para(rng,
filter_shape=(hidden_size, 1,
hidden_size,
filter_size[2]))
# conv_W_4_q, conv_b_4_q=create_conv_para(rng, filter_shape=(hidden_size, 1, hidden_size, filter_size[3]))
# conv_W_5_q, conv_b_5_q=create_conv_para(rng, filter_shape=(hidden_size, 1, hidden_size, filter_size[4]))
CNN_para = [
conv_W_1,
conv_b_1,
conv_W_2,
conv_b_2,
conv_W_1_q,
conv_b_1_q,
conv_W_2_q,
conv_b_2_q,
conv_W_3,
conv_b_3,
conv_W_3_q,
conv_b_3_q,
# conv_W_4, conv_b_4, conv_W_5, conv_b_5,conv_W_4_q, conv_b_4_q, conv_W_5_q, conv_b_5_q,
conv_W_char,
conv_b_char
]
span_input4score, word_input4score, overall_span_hidden_size, overall_word_hidden_size = squad_cnn_rank_spans_word(
rng,
common_input_p,
common_input_q,
char_common_input_p,
char_common_input_q,
batch_size,
p_len_limit,
q_len_limit,
emb_size,
char_emb_size,
char_len,
filter_size,
char_filter_size,
hidden_size,
conv_W_1,
conv_b_1,
conv_W_2,
conv_b_2,
conv_W_1_q,
conv_b_1_q,
conv_W_2_q,
conv_b_2_q,
conv_W_char,
conv_b_char,
conv_W_3,
conv_b_3,
conv_W_3_q,
conv_b_3_q,
# conv_W_4, conv_b_4, conv_W_4_q, conv_b_4_q,
# conv_W_5, conv_b_5, conv_W_5_q, conv_b_5_q,
para_mask,
q_mask,
char_p_masks,
char_q_masks,
extra_rep_batch,
true_extra_size)
test_span_input4score, test_word_input4score, _, _ = squad_cnn_rank_spans_word(
rng,
common_input_p,
common_input_q,
char_common_input_p,
char_common_input_q,
test_batch_size,
test_p_len_limit,
q_len_limit,
emb_size,
char_emb_size,
char_len,
filter_size,
char_filter_size,
hidden_size,
conv_W_1,
conv_b_1,
conv_W_2,
conv_b_2,
conv_W_1_q,
conv_b_1_q,
conv_W_2_q,
conv_b_2_q,
conv_W_char,
conv_b_char,
conv_W_3,
conv_b_3,
conv_W_3_q,
conv_b_3_q,
# conv_W_4, conv_b_4, conv_W_4_q, conv_b_4_q,
# conv_W_5, conv_b_5, conv_W_5_q, conv_b_5_q,
para_mask,
q_mask,
char_p_masks,
char_q_masks,
extra_rep_batch,
true_extra_size) #(batch, hidden, gram_size)
gram_size = 5 * true_p_len - (0 + 1 + 2 + 3 + 4)
# U_a = create_ensemble_para(rng, 1, 4*hidden_size)
# norm_U_a=normalize_matrix(U_a)
# span_scores_matrix=T.dot(span_input4score.dimshuffle(0,2,1), norm_U_a).reshape((batch_size, gram_size)) #(batch, 13*para_len-78, 1)
span_HL_1_para = create_ensemble_para(rng, hidden_size,
overall_span_hidden_size)
span_HL_2_para = create_ensemble_para(rng, hidden_size, hidden_size)
span_HL_3_para = create_ensemble_para(rng, hidden_size, hidden_size)
span_HL_4_para = create_ensemble_para(rng, hidden_size, hidden_size)
span_U_a = create_ensemble_para(rng, 1,
hidden_size + overall_span_hidden_size)
norm_span_U_a = normalize_matrix(span_U_a)
norm_span_HL_1_para = normalize_matrix(span_HL_1_para)
norm_span_HL_2_para = normalize_matrix(span_HL_2_para)
norm_span_HL_3_para = normalize_matrix(span_HL_3_para)
norm_span_HL_4_para = normalize_matrix(span_HL_4_para)
span_scores_matrix = add_HLs_2_tensor3(span_input4score,
norm_span_HL_1_para,
norm_span_HL_2_para,
norm_span_HL_3_para,
norm_span_HL_4_para, norm_span_U_a,
batch_size, gram_size)
span_scores = T.nnet.softmax(span_scores_matrix) #(batch, 7*para_len-21)
loss_neg_likelihood = -T.mean(
T.log(span_scores[T.arange(batch_size), span_indices]))
#ranking loss
tanh_span_scores_matrix = span_scores #T.tanh(span_scores_matrix) #(batch, gram_size)
index_matrix = T.zeros((batch_size, gram_size), dtype=theano.config.floatX)
new_index_matrix = T.set_subtensor(
index_matrix[T.arange(batch_size), span_indices], 1.0)
prob_batch_posi = tanh_span_scores_matrix[new_index_matrix.nonzero()]
prob_batch_nega = tanh_span_scores_matrix[(1.0 -
new_index_matrix).nonzero()]
repeat_posi = T.extra_ops.repeat(prob_batch_posi,
prob_batch_nega.shape[0],
axis=0)
repeat_nega = T.extra_ops.repeat(prob_batch_nega.dimshuffle('x', 0),
prob_batch_posi.shape[0],
axis=0).flatten()
loss_rank = T.mean(T.maximum(0.0, margin - repeat_posi + repeat_nega))
span_loss = loss_neg_likelihood + loss_rank
# test_span_scores_matrix=T.dot(test_span_input4score.dimshuffle(0,2,1), norm_U_a).reshape((true_batch_size, gram_size)) #(batch, 13*para_len-78)
test_span_scores_matrix = add_HLs_2_tensor3(
test_span_input4score, norm_span_HL_1_para, norm_span_HL_2_para,
norm_span_HL_3_para, norm_span_HL_4_para, norm_span_U_a,
true_batch_size, gram_size)
#word
HL_1_para = create_ensemble_para(rng, hidden_size,
overall_word_hidden_size)
HL_2_para = create_ensemble_para(rng, hidden_size, hidden_size)
HL_3_para = create_ensemble_para(rng, hidden_size, hidden_size)
HL_4_para = create_ensemble_para(rng, hidden_size, hidden_size)
start_U_a = create_ensemble_para(rng, 1,
hidden_size + overall_word_hidden_size)
norm_start_U_a = normalize_matrix(start_U_a)
norm_HL_1_para = normalize_matrix(HL_1_para)
norm_HL_2_para = normalize_matrix(HL_2_para)
norm_HL_3_para = normalize_matrix(HL_3_para)
norm_HL_4_para = normalize_matrix(HL_4_para)
end_HL_1_para = create_ensemble_para(
rng, hidden_size, overall_word_hidden_size + distance_emb)
end_HL_2_para = create_ensemble_para(rng, hidden_size, hidden_size)
end_HL_3_para = create_ensemble_para(rng, hidden_size, hidden_size)
end_HL_4_para = create_ensemble_para(rng, hidden_size, hidden_size)
end_U_a = create_ensemble_para(
rng, 1, hidden_size + overall_word_hidden_size + distance_emb)
end_norm_U_a = normalize_matrix(end_U_a)
end_norm_HL_1_para = normalize_matrix(end_HL_1_para)
end_norm_HL_2_para = normalize_matrix(end_HL_2_para)
end_norm_HL_3_para = normalize_matrix(end_HL_3_para)
end_norm_HL_4_para = normalize_matrix(end_HL_4_para)
start_scores_matrix = add_HLs_2_tensor3(word_input4score, norm_HL_1_para,
norm_HL_2_para, norm_HL_3_para,
norm_HL_4_para, norm_start_U_a,
batch_size, true_p_len)
start_scores = T.nnet.softmax(start_scores_matrix) #(batch, para_len)
'''
forward start info to end prediction
'''
distance_matrix = word_indices[:, 0].dimshuffle(
0, 'x') - T.arange(true_p_len).dimshuffle('x', 0) #(batch, p_len)
distance_trunc_matrix = T.maximum(
-distance, T.minimum(distance,
distance_matrix)) + distance #(batch, p_len)
zero_distance_matrix = T.zeros(
(true_batch_size * true_p_len, 2 * distance + 1))
filled_distance_matrix = T.set_subtensor(
zero_distance_matrix[T.arange(true_batch_size * true_p_len),
distance_trunc_matrix.flatten()], 1.0)
filled_distance_tensor3 = filled_distance_matrix.reshape(
(true_batch_size,
true_p_len, 2 * distance + 1)).dot(distance_embeddings).dimshuffle(
0, 2, 1) #(batch_size, distance_emb, p_len)
end_word_input4score = T.concatenate(
[word_input4score, filled_distance_tensor3],
axis=1) #(batch, +distance_emb, p_len)
end_scores_matrix = add_HLs_2_tensor3(end_word_input4score,
end_norm_HL_1_para,
end_norm_HL_2_para,
end_norm_HL_3_para,
end_norm_HL_4_para, end_norm_U_a,
batch_size, true_p_len)
end_scores = T.nnet.softmax(end_scores_matrix) #(batch, para_len)
start_loss_neg_likelihood = -T.mean(
T.log(start_scores[T.arange(batch_size), word_indices[:, 0]]))
end_loss_neg_likelihood = -T.mean(
T.log(end_scores[T.arange(batch_size), word_indices[:, 1]]))
#ranking loss start
tanh_start_scores_matrix = start_scores #T.tanh(span_scores_matrix) #(batch, gram_size)
start_index_matrix = T.zeros((batch_size, p_len_limit),
dtype=theano.config.floatX)
start_new_index_matrix = T.set_subtensor(
start_index_matrix[T.arange(batch_size), word_indices[:, 0]], 1.0)
start_prob_batch_posi = tanh_start_scores_matrix[
start_new_index_matrix.nonzero()]
start_prob_batch_nega = tanh_start_scores_matrix[(
1.0 - start_new_index_matrix).nonzero()]
start_repeat_posi = T.extra_ops.repeat(start_prob_batch_posi,
start_prob_batch_nega.shape[0],
axis=0)
start_repeat_nega = T.extra_ops.repeat(start_prob_batch_nega.dimshuffle(
'x', 0),
start_prob_batch_posi.shape[0],
axis=0).flatten()
start_loss_rank = T.mean(
T.maximum(0.0, margin - start_repeat_posi + start_repeat_nega))
#ranking loss END
end_tanh_scores_matrix = end_scores #T.tanh(span_scores_matrix) #(batch, gram_size)
end_index_matrix = T.zeros((batch_size, p_len_limit),
dtype=theano.config.floatX)
end_new_index_matrix = T.set_subtensor(
end_index_matrix[T.arange(batch_size), word_indices[:, 1]], 1.0)
end_prob_batch_posi = end_tanh_scores_matrix[
end_new_index_matrix.nonzero()]
end_prob_batch_nega = end_tanh_scores_matrix[(
1.0 - end_new_index_matrix).nonzero()]
end_repeat_posi = T.extra_ops.repeat(end_prob_batch_posi,
end_prob_batch_nega.shape[0],
axis=0)
end_repeat_nega = T.extra_ops.repeat(end_prob_batch_nega.dimshuffle(
'x', 0),
end_prob_batch_posi.shape[0],
axis=0).flatten()
end_loss_rank = T.mean(
T.maximum(0.0, margin - end_repeat_posi + end_repeat_nega))
word_loss = start_loss_neg_likelihood + end_loss_neg_likelihood + start_loss_rank + end_loss_rank
#test
test_start_scores_matrix = add_HLs_2_tensor3(
test_word_input4score, norm_HL_1_para, norm_HL_2_para, norm_HL_3_para,
norm_HL_4_para, norm_start_U_a, true_batch_size,
true_p_len) #(batch, test_p_len)
mask_test_start_return = test_start_scores_matrix * para_mask #(batch, p_len)
'''
forward start info to end prediction in testing
'''
test_distance_matrix = T.argmax(mask_test_start_return, axis=1).dimshuffle(
0, 'x') - T.arange(true_p_len).dimshuffle('x', 0) #(batch, p_len)
test_distance_trunc_matrix = T.maximum(
-distance, T.minimum(distance,
test_distance_matrix)) + distance #(batch, p_len)
test_zero_distance_matrix = T.zeros(
(true_batch_size * true_p_len, 2 * distance + 1))
test_filled_distance_matrix = T.set_subtensor(
test_zero_distance_matrix[T.arange(true_batch_size * true_p_len),
test_distance_trunc_matrix.flatten()], 1.0)
test_filled_distance_tensor3 = test_filled_distance_matrix.reshape(
(true_batch_size,
true_p_len, 2 * distance + 1)).dot(distance_embeddings).dimshuffle(
0, 2, 1) #(batch_size, distance_emb, p_len)
test_end_word_input4score = T.concatenate(
[test_word_input4score, test_filled_distance_tensor3],
axis=1) #(batch, +distance-emb, p_len)
end_test_scores_matrix = add_HLs_2_tensor3(
test_end_word_input4score, end_norm_HL_1_para, end_norm_HL_2_para,
end_norm_HL_3_para, end_norm_HL_4_para, end_norm_U_a, true_batch_size,
true_p_len) #(batch, test_p_len)
end_mask_test_return = end_test_scores_matrix * para_mask #(batch, p_len)
word_gram_1 = mask_test_start_return + end_mask_test_return
word_gram_2 = mask_test_start_return[:, :
-1] + end_mask_test_return[:,
1:] #(batch* hidden_size, maxsenlen-1)
word_gram_3 = mask_test_start_return[:, :
-2] + end_mask_test_return[:,
2:] #(batch* hidden_size, maxsenlen-2)
word_gram_4 = mask_test_start_return[:, :
-3] + end_mask_test_return[:,
3:] #(batch* hidden_size, maxsenlen-3)
word_gram_5 = mask_test_start_return[:, :
-4] + end_mask_test_return[:,
4:] #(batch* hidden_size, maxsenlen-4)
word_pair_scores = T.concatenate(
[word_gram_1, word_gram_2, word_gram_3, word_gram_4, word_gram_5],
axis=1) #(batch_size, gram_size)
#ans words train
ans_HL_1_para = create_ensemble_para(rng, hidden_size,
overall_word_hidden_size)
ans_HL_2_para = create_ensemble_para(rng, hidden_size, hidden_size)
ans_HL_3_para = create_ensemble_para(rng, hidden_size, hidden_size)
ans_HL_4_para = create_ensemble_para(rng, hidden_size, hidden_size)
ans_U_a = create_ensemble_para(rng, 1,
hidden_size + overall_word_hidden_size)
norm_ans_U_a = normalize_matrix(ans_U_a)
norm_ans_HL_1_para = normalize_matrix(ans_HL_1_para)
norm_ans_HL_2_para = normalize_matrix(ans_HL_2_para)
norm_ans_HL_3_para = normalize_matrix(ans_HL_3_para)
norm_ans_HL_4_para = normalize_matrix(ans_HL_4_para)
ans_scores_matrix = add_HLs_2_tensor3(word_input4score, norm_ans_HL_1_para,
norm_ans_HL_2_para,
norm_ans_HL_3_para,
norm_ans_HL_4_para, norm_ans_U_a,
batch_size, true_p_len)
ans_scores_vec = T.nnet.softmax(
ans_scores_matrix).flatten() #(batch, para_len)
ans_loss_neg_likelihood = -T.mean(T.log(ans_scores_vec[ans_indices]))
ans_index_vec = T.zeros((batch_size, p_len_limit),
dtype=theano.config.floatX).flatten()
ans_new_index = T.set_subtensor(ans_index_vec[ans_indices], 1.0)
ans_prob_batch_posi = ans_scores_vec[ans_new_index.nonzero()]
ans_prob_batch_nega = ans_scores_vec[(1.0 - ans_new_index).nonzero()]
ans_repeat_posi = T.extra_ops.repeat(ans_prob_batch_posi,
ans_prob_batch_nega.shape[0],
axis=0)
ans_repeat_nega = T.extra_ops.repeat(ans_prob_batch_nega.dimshuffle(
'x', 0),
ans_prob_batch_posi.shape[0],
axis=0).flatten()
ans_loss_rank = T.mean(
T.maximum(0.0, margin - ans_repeat_posi + ans_repeat_nega))
ans_loss = ans_loss_neg_likelihood + ans_loss_rank
#ans words test
test_ans_scores_matrix = add_HLs_2_tensor3(
test_word_input4score, norm_ans_HL_1_para, norm_ans_HL_2_para,
norm_ans_HL_3_para, norm_ans_HL_4_para, norm_ans_U_a, true_batch_size,
true_p_len)
test_ans_scores_matrix = test_ans_scores_matrix * para_mask #T.nnet.softmax(test_ans_scores_matrix) #(batch, para_len)
ans_gram_1 = test_ans_scores_matrix
ans_gram_2 = (test_ans_scores_matrix[:, :-1] +
test_ans_scores_matrix[:, 1:]
) / 2.0 #(batch* hidden_size, maxsenlen-1)
ans_gram_3 = (test_ans_scores_matrix[:, :-2] +
test_ans_scores_matrix[:, 1:-1] +
test_ans_scores_matrix[:, 2:]
) / 3.0 #(batch* hidden_size, maxsenlen-2)
ans_gram_4 = (
test_ans_scores_matrix[:, :-3] + test_ans_scores_matrix[:, 1:-2] +
test_ans_scores_matrix[:, 2:-1] + test_ans_scores_matrix[:, 3:]
) / 4.0 #(batch* hidden_size, maxsenlen-3)
ans_gram_5 = (
test_ans_scores_matrix[:, :-4] + test_ans_scores_matrix[:, 1:-3] +
test_ans_scores_matrix[:, 2:-2] + test_ans_scores_matrix[:, 3:-1] +
test_ans_scores_matrix[:,
4:]) / 5.0 #(batch* hidden_size, maxsenlen-4)
ans_word_scores = T.concatenate(
[ans_gram_1, ans_gram_2, ans_gram_3, ans_gram_4, ans_gram_5],
axis=1) #(batch, hidden_size, maxsenlen-(0+1+2+3+4))
'''
form test spans and masks
'''
test_span_word_scores_matrix = word_pair_scores + ans_word_scores #test_span_scores_matrix+
test_spans_mask_1 = para_mask
test_spans_mask_2 = para_mask[:, :
-1] * para_mask[:,
1:] #(batch* hidden_size, maxsenlen-1)
test_spans_mask_3 = para_mask[:, :
-2] * para_mask[:, 1:
-1] * para_mask[:,
2:] #(batch* hidden_size, maxsenlen-2)
test_spans_mask_4 = para_mask[:, :
-3] * para_mask[:, 1:
-2] * para_mask[:, 2:
-1] * para_mask[:,
3:] #(batch* hidden_size, maxsenlen-3)
test_spans_mask_5 = para_mask[:, :
-4] * para_mask[:, 1:
-3] * para_mask[:, 2:
-2] * para_mask[:,
3:
-1] * para_mask[:,
4:]
test_spans_mask = T.concatenate([
test_spans_mask_1, test_spans_mask_2, test_spans_mask_3,
test_spans_mask_4, test_spans_mask_5
],
axis=1) #(batch, 5*p_len -)
# test_return=T.argmax(test_span_word_scores_matrix, axis=1) #batch T.argmax(test_span_word_scores_matrix*test_spans_mask, axis=1) #batch
test_return = T.argmax(test_span_word_scores_matrix * test_spans_mask,
axis=1) #batch
# params = [embeddings,char_embeddings]+NN_para+[U_a]
params = (
[embeddings, char_embeddings, extra_embeddings, distance_embeddings] +
CNN_para
# +[span_U_a,span_HL_1_para,span_HL_2_para,span_HL_3_para,span_HL_4_para]
+ [start_U_a, HL_1_para, HL_2_para, HL_3_para, HL_4_para] +
[end_U_a, end_HL_1_para, end_HL_2_para, end_HL_3_para, end_HL_4_para] +
[ans_U_a, ans_HL_1_para, ans_HL_2_para, ans_HL_3_para, ans_HL_4_para])
L2_reg = L2norm_paraList([
embeddings,
char_embeddings,
extra_embeddings,
distance_embeddings,
conv_W_1,
conv_W_2,
conv_W_1_q,
conv_W_2_q,
conv_W_char,
conv_W_3,
conv_W_3_q,
# conv_W_4, conv_W_5,conv_W_4_q, conv_W_5_q,
# span_U_a,span_HL_1_para,span_HL_2_para,span_HL_3_para,span_HL_4_para,
start_U_a,
HL_1_para,
HL_2_para,
HL_3_para,
HL_4_para,
end_U_a,
end_HL_1_para,
end_HL_2_para,
end_HL_3_para,
end_HL_4_para,
ans_U_a,
ans_HL_1_para,
ans_HL_2_para,
ans_HL_3_para,
ans_HL_4_para
])
#L2_reg = L2norm_paraList(params)
cost = word_loss + ans_loss + L2_weight * L2_reg #span_loss+
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i /
(T.sqrt(acc) + 1e-8))) #AdaGrad
updates.append((acc_i, acc))
# updates=Adam(cost, params, lr=0.0001)
train_model = theano.function([
paragraph, questions, span_indices, word_indices, ans_indices,
para_mask, q_mask, extra, char_paragraph, char_questions,
char_para_mask, char_q_mask, true_p_len
],
cost,
updates=updates,
on_unused_input='ignore')
test_model = theano.function([
paragraph, questions, para_mask, q_mask, extra, char_paragraph,
char_questions, char_para_mask, char_q_mask, true_p_len
],
test_return,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches = train_size / batch_size
# remain_train=train_size%batch_size
train_batch_start = list(numpy.arange(n_train_batches) *
batch_size) + [train_size - batch_size]
n_test_batches = test_size / test_batch_size
# remain_test=test_size%batch_size
test_batch_start = list(numpy.arange(n_test_batches) *
test_batch_size) + [test_size - test_batch_size]
max_F1_acc = 0.0
max_exact_acc = 0.0
cost_i = 0.0
train_ids = range(train_size)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.Random(200).shuffle(train_ids)
iter_accu = 0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
train_id_batch = train_ids[para_id:para_id + batch_size]
boundary_labels_batch = train_word_label_list[train_id_batch]
ans_label_list = []
for i in range(batch_size):
start = boundary_labels_batch[i][0] + i * p_len_limit
end = boundary_labels_batch[i][1] + i * p_len_limit
ans_label_list += range(start, end + 1)
ans_label_list = numpy.asarray(ans_label_list, dtype='int32')
cost_i += train_model(
train_para_list[train_id_batch], train_Q_list[train_id_batch],
train_span_label_list[train_id_batch], boundary_labels_batch,
ans_label_list, train_para_mask[train_id_batch],
train_Q_mask[train_id_batch],
train_para_extras[train_id_batch],
train_para_char_list[train_id_batch],
train_Q_char_list[train_id_batch],
train_para_char_mask[train_id_batch],
train_Q_char_mask[train_id_batch], p_len_limit)
#print iter
if iter % 100 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
print 'Testing...'
past_time = time.time()
pred_dict = {}
q_amount = 0
for test_para_id in test_batch_start:
batch_predict_ids = test_model(
test_para_list[test_para_id:test_para_id +
test_batch_size],
test_Q_list[test_para_id:test_para_id +
test_batch_size],
test_para_mask[test_para_id:test_para_id +
test_batch_size],
test_Q_mask[test_para_id:test_para_id +
test_batch_size],
test_para_extras[test_para_id:test_para_id +
test_batch_size],
test_para_char_list[test_para_id:test_para_id +
test_batch_size],
test_Q_char_list[test_para_id:test_para_id +
test_batch_size],
test_para_char_mask[test_para_id:test_para_id +
test_batch_size],
test_Q_char_mask[test_para_id:test_para_id +
test_batch_size], test_p_len_limit)
test_para_wordlist_batch = test_para_wordlist_list[
test_para_id:test_para_id + test_batch_size]
q_ids_batch = q_idlist[test_para_id:test_para_id +
test_batch_size]
q_amount += test_batch_size
for q in range(test_batch_size): #for each question
pred_ans = decode_predict_id(
batch_predict_ids[q], test_para_wordlist_batch[q])
q_id = q_ids_batch[q]
pred_dict[q_id] = pred_ans
# print q_id, test_para_wordlist_batch[q],'\t',pred_ans
with codecs.open(rootPath + 'predictions.txt', 'w',
'utf-8') as outfile:
json.dump(pred_dict, outfile)
F1_acc, exact_acc = standard_eval(rootPath + 'dev-v1.1.json',
rootPath + 'predictions.txt')
if F1_acc > max_F1_acc:
max_F1_acc = F1_acc
if exact_acc > max_exact_acc:
max_exact_acc = exact_acc
# if max_exact_acc > max_EM:
# store_model_to_file(rootPath+'Best_Paras_google_'+str(max_exact_acc), params)
# print 'Finished storing best params at:', max_exact_acc
print 'current average F1:', F1_acc, '\t\tmax F1:', max_F1_acc, 'current exact:', exact_acc, '\t\tmax exact_acc:', max_exact_acc
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
return max_exact_acc
def evaluate_lenet5(learning_rate=0.02,
n_epochs=100,
emb_size=300,
batch_size=70,
filter_size=[3, 1],
maxSentLen=70,
hidden_size=[300, 300]):
model_options = locals().copy()
print "model options", model_options
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
srng = T.shared_randomstreams.RandomStreams(rng.randint(seed))
"load raw data"
all_sentences_l, all_masks_l, all_sentences_r, all_masks_r, all_labels, word2id = load_SNLI_dataset(
maxlen=maxSentLen
) #minlen, include one label, at least one word in the sentence
train_sents_l = np.asarray(all_sentences_l[0], dtype='int32')
dev_sents_l = np.asarray(all_sentences_l[1], dtype='int32')
test_sents_l = np.asarray(all_sentences_l[2], dtype='int32')
train_masks_l = np.asarray(all_masks_l[0], dtype=theano.config.floatX)
dev_masks_l = np.asarray(all_masks_l[1], dtype=theano.config.floatX)
test_masks_l = np.asarray(all_masks_l[2], dtype=theano.config.floatX)
train_sents_r = np.asarray(all_sentences_r[0], dtype='int32')
dev_sents_r = np.asarray(all_sentences_r[1], dtype='int32')
test_sents_r = np.asarray(all_sentences_r[2], dtype='int32')
train_masks_r = np.asarray(all_masks_r[0], dtype=theano.config.floatX)
dev_masks_r = np.asarray(all_masks_r[1], dtype=theano.config.floatX)
test_masks_r = np.asarray(all_masks_r[2], dtype=theano.config.floatX)
train_labels_store = np.asarray(all_labels[0], dtype='int32')
dev_labels_store = np.asarray(all_labels[1], dtype='int32')
test_labels_store = np.asarray(all_labels[2], dtype='int32')
train_size = len(train_labels_store)
dev_size = len(dev_labels_store)
test_size = len(test_labels_store)
print 'train size: ', train_size, ' dev size: ', dev_size, ' test size: ', test_size
vocab_size = len(word2id) + 1
"first randomly initialize each word in the matrix 'rand_values', then load pre-trained word2vec embeddinds to initialize words, uncovered"
"words keep random initialization"
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_word2vec()
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
init_embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
"now, start to build the input form of the model"
sents_ids_l = T.imatrix()
sents_mask_l = T.fmatrix()
sents_ids_r = T.imatrix()
sents_mask_r = T.fmatrix()
labels = T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
'Use word ids in sentences to retrieve word embeddings from matrix "init_embeddings", each sentence will be in'
'tensor2 (emb_size, sen_length), then the minibatch will be in tensor3 (batch_size, emb_size, sen_length) '
embed_input_l = init_embeddings[sents_ids_l.flatten(
)].reshape((batch_size, maxSentLen, emb_size)).dimshuffle(
0, 2, 1
) #embed_input(init_embeddings, sents_ids_l)#embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
embed_input_r = init_embeddings[sents_ids_r.flatten()].reshape(
(batch_size, maxSentLen, emb_size)).dimshuffle(0, 2, 1)
'''create parameters for attentive convolution function '''
gate_filter_shape = (emb_size, 1, emb_size, 1)
conv_W_pre, conv_b_pre = create_conv_para(rng,
filter_shape=gate_filter_shape)
conv_W_gate, conv_b_gate = create_conv_para(rng,
filter_shape=gate_filter_shape)
conv_W, conv_b = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size, filter_size[0]))
conv_W_context, conv_b_context = create_conv_para(
rng, filter_shape=(hidden_size[0], 1, emb_size, 1))
conv_W2, conv_b2 = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size,
filter_size[1]))
conv_W2_context, conv_b2_context = create_conv_para(
rng, filter_shape=(hidden_size[0], 1, emb_size, 1))
NN_para = [
conv_W, conv_b, conv_W_context, conv_W_pre, conv_b_pre, conv_W_gate,
conv_b_gate, conv_W2, conv_b2, conv_W2_context
]
"A gated convolution layer to form more expressive word representations in each sentence"
"input tensor3 (batch_size, emb_size, sen_length), output tensor3 (batch_size, emb_size, sen_length)"
conv_layer_gate_l = Conv_with_Mask_with_Gate(
rng,
input_tensor3=embed_input_l,
mask_matrix=sents_mask_l,
image_shape=(batch_size, 1, emb_size, maxSentLen),
filter_shape=gate_filter_shape,
W=conv_W_pre,
b=conv_b_pre,
W_gate=conv_W_gate,
b_gate=conv_b_gate)
conv_layer_gate_r = Conv_with_Mask_with_Gate(
rng,
input_tensor3=embed_input_r,
mask_matrix=sents_mask_r,
image_shape=(batch_size, 1, emb_size, maxSentLen),
filter_shape=gate_filter_shape,
W=conv_W_pre,
b=conv_b_pre,
W_gate=conv_W_gate,
b_gate=conv_b_gate)
'''
attentive convolution function, two sizes of filter_width 3&1 are used. Multi-channel
'''
attentive_conv_layer = Attentive_Conv_for_Pair(
rng,
origin_input_tensor3=embed_input_l,
origin_input_tensor3_r=embed_input_r,
input_tensor3=conv_layer_gate_l.output_tensor3,
input_tensor3_r=conv_layer_gate_r.output_tensor3,
mask_matrix=sents_mask_l,
mask_matrix_r=sents_mask_r,
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[0], 1, emb_size, 1),
W=conv_W,
b=conv_b,
W_context=conv_W_context,
b_context=conv_b_context)
attentive_sent_embeddings_l = attentive_conv_layer.attentive_maxpool_vec_l
attentive_sent_embeddings_r = attentive_conv_layer.attentive_maxpool_vec_r
attentive_conv_layer2 = Attentive_Conv_for_Pair(
rng,
origin_input_tensor3=embed_input_l,
origin_input_tensor3_r=embed_input_r,
input_tensor3=conv_layer_gate_l.output_tensor3,
input_tensor3_r=conv_layer_gate_r.output_tensor3,
mask_matrix=sents_mask_l,
mask_matrix_r=sents_mask_r,
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[1]),
filter_shape_context=(hidden_size[0], 1, emb_size, 1),
W=conv_W2,
b=conv_b2,
W_context=conv_W2_context,
b_context=conv_b2_context)
attentive_sent_embeddings_l2 = attentive_conv_layer2.attentive_maxpool_vec_l
attentive_sent_embeddings_r2 = attentive_conv_layer2.attentive_maxpool_vec_r
"Batch normalization for the four output sentence representation vectors"
gamma = theano.shared(np.asarray(rng.uniform(
low=-1.0 / math.sqrt(hidden_size[0]),
high=1.0 / math.sqrt(hidden_size[0]),
size=(hidden_size[0])),
dtype=theano.config.floatX),
borrow=True)
beta = theano.shared(np.zeros((hidden_size[0]),
dtype=theano.config.floatX),
borrow=True)
bn_params = [gamma, beta]
bn_attentive_sent_embeddings_l = batch_normalization(
inputs=attentive_sent_embeddings_l,
gamma=gamma,
beta=beta,
mean=attentive_sent_embeddings_l.mean((0, ), keepdims=True),
std=attentive_sent_embeddings_l.std((0, ), keepdims=True),
mode='low_mem')
bn_attentive_sent_embeddings_r = batch_normalization(
inputs=attentive_sent_embeddings_r,
gamma=gamma,
beta=beta,
mean=attentive_sent_embeddings_r.mean((0, ), keepdims=True),
std=attentive_sent_embeddings_r.std((0, ), keepdims=True),
mode='low_mem')
bn_attentive_sent_embeddings_l2 = batch_normalization(
inputs=attentive_sent_embeddings_l2,
gamma=gamma,
beta=beta,
mean=attentive_sent_embeddings_l2.mean((0, ), keepdims=True),
std=attentive_sent_embeddings_l2.std((0, ), keepdims=True),
mode='low_mem')
bn_attentive_sent_embeddings_r2 = batch_normalization(
inputs=attentive_sent_embeddings_r2,
gamma=gamma,
beta=beta,
mean=attentive_sent_embeddings_r2.mean((0, ), keepdims=True),
std=attentive_sent_embeddings_r2.std((0, ), keepdims=True),
mode='low_mem')
"Before logistic regression layer, we insert a hidden layer. Now form input to HL classifier"
HL_layer_1_input = T.concatenate([
bn_attentive_sent_embeddings_l, bn_attentive_sent_embeddings_r,
bn_attentive_sent_embeddings_l + bn_attentive_sent_embeddings_r,
bn_attentive_sent_embeddings_l * bn_attentive_sent_embeddings_r,
bn_attentive_sent_embeddings_l2, bn_attentive_sent_embeddings_r2,
bn_attentive_sent_embeddings_l2 + bn_attentive_sent_embeddings_r2,
bn_attentive_sent_embeddings_l2 * bn_attentive_sent_embeddings_r2
],
axis=1)
HL_layer_1_input_size = 8 * hidden_size[0]
"Create hidden layer parameters"
HL_layer_1_W, HL_layer_1_b = create_HiddenLayer_para(
rng, HL_layer_1_input_size, hidden_size[1])
HL_layer_1_params = [HL_layer_1_W, HL_layer_1_b]
"Hidden Layer and batch norm to its output again"
HL_layer_1 = HiddenLayer(rng,
input=HL_layer_1_input,
n_in=HL_layer_1_input_size,
n_out=hidden_size[1],
W=HL_layer_1_W,
b=HL_layer_1_b,
activation=T.tanh)
gamma_HL = theano.shared(np.asarray(rng.uniform(
low=-1.0 / math.sqrt(hidden_size[1]),
high=1.0 / math.sqrt(hidden_size[1]),
size=(hidden_size[1])),
dtype=theano.config.floatX),
borrow=True)
beta_HL = theano.shared(np.zeros((hidden_size[1]),
dtype=theano.config.floatX),
borrow=True)
bn_params_HL = [gamma_HL, beta_HL]
bn_HL_output = batch_normalization(inputs=HL_layer_1.output,
gamma=gamma_HL,
beta=beta_HL,
mean=HL_layer_1.output.mean(
(0, ), keepdims=True),
std=HL_layer_1.output.std(
(0, ), keepdims=True),
mode='low_mem')
"Form input to LR classifier"
LR_input = T.concatenate([HL_layer_1_input, bn_HL_output], axis=1)
LR_input_size = HL_layer_1_input_size + hidden_size[1]
U_a = create_ensemble_para(rng, 3, LR_input_size) # (input_size, 3)
LR_b = theano.shared(value=np.zeros((3, ), dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
LR_para = [U_a, LR_b]
"Logistic Regression layer"
layer_LR = LogisticRegression(
rng,
input=normalize_matrix_col_wise(LR_input),
n_in=LR_input_size,
n_out=3,
W=U_a,
b=LR_b
) #basically it is a multiplication between weight matrix and input feature vector
loss = layer_LR.negative_log_likelihood(
labels
) #for classification task, we usually used negative log likelihood as loss, the lower the better.
params = [
init_embeddings
] + NN_para + LR_para + bn_params + HL_layer_1_params + bn_params_HL
cost = loss
"Use AdaGrad to update parameters"
updates = Gradient_Cost_Para(cost, params, learning_rate)
train_model = theano.function(
[sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels],
cost,
updates=updates,
allow_input_downcast=True,
on_unused_input='ignore')
dev_model = theano.function(
[sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels],
layer_LR.errors(labels),
allow_input_downcast=True,
on_unused_input='ignore')
test_model = theano.function(
[sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels],
layer_LR.errors(labels),
allow_input_downcast=True,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
n_train_batches = train_size / batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
n_dev_batches = dev_size / batch_size
dev_batch_start = list(
np.arange(n_dev_batches) * batch_size) + [dev_size - batch_size]
n_test_batches = test_size / batch_size
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
max_acc_dev = 0.0
max_acc_test = 0.0
cost_i = 0.0
train_indices = range(train_size)
while epoch < n_epochs:
epoch = epoch + 1
random.Random(100).shuffle(
train_indices
) #shuffle training set for each new epoch, is supposed to promote performance, but not garrenteed
iter_accu = 0
for batch_id in train_batch_start: #for each batch
# iter means how many batches have been run, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
train_id_batch = train_indices[batch_id:batch_id + batch_size]
cost_i += train_model(train_sents_l[train_id_batch],
train_masks_l[train_id_batch],
train_sents_r[train_id_batch],
train_masks_r[train_id_batch],
train_labels_store[train_id_batch])
if (epoch == 1 and iter % 1000 == 0) or (epoch >= 2
and iter % 5 == 0):
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
past_time = time.time()
dev_error_sum = 0.0
for dev_batch_id in dev_batch_start: # for each test batch
dev_error_i = dev_model(
dev_sents_l[dev_batch_id:dev_batch_id + batch_size],
dev_masks_l[dev_batch_id:dev_batch_id + batch_size],
dev_sents_r[dev_batch_id:dev_batch_id + batch_size],
dev_masks_r[dev_batch_id:dev_batch_id + batch_size],
dev_labels_store[dev_batch_id:dev_batch_id +
batch_size])
dev_error_sum += dev_error_i
dev_acc = 1.0 - dev_error_sum / (len(dev_batch_start))
if dev_acc > max_acc_dev:
max_acc_dev = dev_acc
print '\tcurrent dev_acc:', dev_acc, ' ; ', '\tmax_dev_acc:', max_acc_dev
'''
best dev model, test
'''
error_sum = 0.0
for test_batch_id in test_batch_start: # for each test batch
error_i = test_model(
test_sents_l[test_batch_id:test_batch_id +
batch_size],
test_masks_l[test_batch_id:test_batch_id +
batch_size],
test_sents_r[test_batch_id:test_batch_id +
batch_size],
test_masks_r[test_batch_id:test_batch_id +
batch_size],
test_labels_store[test_batch_id:test_batch_id +
batch_size])
error_sum += error_i
test_acc = 1.0 - error_sum / (len(test_batch_start))
if test_acc > max_acc_test:
max_acc_test = test_acc
print '\t\tcurrent test_acc:', test_acc, ' ; ', '\t\t\t\t\tmax_test_acc:', max_acc_test
else:
print '\tcurrent dev_acc:', dev_acc, ' ; ', '\tmax_dev_acc:', max_acc_dev
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
return max_acc_test
def evaluate_lenet5(learning_rate=0.01, n_epochs=2000, batch_size=100, emb_size=300, char_emb_size=20, hidden_size=300,
L2_weight=0.0001, p_len_limit=400, test_p_len_limit=100, q_len_limit=20, char_len=15, filter_size = [5,5],
char_filter_size=3, margin=2.0, max_EM=50.302743615):
test_batch_size=batch_size*10
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/SQuAD/';
rng = numpy.random.RandomState(23455)
word2id={}
char2id={}
#questions,paragraphs,q_masks,p_masks,labels, word2id
train_Q_list,train_para_list, train_Q_mask, train_para_mask, train_Q_char_list,train_para_char_list, train_Q_char_mask, train_para_char_mask, train_label_list, word2id, char2id=load_squad_cnn_rank_word_train(word2id, char2id, p_len_limit, q_len_limit, char_len)
train_size=len(train_para_list)
test_Q_list, test_para_list, test_Q_mask, test_para_mask,test_Q_char_list, test_para_char_list, test_Q_char_mask, test_para_char_mask, test_label_list, q_idlist, word2id, char2id, test_para_wordlist_list= load_squad_cnn_rank_word_dev(word2id, char2id, test_p_len_limit, q_len_limit, char_len)
test_size=len(test_para_list)
train_Q_list = numpy.asarray(train_Q_list, dtype='int32')
train_para_list = numpy.asarray(train_para_list, dtype='int32')
train_Q_mask = numpy.asarray(train_Q_mask, dtype=theano.config.floatX)
train_para_mask = numpy.asarray(train_para_mask, dtype=theano.config.floatX)
train_Q_char_list = numpy.asarray(train_Q_char_list, dtype='int32')
train_para_char_list = numpy.asarray(train_para_char_list, dtype='int32')
train_Q_char_mask = numpy.asarray(train_Q_char_mask, dtype=theano.config.floatX)
train_para_char_mask = numpy.asarray(train_para_char_mask, dtype=theano.config.floatX)
train_label_list = numpy.asarray(train_label_list, dtype='int32')
test_Q_list = numpy.asarray(test_Q_list, dtype='int32')
test_para_list = numpy.asarray(test_para_list, dtype='int32')
test_Q_mask = numpy.asarray(test_Q_mask, dtype=theano.config.floatX)
test_para_mask = numpy.asarray(test_para_mask, dtype=theano.config.floatX)
test_Q_char_list = numpy.asarray(test_Q_char_list, dtype='int32')
test_para_char_list = numpy.asarray(test_para_char_list, dtype='int32')
test_Q_char_mask = numpy.asarray(test_Q_char_mask, dtype=theano.config.floatX)
test_para_char_mask = numpy.asarray(test_para_char_mask, dtype=theano.config.floatX)
vocab_size = len(word2id)
print 'vocab size: ', vocab_size
rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, rng)
rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
id2word = {y:x for x,y in word2id.iteritems()}
word2vec=load_glove()
rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings=theano.shared(value=rand_values, borrow=True)
char_size = len(char2id)
print 'char size: ', char_size
char_rand_values=random_value_normal((char_size+1, char_emb_size), theano.config.floatX, rng)
char_embeddings=theano.shared(value=char_rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
paragraph = T.imatrix('paragraph')
questions = T.imatrix('questions')
gold_indices= T.imatrix() #batch, (start, end) for each sample
para_mask=T.fmatrix('para_mask')
q_mask=T.fmatrix('q_mask')
char_paragraph = T.imatrix() #(batch, char_len*p_len)
char_questions = T.imatrix()
char_para_mask=T.fmatrix()
char_q_mask=T.fmatrix()
true_p_len = T.iscalar()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
true_batch_size = paragraph.shape[0]
common_input_p=embeddings[paragraph.flatten()].reshape((true_batch_size,true_p_len, emb_size)) #the input format can be adapted into CNN or GRU or LSTM
common_input_q=embeddings[questions.flatten()].reshape((true_batch_size,q_len_limit, emb_size))
char_common_input_p=char_embeddings[char_paragraph.flatten()].reshape((true_batch_size*true_p_len, char_len, char_emb_size)) #the input format can be adapted into CNN or GRU or LSTM
char_common_input_q=char_embeddings[char_questions.flatten()].reshape((true_batch_size*q_len_limit, char_len, char_emb_size))
char_p_masks = char_para_mask.reshape((true_batch_size*true_p_len, char_len))
char_q_masks = char_q_mask.reshape((true_batch_size*q_len_limit, char_len))
conv_W_char, conv_b_char=create_conv_para(rng, filter_shape=(char_emb_size, 1, char_emb_size, char_filter_size))
conv_W_1, conv_b_1=create_conv_para(rng, filter_shape=(hidden_size, 1, emb_size+char_emb_size, filter_size[0]))
conv_W_2, conv_b_2=create_conv_para(rng, filter_shape=(hidden_size, 1, hidden_size, filter_size[1]))
conv_W_1_q, conv_b_1_q=create_conv_para(rng, filter_shape=(hidden_size, 1, emb_size+char_emb_size, filter_size[0]))
conv_W_2_q, conv_b_2_q=create_conv_para(rng, filter_shape=(hidden_size, 1, hidden_size, filter_size[1]))
NN_para=[conv_W_1, conv_b_1,conv_W_2, conv_b_2,conv_W_1_q, conv_b_1_q, conv_W_2_q, conv_b_2_q, conv_W_char, conv_b_char]
input4score = squad_cnn_rank_word(rng, common_input_p, common_input_q, char_common_input_p, char_common_input_q,batch_size, p_len_limit,q_len_limit,
emb_size, char_emb_size,char_len,filter_size,char_filter_size,hidden_size,
conv_W_1, conv_b_1,conv_W_2, conv_b_2,conv_W_1_q, conv_b_1_q, conv_W_2_q, conv_b_2_q,conv_W_char,conv_b_char,
para_mask, q_mask, char_p_masks,char_q_masks)
test_input4score = squad_cnn_rank_word(rng, common_input_p, common_input_q, char_common_input_p, char_common_input_q,test_batch_size, test_p_len_limit,q_len_limit,
emb_size, char_emb_size,char_len,filter_size,char_filter_size,hidden_size,
conv_W_1, conv_b_1,conv_W_2, conv_b_2, conv_W_1_q, conv_b_1_q, conv_W_2_q, conv_b_2_q,conv_W_char,conv_b_char,
para_mask, q_mask, char_p_masks,char_q_masks) #(batch, hidden, #(batch, 2*hidden, p_len_limit))
# gram_size = 5*true_p_len-(0+1+2+3+4)
HL_1_para = create_ensemble_para(rng, hidden_size, 2*hidden_size)
HL_2_para = create_ensemble_para(rng, hidden_size, hidden_size)
HL_3_para = create_ensemble_para(rng, hidden_size, hidden_size)
HL_4_para = create_ensemble_para(rng, hidden_size, hidden_size)
U_a = create_ensemble_para(rng, 1, hidden_size)
norm_U_a=normalize_matrix(U_a)
norm_HL_1_para=normalize_matrix(HL_1_para)
norm_HL_2_para=normalize_matrix(HL_2_para)
norm_HL_3_para=normalize_matrix(HL_3_para)
norm_HL_4_para=normalize_matrix(HL_4_para)
end_HL_1_para = create_ensemble_para(rng, hidden_size, 2*hidden_size)
end_HL_2_para = create_ensemble_para(rng, hidden_size, hidden_size)
end_HL_3_para = create_ensemble_para(rng, hidden_size, hidden_size)
end_HL_4_para = create_ensemble_para(rng, hidden_size, hidden_size)
end_U_a = create_ensemble_para(rng, 1, hidden_size)
end_norm_U_a=normalize_matrix(end_U_a)
end_norm_HL_1_para=normalize_matrix(end_HL_1_para)
end_norm_HL_2_para=normalize_matrix(end_HL_2_para)
end_norm_HL_3_para=normalize_matrix(end_HL_3_para)
end_norm_HL_4_para=normalize_matrix(end_HL_4_para)
span_scores_matrix = add_HLs_2_tensor3(input4score, norm_HL_1_para,norm_HL_2_para,norm_HL_3_para,norm_HL_4_para, norm_U_a, batch_size,true_p_len)
span_scores=T.nnet.softmax(span_scores_matrix) #(batch, para_len)
end_span_scores_matrix = add_HLs_2_tensor3(input4score, end_norm_HL_1_para,end_norm_HL_2_para,end_norm_HL_3_para,end_norm_HL_4_para, end_norm_U_a, batch_size,true_p_len)
end_span_scores=T.nnet.softmax(end_span_scores_matrix) #(batch, para_len)
loss_neg_likelihood=-T.mean(T.log(span_scores[T.arange(batch_size), gold_indices[:,0]]))
end_loss_neg_likelihood=-T.mean(T.log(span_scores[T.arange(batch_size), gold_indices[:,1]]))
#ranking loss start
tanh_span_scores_matrix = span_scores#T.tanh(span_scores_matrix) #(batch, gram_size)
index_matrix = T.zeros((batch_size, p_len_limit), dtype=theano.config.floatX)
new_index_matrix = T.set_subtensor(index_matrix[T.arange(batch_size), gold_indices[:,0]], 1.0)
prob_batch_posi = tanh_span_scores_matrix[new_index_matrix.nonzero()]
prob_batch_nega = tanh_span_scores_matrix[(1.0-new_index_matrix).nonzero()]
repeat_posi = T.extra_ops.repeat(prob_batch_posi, prob_batch_nega.shape[0], axis=0)
repeat_nega = T.extra_ops.repeat(prob_batch_nega.dimshuffle('x',0), prob_batch_posi.shape[0], axis=0).flatten()
loss_rank = T.mean(T.maximum(0.0, margin-repeat_posi+repeat_nega))
#ranking loss END
end_tanh_span_scores_matrix = end_span_scores#T.tanh(span_scores_matrix) #(batch, gram_size)
end_index_matrix = T.zeros((batch_size, p_len_limit), dtype=theano.config.floatX)
end_new_index_matrix = T.set_subtensor(end_index_matrix[T.arange(batch_size), gold_indices[:,1]], 1.0)
end_prob_batch_posi = end_tanh_span_scores_matrix[end_new_index_matrix.nonzero()]
end_prob_batch_nega = end_tanh_span_scores_matrix[(1.0-end_new_index_matrix).nonzero()]
end_repeat_posi = T.extra_ops.repeat(end_prob_batch_posi, end_prob_batch_nega.shape[0], axis=0)
end_repeat_nega = T.extra_ops.repeat(end_prob_batch_nega.dimshuffle('x',0), end_prob_batch_posi.shape[0], axis=0).flatten()
end_loss_rank = T.mean(T.maximum(0.0, margin-end_repeat_posi+end_repeat_nega))
loss = loss_neg_likelihood +end_loss_neg_likelihood+loss_rank+end_loss_rank
#test
test_span_scores_matrix = add_HLs_2_tensor3(test_input4score, norm_HL_1_para,norm_HL_2_para,norm_HL_3_para,norm_HL_4_para,norm_U_a, true_batch_size,true_p_len) #(batch, test_p_len)
mask_test_return=T.argmax(test_span_scores_matrix*para_mask, axis=1) #batch
end_test_span_scores_matrix = add_HLs_2_tensor3(test_input4score, end_norm_HL_1_para,end_norm_HL_2_para,end_norm_HL_3_para,end_norm_HL_4_para,end_norm_U_a, true_batch_size,true_p_len) #(batch, test_p_len)
end_mask_test_return=T.argmax(end_test_span_scores_matrix*para_mask, axis=1) #batch
params = [embeddings,char_embeddings]+NN_para+[U_a,HL_1_para,HL_2_para,HL_3_para,HL_4_para]+[end_U_a,end_HL_1_para,end_HL_2_para,end_HL_3_para,end_HL_4_para]
L2_reg =L2norm_paraList([embeddings,char_embeddings,conv_W_1,conv_W_2,conv_W_1_q, conv_W_2_q, conv_W_char,U_a,HL_1_para,HL_2_para,HL_3_para,HL_4_para])
#L2_reg = L2norm_paraList(params)
cost=loss#+L2_weight*L2_reg
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / (T.sqrt(acc)+1e-8))) #AdaGrad
updates.append((acc_i, acc))
# updates=Adam(cost, params, lr=0.0001)
train_model = theano.function([paragraph, questions,gold_indices, para_mask, q_mask, char_paragraph, #(batch, char_len*p_len)
char_questions, char_para_mask, char_q_mask, true_p_len], cost, updates=updates,on_unused_input='ignore')
test_model = theano.function([paragraph, questions,para_mask, q_mask,
char_paragraph,
char_questions,
char_para_mask,
char_q_mask,
true_p_len], [mask_test_return,end_mask_test_return], on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches=train_size/batch_size
# remain_train=train_size%batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)+[train_size-batch_size]
n_test_batches=test_size/test_batch_size
# remain_test=test_size%batch_size
test_batch_start=list(numpy.arange(n_test_batches)*test_batch_size)+[test_size-test_batch_size]
max_F1_acc=0.0
max_exact_acc=0.0
cost_i=0.0
train_ids = range(train_size)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.shuffle(train_ids)
iter_accu=0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
iter_accu+=1
train_id_batch = train_ids[para_id:para_id+batch_size]
cost_i+= train_model(
train_para_list[train_id_batch],
train_Q_list[train_id_batch],
train_label_list[train_id_batch],
train_para_mask[train_id_batch],
train_Q_mask[train_id_batch],
train_para_char_list[train_id_batch],
train_Q_char_list[train_id_batch],
train_para_char_mask[train_id_batch],
train_Q_char_mask[train_id_batch],
p_len_limit)
#print iter
if iter%100==0:
print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
print 'Testing...'
past_time = time.time()
pred_dict={}
q_amount=0
p1=0
for test_para_id in test_batch_start:
batch_predict_ids, batch_predict_end_ids=test_model(
test_para_list[test_para_id:test_para_id+test_batch_size],
test_Q_list[test_para_id:test_para_id+test_batch_size],
test_para_mask[test_para_id:test_para_id+test_batch_size],
test_Q_mask[test_para_id:test_para_id+test_batch_size],
test_para_char_list[test_para_id:test_para_id+test_batch_size],
test_Q_char_list[test_para_id:test_para_id+test_batch_size],
test_para_char_mask[test_para_id:test_para_id+test_batch_size],
test_Q_char_mask[test_para_id:test_para_id+test_batch_size],
test_p_len_limit)
test_para_wordlist_batch=test_para_wordlist_list[test_para_id:test_para_id+test_batch_size]
# test_label_batch=test_label_list[test_para_id:test_para_id+test_batch_size]
# q_amount+=test_batch_size
q_ids_batch=q_idlist[test_para_id:test_para_id+test_batch_size]
q_amount+=test_batch_size
for q in range(test_batch_size): #for each question
# pred_ans=decode_predict_id(batch_predict_ids[q], test_para_wordlist_batch[q])
start = batch_predict_ids[q]
end = batch_predict_end_ids[q]
if end < start:
start, end = end, start
pred_ans = ' '.join(test_para_wordlist_batch[q][start:end+1])
q_id=q_ids_batch[q]
pred_dict[q_id]=pred_ans
with codecs.open(rootPath+'predictions.txt', 'w', 'utf-8') as outfile:
json.dump(pred_dict, outfile)
F1_acc, exact_acc = standard_eval(rootPath+'dev-v1.1.json', rootPath+'predictions.txt')
if F1_acc> max_F1_acc:
max_F1_acc=F1_acc
if exact_acc> max_exact_acc:
max_exact_acc=exact_acc
# if max_exact_acc > max_EM:
# store_model_to_file(rootPath+'Best_Paras_google_'+str(max_exact_acc), params)
# print 'Finished storing best params at:', max_exact_acc
print 'current average F1:', F1_acc, '\t\tmax F1:', max_F1_acc, 'current exact:', exact_acc, '\t\tmax exact_acc:', max_exact_acc
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.02,
n_epochs=100,
emb_size=300,
batch_size=50,
filter_size=[3],
sent_len=40,
claim_len=40,
cand_size=10,
hidden_size=[300, 300],
max_pred_pick=5):
model_options = locals().copy()
print "model options", model_options
pred_id2label = {1: 'SUPPORTS', 0: 'REFUTES', 2: 'NOT ENOUGH INFO'}
root = '/save/wenpeng/datasets/FEVER/'
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
srng = T.shared_randomstreams.RandomStreams(rng.randint(seed))
"load raw data"
vocabfile = codecs.open(root + 'word2id.txt', 'r', 'utf-8')
word2id = json.loads(vocabfile.read())
# co=0
# for line in vocabfile:
# word2id = json.loads(line)
# co+=1
# print 'co: ', co
# word2id = json.load(open(root+'word2id.json')) #json.loads(vocabfile)
vocabfile.close()
print 'load word2id over'
# train_sents, train_sent_masks, train_sent_labels, train_claims, train_claim_mask, train_labels, word2id = load_fever_train(sent_len, claim_len, cand_size)
# train_3th_sents, train_3th_sent_masks, train_3th_sent_labels, train_3th_claims, train_3th_claim_mask, train_3th_labels, word2id = load_fever_train_NoEnoughInfo(sent_len, claim_len, cand_size, word2id)
all_sentences_l, all_masks_l, all_sentences_r, all_masks_r, all_labels, _ = load_SciTailV1_dataset(
sent_len, word2id)
# all_sentences_l, all_masks_l, all_sentences_r, all_masks_r, all_labels, _ = load_RTE_dataset_as_test(sent_len, word2id)
# dev_sents_l=np.asarray(all_sentences_l[1], dtype='int32')
test_sents_l = np.asarray(all_sentences_l[2], dtype='int32')
# dev_masks_l=np.asarray(all_masks_l[1], dtype=theano.config.floatX)
test_masks_l = np.asarray(all_masks_l[2], dtype=theano.config.floatX)
# dev_sents_r=np.asarray(all_sentences_r[1] , dtype='int32')
test_sents_r = np.asarray(all_sentences_r[2], dtype='int32')
# dev_masks_r=np.asarray(all_masks_r[1], dtype=theano.config.floatX)
test_masks_r = np.asarray(all_masks_r[2], dtype=theano.config.floatX)
# dev_labels_store=np.asarray(all_labels[1], dtype='int32')
test_labels_store = np.asarray(all_labels[2], dtype='int32')
# dev_size=len(dev_labels_store)
test_size = len(test_labels_store)
vocab_size = len(word2id) + 1
print 'vocab size: ', vocab_size
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
# id2word = {y:x for x,y in word2id.iteritems()}
# word2vec=load_word2vec()
# rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
init_embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
"now, start to build the input form of the model"
sents_ids = T.imatrix() #(batch, cand_size, sent_len)
sents_mask = T.fmatrix()
# sents_labels=T.imatrix() #(batch, cand_size)
# claim_ids = T.imatrix() #(batch, claim_len)
# claim_mask = T.fmatrix()
# joint_sents_ids=T.itensor3() #(batch, cand_size, sent_len)
# joint_sents_mask=T.ftensor3()
# joint_sents_labels=T.imatrix() #(batch, cand_size)
claim_ids = T.imatrix() #(batch, claim_len)
claim_mask = T.fmatrix()
labels = T.ivector()
# test_premise_ids = T.imatrix()
# test_premise_matrix = T.fmatrix()
# test_hypo_ids = T.imatrix()
# test_hypo_matrix = T.fmatrix()
# test_scitail_minibatch_labels = T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
embed_input_sents = init_embeddings[sents_ids.flatten(
)].reshape((batch_size, sent_len, emb_size)).dimshuffle(
0, 2, 1
) #embed_input(init_embeddings, sents_ids_l)#embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
embed_input_claim = init_embeddings[claim_ids.flatten()].reshape(
(batch_size, sent_len, emb_size)).dimshuffle(0, 2, 1)
conv_W, conv_b = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size, filter_size[0]))
# task1_att_conv_W, task1_att_conv_b=create_conv_para(rng, filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]))
# task1_conv_W_context, task1_conv_b_context=create_conv_para(rng, filter_shape=(hidden_size[0], 1, emb_size, 1))
att_conv_W, att_conv_b = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size,
filter_size[0]))
conv_W_context, conv_b_context = create_conv_para(
rng, filter_shape=(hidden_size[0], 1, emb_size, 1))
NN_para = [
conv_W, conv_b, att_conv_W, att_conv_b, conv_W_context, conv_b_context
]
'''
training task2, predict 3 labels
'''
joint_embed_input_sents = init_embeddings[sents_ids.flatten(
)].reshape((batch_size, sent_len, emb_size)).dimshuffle(
0, 2, 1
) #embed_input(init_embeddings, sents_ids_l)#embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
joint_embed_input_claim = init_embeddings[claim_ids.flatten()].reshape(
(batch_size, sent_len, emb_size)).dimshuffle(0, 2, 1)
joint_conv_model_sents = Conv_with_Mask(
rng,
input_tensor3=joint_embed_input_sents,
mask_matrix=sents_mask,
image_shape=(batch_size, 1, emb_size, sent_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
W=conv_W,
b=conv_b
) #mutiple mask with the conv_out to set the features by UNK to zero
joint_premise_emb = joint_conv_model_sents.maxpool_vec #(batch_size*cand_size, hidden_size) # each sentence then have an embedding of length hidden_size
# joint_batch_sent_emb = joint_sent_embeddings.reshape((batch_size, cand_size, hidden_size[0]))
# joint_premise_emb = T.sum(joint_batch_sent_emb*joint_sents_labels.dimshuffle(0,1,'x'), axis=1) #(batch, hidden_size)
joint_conv_model_claims = Conv_with_Mask(
rng,
input_tensor3=joint_embed_input_claim,
mask_matrix=claim_mask,
image_shape=(batch_size, 1, emb_size, claim_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
W=conv_W,
b=conv_b
) #mutiple mask with the conv_out to set the features by UNK to zero
joint_claim_embeddings = joint_conv_model_claims.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
joint_premise_hypo_emb = T.concatenate(
[joint_premise_emb, joint_claim_embeddings],
axis=1) #(batch, 2*hidden_size)
'''
attentive conv in task2
'''
# joint_sents_tensor3 = joint_embed_input_sents.dimshuffle(0,2,1).reshape((batch_size, cand_size*sent_len, emb_size))
# joint_sents_dot = T.batched_dot(joint_sents_tensor3, joint_sents_tensor3.dimshuffle(0,2,1)) #(batch_size, cand_size*sent_len, cand_size*sent_len)
# joint_sents_dot_2_matrix = T.nnet.softmax(joint_sents_dot.reshape((batch_size*cand_size*sent_len, cand_size*sent_len)))
# joint_sents_context = T.batched_dot(joint_sents_dot_2_matrix.reshape((batch_size, cand_size*sent_len, cand_size*sent_len)), joint_sents_tensor3) #(batch_size, cand_size*sent_len, emb_size)
# joint_add_sents_context = joint_embed_input_sents+joint_sents_context.reshape((batch_size*cand_size, sent_len, emb_size)).dimshuffle(0,2,1)#T.concatenate([joint_embed_input_sents, joint_sents_context.reshape((batch_size*cand_size, sent_len, emb_size)).dimshuffle(0,2,1)], axis=1) #(batch_size*cand_size, 2*emb_size, sent_len)
attentive_conv_layer = Attentive_Conv_for_Pair_easy_version(
rng,
input_tensor3=
joint_embed_input_sents, #batch_size*cand_size, 2*emb_size, sent_len
input_tensor3_r=joint_embed_input_claim,
mask_matrix=sents_mask,
mask_matrix_r=claim_mask,
image_shape=(batch_size, 1, emb_size, sent_len),
image_shape_r=(batch_size, 1, emb_size, sent_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[0], 1, emb_size, 1),
W=att_conv_W,
b=att_conv_b,
W_context=conv_W_context,
b_context=conv_b_context)
attentive_sent_embeddings_l = attentive_conv_layer.attentive_maxpool_vec_l #(batch_size*cand_size, hidden_size)
attentive_sent_embeddings_r = attentive_conv_layer.attentive_maxpool_vec_r
"Logistic Regression layer"
joint_LR_input = T.concatenate([
joint_premise_hypo_emb, attentive_sent_embeddings_l,
attentive_sent_embeddings_r
],
axis=1)
joint_LR_input_size = 2 * hidden_size[0] + 2 * hidden_size[0]
joint_U_a = create_ensemble_para(rng, 3,
joint_LR_input_size) # (input_size, 3)
joint_LR_b = theano.shared(value=np.zeros((3, ),
dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
joint_LR_para = [joint_U_a, joint_LR_b]
joint_layer_LR = LogisticRegression(
rng,
input=joint_LR_input,
n_in=joint_LR_input_size,
n_out=3,
W=joint_U_a,
b=joint_LR_b
) #basically it is a multiplication between weight matrix and input feature vector
# joint_loss=joint_layer_LR.negative_log_likelihood(joint_labels) #for classification task, we usually used negative log likelihood as loss, the lower the better.
'''
testing
joint_sents_ids=T.itensor3() #(batch, cand_size, sent_len)
joint_sents_mask=T.ftensor3()
joint_sents_labels=T.imatrix() #(batch, cand_size)
joint_claim_ids = T.imatrix() #(batch, claim_len)
joint_claim_mask = T.fmatrix()
joint_labels=T.ivector()
'''
pred_minibatch_labels = joint_layer_LR.y_pred
pred_minibatch_labels_2_2classes = T.where(pred_minibatch_labels > 1, 0,
pred_minibatch_labels)
pred_minibatch_error = T.mean(
T.neq(pred_minibatch_labels_2_2classes, labels))
params = [init_embeddings] + NN_para + joint_LR_para
load_model_from_file(root + 'para_for_test_scitail', params)
# train_model = theano.function([sents_ids,sents_mask,sents_labels,claim_ids,claim_mask,joint_sents_ids,joint_sents_mask,joint_sents_labels, joint_claim_ids, joint_claim_mask, joint_labels], cost, updates=updates, allow_input_downcast=True, on_unused_input='ignore')
test_model = theano.function(
[sents_ids, sents_mask, claim_ids, claim_mask, labels],
pred_minibatch_error,
allow_input_downcast=True,
on_unused_input='ignore')
# dev_model = theano.function([joint_sents_ids,joint_sents_mask,joint_sents_labels, joint_claim_ids, joint_claim_mask, joint_labels], pred_minibatch_error, allow_input_downcast=True, on_unused_input='ignore')
# test_model = theano.function([sents_ids,sents_mask,sents_labels, claim_ids,claim_mask, joint_labels], [inter_matrix,test_layer_LR.errors(joint_labels), test_layer_LR.y_pred], allow_input_downcast=True, on_unused_input='ignore')
# dev_model = theano.function([sents_ids,sents_mask,sents_labels, claim_ids,claim_mask, joint_labels], [inter_matrix,test_layer_LR.errors(joint_labels), test_layer_LR.y_pred], allow_input_downcast=True, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... testing'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
# joint_n_train_batches=joint_train_size/batch_size
# joint_train_batch_start=list(np.arange(joint_n_train_batches)*batch_size)+[joint_train_size-batch_size]
# n_train_batches=train_size/batch_size
# train_batch_start=list(np.arange(n_train_batches)*batch_size)+[train_size-batch_size]
# n_dev_batches=dev_size/batch_size
# dev_batch_start=list(np.arange(n_dev_batches)*batch_size)+[dev_size-batch_size]
n_test_batches = test_size / batch_size
remain_test_batches = test_size % batch_size
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
max_acc_dev = 0.0
max_acc_test = 0.0
cost_i = 0.0
error_sum = 0.0
for idd, test_batch_id in enumerate(
test_batch_start): # for each test batch
error_i = test_model(
test_sents_l[test_batch_id:test_batch_id + batch_size],
test_masks_l[test_batch_id:test_batch_id + batch_size],
test_sents_r[test_batch_id:test_batch_id + batch_size],
test_masks_r[test_batch_id:test_batch_id + batch_size],
test_labels_store[test_batch_id:test_batch_id + batch_size])
error_sum += error_i
test_acc = 1.0 - error_sum / (len(test_batch_start))
print '\tcurrent test_acc:', test_acc
def evaluate_lenet5(learning_rate=0.02,
n_epochs=4,
L2_weight=1e-5,
extra_size=4,
emb_size=300,
batch_size=100,
filter_size=[3, 3],
maxSentLen=40,
hidden_size=[300, 300],
max_term_len=4,
p_mode='conc'):
model_options = locals().copy()
print "model options", model_options
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
all_sentences_l, all_masks_l, all_sentences_r, all_masks_r, all_word1, all_word2, all_word1_mask, all_word2_mask, all_labels, all_extra, word2id = load_wordnet_hyper_vs_all_with_words(
maxlen=maxSentLen, wordlen=max_term_len
) #minlen, include one label, at least one word in the sentence
# test_sents_l, test_masks_l, test_sents_r, test_masks_r, test_labels, word2id =load_ACE05_dataset(maxSentLen, word2id)
test_sents_l, test_masks_l, test_sents_r, test_masks_r, test_word1, test_word2, test_word1_mask, test_word2_mask, test_labels, test_extra, word2id = load_task_hyper_vs_all_with_words(
LenciBenotto_file, maxSentLen, word2id, wordlen=max_term_len)
store_word2id(word2id, root_dic + 'LenciBenotto_word2id.pkl')
# exit(0)
total_size = len(all_sentences_l)
hold_test_size = 10000
train_size = total_size - hold_test_size
train_sents_l = np.asarray(all_sentences_l[:train_size], dtype='int32')
# dev_sents_l=np.asarray(all_sentences_l[1], dtype='int32')
# test_sents_l=np.asarray(all_sentences_l[-test_size:], dtype='int32')
test_sents_l = np.asarray(test_sents_l, dtype='int32')
train_masks_l = np.asarray(all_masks_l[:train_size],
dtype=theano.config.floatX)
# dev_masks_l=np.asarray(all_masks_l[1], dtype=theano.config.floatX)
# test_masks_l=np.asarray(all_masks_l[-test_size:], dtype=theano.config.floatX)
test_masks_l = np.asarray(test_masks_l, dtype=theano.config.floatX)
train_sents_r = np.asarray(all_sentences_r[:train_size], dtype='int32')
# dev_sents_r=np.asarray(all_sentences_r[1] , dtype='int32')
# test_sents_r=np.asarray(all_sentences_r[-test_size:], dtype='int32')
test_sents_r = np.asarray(test_sents_r, dtype='int32')
train_masks_r = np.asarray(all_masks_r[:train_size],
dtype=theano.config.floatX)
# dev_masks_r=np.asarray(all_masks_r[1], dtype=theano.config.floatX)
# test_masks_r=np.asarray(all_masks_r[-test_size:], dtype=theano.config.floatX)
test_masks_r = np.asarray(test_masks_r, dtype=theano.config.floatX)
train_word1 = np.asarray(all_word1[:train_size], dtype='int32')
train_word2 = np.asarray(all_word2[:train_size], dtype='int32')
test_word1 = np.asarray(test_word1, dtype='int32')
test_word2 = np.asarray(test_word2, dtype='int32')
train_word1_mask = np.asarray(all_word1_mask[:train_size],
dtype=theano.config.floatX)
train_word2_mask = np.asarray(all_word2_mask[:train_size],
dtype=theano.config.floatX)
test_word1_mask = np.asarray(test_word1_mask, dtype=theano.config.floatX)
test_word2_mask = np.asarray(test_word2_mask, dtype=theano.config.floatX)
train_labels_store = np.asarray(all_labels[:train_size], dtype='int32')
# dev_labels_store=np.asarray(all_labels[1], dtype='int32')
# test_labels_store=np.asarray(all_labels[-test_size:], dtype='int32')
test_labels_store = np.asarray(test_labels, dtype='int32')
train_extra = np.asarray(all_extra[:train_size],
dtype=theano.config.floatX)
test_extra = np.asarray(test_extra, dtype=theano.config.floatX)
# train_size=len(train_labels_store)
# dev_size=len(dev_labels_store)
test_size = len(test_labels_store)
print 'train size: ', train_size, ' test size: ', test_size
vocab_size = len(word2id) + 1
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
#here, we leave code for loading word2vec to initialize words
rand_values[0] = np.array(np.zeros(emb_size), dtype=theano.config.floatX)
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_word2vec()
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
init_embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
store_model_to_file(root_dic + 'LenciBenotto_best_para_init_embeddings',
[init_embeddings])
#now, start to build the input form of the model
sents_ids_l = T.imatrix()
sents_mask_l = T.fmatrix()
sents_ids_r = T.imatrix()
sents_mask_r = T.fmatrix()
word1_ids = T.imatrix()
word2_ids = T.imatrix()
word1_mask = T.fmatrix()
word2_mask = T.fmatrix()
extra = T.fvector()
labels = T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
def embed_input(emb_matrix, sent_ids):
return emb_matrix[sent_ids.flatten()].reshape(
(batch_size, maxSentLen, emb_size)).dimshuffle(0, 2, 1)
embed_input_l = embed_input(
init_embeddings, sents_ids_l
) #embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
embed_input_r = embed_input(
init_embeddings, sents_ids_r
) #embeddings[sents_ids_r.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1)
embed_word1 = init_embeddings[word1_ids.flatten()].reshape(
(batch_size, word1_ids.shape[1], emb_size))
embed_word2 = init_embeddings[word2_ids.flatten()].reshape(
(batch_size, word2_ids.shape[1], emb_size))
word1_embedding = T.sum(embed_word1 * word1_mask.dimshuffle(0, 1, 'x'),
axis=1)
word2_embedding = T.sum(embed_word2 * word2_mask.dimshuffle(0, 1, 'x'),
axis=1)
'''create_AttentiveConv_params '''
conv_W, conv_b = create_conv_para(rng,
filter_shape=(hidden_size[1], 1,
emb_size, filter_size[0]))
conv_W_context, conv_b_context = create_conv_para(
rng, filter_shape=(hidden_size[1], 1, emb_size, 1))
NN_para = [conv_W, conv_b, conv_W_context]
'''
attentive convolution function
'''
term_vs_term_layer = Conv_for_Pair(
rng,
origin_input_tensor3=embed_word1.dimshuffle(0, 2, 1),
origin_input_tensor3_r=embed_word2.dimshuffle(0, 2, 1),
input_tensor3=embed_word1.dimshuffle(0, 2, 1),
input_tensor3_r=embed_word2.dimshuffle(0, 2, 1),
mask_matrix=word1_mask,
mask_matrix_r=word2_mask,
image_shape=(batch_size, 1, emb_size, max_term_len),
image_shape_r=(batch_size, 1, emb_size, max_term_len),
filter_shape=(hidden_size[1], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[1], 1, emb_size, 1),
W=conv_W,
b=conv_b,
W_context=conv_W_context,
b_context=conv_b_context)
tt_embeddings_l = term_vs_term_layer.attentive_maxpool_vec_l
tt_embeddings_r = term_vs_term_layer.attentive_maxpool_vec_r
p_ww = T.concatenate([
tt_embeddings_l, tt_embeddings_r, tt_embeddings_l * tt_embeddings_r,
tt_embeddings_l - tt_embeddings_r
],
axis=1)
term_vs_def_layer = Conv_for_Pair(
rng,
origin_input_tensor3=embed_word1.dimshuffle(0, 2, 1),
origin_input_tensor3_r=embed_input_r,
input_tensor3=embed_word1.dimshuffle(0, 2, 1),
input_tensor3_r=embed_input_r,
mask_matrix=word1_mask,
mask_matrix_r=sents_mask_r,
image_shape=(batch_size, 1, emb_size, max_term_len),
image_shape_r=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[1], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[1], 1, emb_size, 1),
W=conv_W,
b=conv_b,
W_context=conv_W_context,
b_context=conv_b_context)
td_embeddings_l = term_vs_def_layer.attentive_maxpool_vec_l
td_embeddings_r = term_vs_def_layer.attentive_maxpool_vec_r
p_wd = T.concatenate([
td_embeddings_l, td_embeddings_r, td_embeddings_l * td_embeddings_r,
td_embeddings_l - td_embeddings_r
],
axis=1)
def_vs_term_layer = Conv_for_Pair(
rng,
origin_input_tensor3=embed_input_l,
origin_input_tensor3_r=embed_word2.dimshuffle(0, 2, 1),
input_tensor3=embed_input_l,
input_tensor3_r=embed_word2.dimshuffle(0, 2, 1),
mask_matrix=sents_mask_l,
mask_matrix_r=word2_mask,
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r=(batch_size, 1, emb_size, max_term_len),
filter_shape=(hidden_size[1], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[1], 1, emb_size, 1),
W=conv_W,
b=conv_b,
W_context=conv_W_context,
b_context=conv_b_context)
dt_embeddings_l = def_vs_term_layer.attentive_maxpool_vec_l
dt_embeddings_r = def_vs_term_layer.attentive_maxpool_vec_r
p_dw = T.concatenate([
dt_embeddings_l, dt_embeddings_r, dt_embeddings_l * dt_embeddings_r,
dt_embeddings_l - dt_embeddings_r
],
axis=1)
def_vs_def_layer = Conv_for_Pair(
rng,
origin_input_tensor3=embed_input_l,
origin_input_tensor3_r=embed_input_r,
input_tensor3=embed_input_l,
input_tensor3_r=embed_input_r,
mask_matrix=sents_mask_l,
mask_matrix_r=sents_mask_r,
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[1], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[1], 1, emb_size, 1),
W=conv_W,
b=conv_b,
W_context=conv_W_context,
b_context=conv_b_context)
dd_embeddings_l = def_vs_def_layer.attentive_maxpool_vec_l
dd_embeddings_r = def_vs_def_layer.attentive_maxpool_vec_r
p_dd = T.concatenate([
dd_embeddings_l, dd_embeddings_r, dd_embeddings_l * dd_embeddings_r,
dd_embeddings_l - dd_embeddings_r
],
axis=1)
if p_mode == 'conc':
p = T.concatenate([p_ww, p_wd, p_dw, p_dd], axis=1)
p_len = 4 * 4 * hidden_size[1]
else:
p = T.max(T.concatenate([
p_ww.dimshuffle('x', 0, 1),
p_wd.dimshuffle('x', 0, 1),
p_dw.dimshuffle('x', 0, 1),
p_dd.dimshuffle('x', 0, 1)
],
axis=0),
axis=0)
p_len = 4 * hidden_size[1]
# HL_input = T.concatenate([p,cosine_matrix1_matrix2_rowwise(word1_embedding,word2_embedding).dimshuffle(0,'x'),extra.dimshuffle(0,'x')],axis=1)
# HL_input_size=p_len+1+1
#
# HL_layer_1=HiddenLayer(rng, input=HL_input, n_in=HL_input_size, n_out=hidden_size[1], activation=T.tanh)
"form input to LR classifier"
LR_input = T.concatenate([
p,
cosine_matrix1_matrix2_rowwise(word1_embedding,
word2_embedding).dimshuffle(0, 'x'),
extra.dimshuffle(0, 'x')
],
axis=1)
LR_input_size = p_len + 1 + 1
# LR_input = HL_layer_1.output
# LR_input_size = hidden_size[1]
U_a = create_ensemble_para(
rng, 2, LR_input_size) # the weight matrix hidden_size*2
LR_b = theano.shared(value=np.zeros((2, ), dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
LR_para = [U_a, LR_b]
layer_LR = LogisticRegression(
rng,
input=LR_input,
n_in=LR_input_size,
n_out=2,
W=U_a,
b=LR_b,
bias=0.25
) #basically it is a multiplication between weight matrix and input feature vector
loss = layer_LR.negative_log_likelihood(
labels
) #for classification task, we usually used negative log likelihood as loss, the lower the better.
# L2_reg = (conv_W**2).sum()+(conv_W_context**2).sum()+(U_a**2).sum()
params = NN_para + LR_para #[init_embeddings]
cost = loss #+L2_weight*L2_reg
updates = Gradient_Cost_Para(cost, params, learning_rate)
train_model = theano.function([
sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, word1_ids,
word2_ids, word1_mask, word2_mask, extra, labels
],
cost,
updates=updates,
allow_input_downcast=True,
on_unused_input='ignore')
test_model = theano.function([
sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, word1_ids,
word2_ids, word1_mask, word2_mask, extra, labels
], [layer_LR.errors(labels), layer_LR.y_pred, layer_LR.prop_for_posi],
allow_input_downcast=True,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
n_train_batches = train_size / batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
# n_dev_batches=dev_size/batch_size
# dev_batch_start=list(np.arange(n_dev_batches)*batch_size)+[dev_size-batch_size]
n_test_batches = test_size / batch_size
n_test_remain = test_size % batch_size
if n_test_remain != 0:
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
else:
test_batch_start = list(np.arange(n_test_batches) * batch_size)
# max_acc_dev=0.0
max_ap_test = 0.0
max_ap_topk_test = 0.0
max_f1 = 0.0
cost_i = 0.0
train_indices = range(train_size)
while epoch < n_epochs:
epoch = epoch + 1
random.Random(100).shuffle(
train_indices
) #shuffle training set for each new epoch, is supposed to promote performance, but not garrenteed
iter_accu = 0
for batch_id in train_batch_start: #for each batch
# iter means how many batches have been run, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
train_id_batch = train_indices[batch_id:batch_id + batch_size]
cost_i += train_model(
train_sents_l[train_id_batch], train_masks_l[train_id_batch],
train_sents_r[train_id_batch], train_masks_r[train_id_batch],
train_word1[train_id_batch], train_word2[train_id_batch],
train_word1_mask[train_id_batch],
train_word2_mask[train_id_batch], train_extra[train_id_batch],
train_labels_store[train_id_batch])
#after each 1000 batches, we test the performance of the model on all test data
if iter % 100 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
past_time = time.time()
pred_labels = []
probs = []
gold_labels = []
error_sum = 0.0
for idd, test_batch_id in enumerate(
test_batch_start): # for each test batch
error_i, pred_i, prob_i = test_model(
test_sents_l[test_batch_id:test_batch_id + batch_size],
test_masks_l[test_batch_id:test_batch_id + batch_size],
test_sents_r[test_batch_id:test_batch_id + batch_size],
test_masks_r[test_batch_id:test_batch_id + batch_size],
test_word1[test_batch_id:test_batch_id + batch_size],
test_word2[test_batch_id:test_batch_id + batch_size],
test_word1_mask[test_batch_id:test_batch_id +
batch_size],
test_word2_mask[test_batch_id:test_batch_id +
batch_size],
test_extra[test_batch_id:test_batch_id + batch_size],
test_labels_store[test_batch_id:test_batch_id +
batch_size])
error_sum += error_i
pred_labels += list(pred_i)
probs += list(prob_i)
if n_test_remain != 0:
probs = probs[:(len(test_batch_start) - 1) *
batch_size] + probs[-n_test_remain:]
assert len(test_labels) == len(probs)
# test_acc=1.0-error_sum/(len(test_batch_start))
test_ap = apk(test_labels, probs, k=len(test_labels))
test_ap_top100 = apk(test_labels, probs, k=100)
if test_ap > max_ap_test:
max_ap_test = test_ap
store_model_to_file(
root_dic + 'LenciBenotto_best_para_' +
str(max_ap_test), params)
if test_ap_top100 > max_ap_topk_test:
max_ap_topk_test = test_ap_top100
print '\t\tcurrent ap:', test_ap, ' ; ', '\t\tmax_ap: ', max_ap_test, 'ap@100: ', test_ap_top100, '\tmax_ap@100:', max_ap_topk_test
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.02,
n_epochs=100,
emb_size=300,
batch_size=70,
filter_size=[3],
maxSentLen=40,
hidden_size=[300, 300]):
model_options = locals().copy()
print "model options", model_options
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
srng = T.shared_randomstreams.RandomStreams(rng.randint(seed))
"load raw data"
all_sentences_l, all_masks_l, all_sentences_r, all_masks_r, all_labels, word2id = load_SNLI_dataset(
maxlen=maxSentLen
) #minlen, include one label, at least one word in the sentence
train_sents_l = np.asarray(all_sentences_l[0], dtype='int32')
dev_sents_l = np.asarray(all_sentences_l[1], dtype='int32')
test_sents_l = np.asarray(all_sentences_l[2], dtype='int32')
train_masks_l = np.asarray(all_masks_l[0], dtype=theano.config.floatX)
dev_masks_l = np.asarray(all_masks_l[1], dtype=theano.config.floatX)
test_masks_l = np.asarray(all_masks_l[2], dtype=theano.config.floatX)
train_sents_r = np.asarray(all_sentences_r[0], dtype='int32')
dev_sents_r = np.asarray(all_sentences_r[1], dtype='int32')
test_sents_r = np.asarray(all_sentences_r[2], dtype='int32')
train_masks_r = np.asarray(all_masks_r[0], dtype=theano.config.floatX)
dev_masks_r = np.asarray(all_masks_r[1], dtype=theano.config.floatX)
test_masks_r = np.asarray(all_masks_r[2], dtype=theano.config.floatX)
train_labels_store = np.asarray(all_labels[0], dtype='int32')
dev_labels_store = np.asarray(all_labels[1], dtype='int32')
test_labels_store = np.asarray(all_labels[2], dtype='int32')
train_size = len(train_labels_store)
dev_size = len(dev_labels_store)
test_size = len(test_labels_store)
print 'train size: ', train_size, ' dev size: ', dev_size, ' test size: ', test_size
vocab_size = len(word2id) + 1
"first randomly initialize each word in the matrix 'rand_values', then load pre-trained word2vec embeddinds to initialize words, uncovered"
"words keep random initialization"
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_word2vec()
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
init_embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
"now, start to build the input form of the model"
sents_ids_l = T.imatrix()
sents_mask_l = T.fmatrix()
sents_ids_r = T.imatrix()
sents_mask_r = T.fmatrix()
labels = T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
'Use word ids in sentences to retrieve word embeddings from matrix "init_embeddings", each sentence will be in'
'tensor2 (emb_size, sen_length), then the minibatch will be in tensor3 (batch_size, emb_size, sen_length) '
embed_input_l = init_embeddings[sents_ids_l.flatten(
)].reshape((batch_size, maxSentLen, emb_size)).dimshuffle(
0, 2, 1
) #embed_input(init_embeddings, sents_ids_l)#embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
embed_input_r = init_embeddings[sents_ids_r.flatten()].reshape(
(batch_size, maxSentLen, emb_size)).dimshuffle(0, 2, 1)
'''create parameters for attentive convolution function '''
conv_W, conv_b = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size, filter_size[0]))
conv_W_context, conv_b_context = create_conv_para(
rng, filter_shape=(hidden_size[0], 1, emb_size, 1))
NN_para = [conv_W, conv_b, conv_W_context]
'''
attentive convolution function
'''
attentive_conv_layer = Attentive_Conv_for_Pair_easy_version(
rng,
input_tensor3=embed_input_l,
input_tensor3_r=embed_input_r,
mask_matrix=sents_mask_l,
mask_matrix_r=sents_mask_r,
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[0], 1, emb_size, 1),
W=conv_W,
b=conv_b,
W_context=conv_W_context,
b_context=conv_b_context)
attentive_sent_embeddings_l = attentive_conv_layer.attentive_maxpool_vec_l
attentive_sent_embeddings_r = attentive_conv_layer.attentive_maxpool_vec_r
"Logistic Regression layer"
LR_input = T.concatenate([
attentive_sent_embeddings_l, attentive_sent_embeddings_r,
attentive_sent_embeddings_l + attentive_sent_embeddings_r,
attentive_sent_embeddings_l * attentive_sent_embeddings_r
],
axis=1)
LR_input_size = 4 * hidden_size[0]
U_a = create_ensemble_para(rng, 3, LR_input_size) # (input_size, 3)
LR_b = theano.shared(value=np.zeros((3, ), dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
LR_para = [U_a, LR_b]
layer_LR = LogisticRegression(
rng,
input=normalize_matrix_col_wise(LR_input),
n_in=LR_input_size,
n_out=3,
W=U_a,
b=LR_b
) #basically it is a multiplication between weight matrix and input feature vector
loss = layer_LR.negative_log_likelihood(
labels
) #for classification task, we usually used negative log likelihood as loss, the lower the better.
params = [init_embeddings] + NN_para + LR_para
cost = loss
"Use AdaGrad to update parameters"
updates = Gradient_Cost_Para(cost, params, learning_rate)
train_model = theano.function(
[sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels],
cost,
updates=updates,
allow_input_downcast=True,
on_unused_input='ignore')
dev_model = theano.function(
[sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels],
layer_LR.errors(labels),
allow_input_downcast=True,
on_unused_input='ignore')
test_model = theano.function(
[sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels],
layer_LR.errors(labels),
allow_input_downcast=True,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
n_train_batches = train_size / batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
n_dev_batches = dev_size / batch_size
dev_batch_start = list(
np.arange(n_dev_batches) * batch_size) + [dev_size - batch_size]
n_test_batches = test_size / batch_size
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
max_acc_dev = 0.0
max_acc_test = 0.0
cost_i = 0.0
train_indices = range(train_size)
while epoch < n_epochs:
epoch = epoch + 1
random.Random(100).shuffle(
train_indices
) #shuffle training set for each new epoch, is supposed to promote performance, but not garrenteed
iter_accu = 0
for batch_id in train_batch_start: #for each batch
# iter means how many batches have been run, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
train_id_batch = train_indices[batch_id:batch_id + batch_size]
cost_i += train_model(train_sents_l[train_id_batch],
train_masks_l[train_id_batch],
train_sents_r[train_id_batch],
train_masks_r[train_id_batch],
train_labels_store[train_id_batch])
#after each 1000 batches, we test the performance of the model on all test data
# if (epoch==1 and iter%1000==0) or (epoch>=2 and iter%5==0):
if iter % 1000 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
past_time = time.time()
'''
test
'''
error_sum = 0.0
for test_batch_id in test_batch_start: # for each test batch
error_i = test_model(
test_sents_l[test_batch_id:test_batch_id + batch_size],
test_masks_l[test_batch_id:test_batch_id + batch_size],
test_sents_r[test_batch_id:test_batch_id + batch_size],
test_masks_r[test_batch_id:test_batch_id + batch_size],
test_labels_store[test_batch_id:test_batch_id +
batch_size])
error_sum += error_i
test_acc = 1.0 - error_sum / (len(test_batch_start))
if test_acc > max_acc_test:
max_acc_test = test_acc
print '\t\tcurrent test_acc:', test_acc, ' ; ', '\t\t\t\t\tmax_test_acc:', max_acc_test
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
return max_acc_test
def evaluate_lenet5(learning_rate=0.01, n_epochs=2000, batch_size=100, emb_size=10, hidden_size=10,
L2_weight=0.0001, para_len_limit=400, q_len_limit=40, max_EM=0.217545454546):
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/SQuAD/';
rng = numpy.random.RandomState(23455)
train_para_list, train_Q_list, train_label_list, train_para_mask, train_mask, word2id, train_feature_matrixlist=load_train(para_len_limit, q_len_limit)
train_size=len(train_para_list)
if train_size!=len(train_Q_list) or train_size!=len(train_label_list) or train_size!=len(train_para_mask):
print 'train_size!=len(Q_list) or train_size!=len(label_list) or train_size!=len(para_mask)'
exit(0)
test_para_list, test_Q_list, test_Q_list_word, test_para_mask, test_mask, overall_vocab_size, overall_word2id, test_text_list, q_ansSet_list, test_feature_matrixlist= load_dev_or_test(word2id, para_len_limit, q_len_limit)
test_size=len(test_para_list)
if test_size!=len(test_Q_list) or test_size!=len(test_mask) or test_size!=len(test_para_mask):
print 'test_size!=len(test_Q_list) or test_size!=len(test_mask) or test_size!=len(test_para_mask)'
exit(0)
rand_values=random_value_normal((overall_vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
# rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
# id2word = {y:x for x,y in overall_word2id.iteritems()}
# word2vec=load_word2vec()
# rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings=theano.shared(value=rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
paragraph = T.imatrix('paragraph')
questions = T.imatrix('questions')
labels = T.imatrix('labels')
para_mask=T.fmatrix('para_mask')
q_mask=T.fmatrix('q_mask')
extraF=T.ftensor3('extraF') # should be in shape (batch, wordsize, 3)
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
norm_extraF=normalize_matrix(extraF)
U1, W1, b1=create_GRU_para(rng, emb_size, hidden_size)
U1_b, W1_b, b1_b=create_GRU_para(rng, emb_size, hidden_size)
paragraph_para=[U1, W1, b1, U1_b, W1_b, b1_b]
UQ, WQ, bQ=create_GRU_para(rng, emb_size, hidden_size)
UQ_b, WQ_b, bQ_b=create_GRU_para(rng, emb_size, hidden_size)
Q_para=[UQ, WQ, bQ, UQ_b, WQ_b, bQ_b]
W_a1 = create_ensemble_para(rng, hidden_size, hidden_size)# init_weights((2*hidden_size, hidden_size))
W_a2 = create_ensemble_para(rng, hidden_size, hidden_size)
U_a = create_ensemble_para(rng, 2, hidden_size+3) # 3 extra features
LR_b = theano.shared(value=numpy.zeros((2,),
dtype=theano.config.floatX), # @UndefinedVariable
name='LR_b', borrow=True)
attention_paras=[W_a1, W_a2, U_a, LR_b]
params = [embeddings]+paragraph_para+Q_para+attention_paras
load_model_from_file(rootPath+'Best_Paras_conv_0.217545454545', params)
paragraph_input = embeddings[paragraph.flatten()].reshape((paragraph.shape[0], paragraph.shape[1], emb_size)).transpose((0, 2,1)) # (batch_size, emb_size, maxparalen)
concate_paragraph_input=T.concatenate([paragraph_input, norm_extraF.dimshuffle((0,2,1))], axis=1)
paragraph_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=paragraph_input, Mask=para_mask, hidden_dim=hidden_size,U=U1,W=W1,b=b1,Ub=U1_b,Wb=W1_b,bb=b1_b)
para_reps=paragraph_model.output_tensor #(batch, emb, para_len)
# #LSTM
# fwd_LSTM_para_dict=create_LSTM_para(rng, emb_size, hidden_size)
# bwd_LSTM_para_dict=create_LSTM_para(rng, emb_size, hidden_size)
# paragraph_para=fwd_LSTM_para_dict.values()+ bwd_LSTM_para_dict.values()# .values returns a list of parameters
# paragraph_model=Bd_LSTM_Batch_Tensor_Input_with_Mask(paragraph_input, para_mask, hidden_size, fwd_LSTM_para_dict, bwd_LSTM_para_dict)
# para_reps=paragraph_model.output_tensor
Qs_emb = embeddings[questions.flatten()].reshape((questions.shape[0], questions.shape[1], emb_size)).transpose((0, 2,1)) #(#questions, emb_size, maxsenlength)
questions_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=Qs_emb, Mask=q_mask, hidden_dim=hidden_size, U=UQ,W=WQ,b=bQ, Ub=UQ_b, Wb=WQ_b, bb=bQ_b)
# questions_reps=questions_model.output_sent_rep_maxpooling.reshape((batch_size, 1, hidden_size)) #(batch, 2*out_size)
questions_reps_tensor=questions_model.output_tensor
#questions_reps=T.repeat(questions_reps, para_reps.shape[2], axis=1)
# #LSTM for questions
# fwd_LSTM_q_dict=create_LSTM_para(rng, emb_size, hidden_size)
# bwd_LSTM_q_dict=create_LSTM_para(rng, emb_size, hidden_size)
# Q_para=fwd_LSTM_q_dict.values()+ bwd_LSTM_q_dict.values()# .values returns a list of parameters
# questions_model=Bd_LSTM_Batch_Tensor_Input_with_Mask(Qs_emb, q_mask, hidden_size, fwd_LSTM_q_dict, bwd_LSTM_q_dict)
# questions_reps_tensor=questions_model.output_tensor
#use CNN for question modeling
# Qs_emb_tensor4=Qs_emb.dimshuffle((0,'x', 1,2)) #(batch_size, 1, emb+3, maxparalen)
# conv_W, conv_b=create_conv_para(rng, filter_shape=(hidden_size, 1, emb_size, 5))
# Q_conv_para=[conv_W, conv_b]
# conv_model = Conv_with_input_para(rng, input=Qs_emb_tensor4,
# image_shape=(batch_size, 1, emb_size, q_len_limit),
# filter_shape=(hidden_size, 1, emb_size, 5), W=conv_W, b=conv_b)
# conv_output=conv_model.narrow_conv_out.reshape((batch_size, hidden_size, q_len_limit-5+1)) #(batch, 1, hidden_size, maxparalen-1)
# gru_mask=(q_mask[:,:-4]*q_mask[:,1:-3]*q_mask[:,2:-2]*q_mask[:,3:-1]*q_mask[:,4:]).reshape((batch_size, 1, q_len_limit-5+1))
# masked_conv_output=conv_output*gru_mask
# questions_conv_reps=T.max(masked_conv_output, axis=2).reshape((batch_size, 1, hidden_size))
# new_labels=T.gt(labels[:,:-1]+labels[:,1:], 0.0)
# ConvGRU_1=Conv_then_GRU_then_Classify(rng, concate_paragraph_input, Qs_emb, para_len_limit, q_len_limit, emb_size+3, hidden_size, emb_size, 2, batch_size, para_mask, q_mask, new_labels, 2)
# ConvGRU_1_dis=ConvGRU_1.masked_dis_inprediction
# padding_vec = T.zeros((batch_size, 1), dtype=theano.config.floatX)
# ConvGRU_1_dis_leftpad=T.concatenate([padding_vec, ConvGRU_1_dis], axis=1)
# ConvGRU_1_dis_rightpad=T.concatenate([ConvGRU_1_dis, padding_vec], axis=1)
# ConvGRU_1_dis_into_unigram=0.5*(ConvGRU_1_dis_leftpad+ConvGRU_1_dis_rightpad)
#
def example_in_batch(para_matrix, q_matrix):
#assume both are (hidden, len)
transpose_para_matrix=para_matrix.T
interaction_matrix=T.dot(transpose_para_matrix, q_matrix) #(para_len, q_len)
norm_interaction_matrix=T.nnet.softmax(interaction_matrix)
return T.dot(q_matrix, norm_interaction_matrix.T) #(len, para_len)
batch_q_reps, updates = theano.scan(fn=example_in_batch,
outputs_info=None,
sequences=[para_reps, questions_reps_tensor]) #batch_q_reps (batch, hidden, para_len)
#attention distributions
norm_W_a1=normalize_matrix(W_a1)
norm_W_a2=normalize_matrix(W_a2)
norm_U_a=normalize_matrix(U_a)
transformed_para_reps=T.maximum(T.dot(para_reps.transpose((0, 2,1)), norm_W_a2),0.0) #relu
transformed_q_reps=T.maximum(T.dot(batch_q_reps.transpose((0, 2,1)), norm_W_a1),0.0)
#transformed_q_reps=T.repeat(transformed_q_reps, transformed_para_reps.shape[1], axis=1)
add_both=transformed_para_reps+transformed_q_reps
# U_c, W_c, b_c=create_GRU_para(rng, hidden_size, hidden_size)
# U_c_b, W_c_b, b_c_b=create_GRU_para(rng, hidden_size, hidden_size)
# accumu_para=[U_c, W_c, b_c, U_c_b, W_c_b, b_c_b]
# accumu_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_both.transpose((0,2,1)), Mask=para_mask, hidden_dim=hidden_size,U=U_c,W=W_c,b=b_c,Ub=U_c_b,Wb=W_c_b,bb=b_c_b)
# accu_both=accumu_model.output_tensor.transpose((0,2,1))
prior_att=T.concatenate([add_both, norm_extraF], axis=2)
#prior_att=T.concatenate([transformed_para_reps, transformed_q_reps], axis=2)
valid_indices=para_mask.flatten().nonzero()[0]
layer3=LogisticRegression(rng, input=prior_att.reshape((batch_size*prior_att.shape[1], hidden_size+3)), n_in=hidden_size+3, n_out=2, W=norm_U_a, b=LR_b)
#error =layer3.negative_log_likelihood(labels.flatten()[valid_indices])
error = -T.sum(T.log(layer3.p_y_given_x)[valid_indices, labels.flatten()[valid_indices]])#[T.arange(y.shape[0]), y])
distributions=layer3.p_y_given_x[:,-1].reshape((batch_size, para_mask.shape[1]))
#distributions=layer3.y_pred.reshape((batch_size, para_mask.shape[1]))
# masked_dis=(distributions+ConvGRU_1_dis_into_unigram)*para_mask
masked_dis=distributions*para_mask
'''
strength = T.tanh(T.dot(prior_att, norm_U_a)) #(batch, #word, 1)
distributions=debug_print(strength.reshape((batch_size, paragraph.shape[1])), 'distributions')
para_mask=para_mask
masked_dis=distributions*para_mask
# masked_label=debug_print(labels*para_mask, 'masked_label')
# error=((masked_dis-masked_label)**2).mean()
label_mask=T.gt(labels,0.0)
neg_label_mask=T.lt(labels,0.0)
dis_masked=distributions*label_mask
remain_dis_masked=distributions*neg_label_mask
ans_size=T.sum(label_mask)
non_ans_size=T.sum(neg_label_mask)
pos_error=T.sum((dis_masked-label_mask)**2)/ans_size
neg_error=T.sum((remain_dis_masked-(-neg_label_mask))**2)/non_ans_size
error=pos_error+0.5*neg_error #(ans_size*1.0/non_ans_size)*
'''
# def AttentionLayer(q_rep, ext_M):
# theano_U_a=debug_print(norm_U_a, 'norm_U_a')
# prior_att=debug_print(T.nnet.sigmoid(T.dot(q_rep, norm_W_a1).reshape((1, hidden_size)) + T.dot(paragraph_model.output_matrix.transpose(), norm_W_a2)), 'prior_att')
# f __name__ == '__main__':
# prior_att=T.concatenate([prior_att, ext_M], axis=1)
#
# strength = debug_print(T.tanh(T.dot(prior_att, theano_U_a)), 'strength') #(#word, 1)
# return strength.transpose() #(1, #words)
# distributions, updates = theano.scan(
# AttentionLayer,
# sequences=[questions_reps,extraF] )
# distributions=debug_print(distributions.reshape((questions.shape[0],paragraph.shape[0])), 'distributions')
# labels=debug_print(labels, 'labels')
# label_mask=T.gt(labels,0.0)
# neg_label_mask=T.lt(labels,0.0)
# dis_masked=distributions*label_mask
# remain_dis_masked=distributions*neg_label_mask
# pos_error=((dis_masked-1)**2).mean()
# neg_error=((remain_dis_masked-(-1))**2).mean()
# error=pos_error+(T.sum(label_mask)*1.0/T.sum(neg_label_mask))*neg_error
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
L2_reg =L2norm_paraList([embeddings,U1, W1, U1_b, W1_b,UQ, WQ , UQ_b, WQ_b, W_a1, W_a2, U_a])
#L2_reg = L2norm_paraList(params)
cost=error#+ConvGRU_1.error#
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / (T.sqrt(acc)+1e-8))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([paragraph, questions,labels, para_mask, q_mask, extraF], cost, updates=updates,on_unused_input='ignore')
test_model = theano.function([paragraph, questions,para_mask, q_mask, extraF], masked_dis, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches=train_size/batch_size
# remain_train=train_size%batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)+[train_size-batch_size]
n_test_batches=test_size/batch_size
# remain_test=test_size%batch_size
test_batch_start=list(numpy.arange(n_test_batches)*batch_size)+[test_size-batch_size]
max_F1_acc=0.0
max_exact_acc=0.0
cost_i=0.0
train_ids = range(train_size)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.shuffle(train_ids)
iter_accu=0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
iter_accu+=1
# haha=para_mask[para_id:para_id+batch_size]
# print haha
# for i in range(batch_size):
# print len(haha[i])
cost_i+= train_model(
np.asarray([train_para_list[id] for id in train_ids[para_id:para_id+batch_size]], dtype='int32'),
np.asarray([train_Q_list[id] for id in train_ids[para_id:para_id+batch_size]], dtype='int32'),
np.asarray([train_label_list[id] for id in train_ids[para_id:para_id+batch_size]], dtype='int32'),
np.asarray([train_para_mask[id] for id in train_ids[para_id:para_id+batch_size]], dtype=theano.config.floatX),
np.asarray([train_mask[id] for id in train_ids[para_id:para_id+batch_size]], dtype=theano.config.floatX),
np.asarray([train_feature_matrixlist[id] for id in train_ids[para_id:para_id+batch_size]], dtype=theano.config.floatX))
#print iter
if iter%10==0:
print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
print 'Testing...'
past_time = time.time()
exact_match=0.0
F1_match=0.0
q_amount=0
for test_para_id in test_batch_start:
distribution_matrix=test_model(
np.asarray(test_para_list[test_para_id:test_para_id+batch_size], dtype='int32'),
np.asarray(test_Q_list[test_para_id:test_para_id+batch_size], dtype='int32'),
np.asarray(test_para_mask[test_para_id:test_para_id+batch_size], dtype=theano.config.floatX),
np.asarray(test_mask[test_para_id:test_para_id+batch_size], dtype=theano.config.floatX),
np.asarray(test_feature_matrixlist[test_para_id:test_para_id+batch_size], dtype=theano.config.floatX))
# print distribution_matrix
test_para_wordlist_list=test_text_list[test_para_id:test_para_id+batch_size]
para_gold_ansset_list=q_ansSet_list[test_para_id:test_para_id+batch_size]
paralist_extra_features=test_feature_matrixlist[test_para_id:test_para_id+batch_size]
sub_para_mask=test_para_mask[test_para_id:test_para_id+batch_size]
para_len=len(test_para_wordlist_list[0])
if para_len!=len(distribution_matrix[0]):
print 'para_len!=len(distribution_matrix[0]):', para_len, len(distribution_matrix[0])
exit(0)
# q_size=len(distribution_matrix)
q_amount+=batch_size
# print q_size
# print test_para_word_list
Q_list_inword=test_Q_list_word[test_para_id:test_para_id+batch_size]
for q in range(batch_size): #for each question
# if len(distribution_matrix[q])!=len(test_label_matrix[q]):
# print 'len(distribution_matrix[q])!=len(test_label_matrix[q]):', len(distribution_matrix[q]), len(test_label_matrix[q])
# else:
# ss=len(distribution_matrix[q])
# combine_list=[]
# for ii in range(ss):
# combine_list.append(str(distribution_matrix[q][ii])+'('+str(test_label_matrix[q][ii])+')')
# print combine_list
# exit(0)
# print 'distribution_matrix[q]:',distribution_matrix[q]
pred_ans=extract_ansList_attentionList(test_para_wordlist_list[q], distribution_matrix[q], np.asarray(paralist_extra_features[q], dtype=theano.config.floatX), sub_para_mask[q], Q_list_inword[q])
q_gold_ans_set=para_gold_ansset_list[q]
# print test_para_wordlist_list[q]
# print Q_list_inword[q]
# print pred_ans.encode('utf8'), q_gold_ans_set
if pred_ans in q_gold_ans_set:
exact_match+=1
F1=MacroF1(pred_ans, q_gold_ans_set)
F1_match+=F1
# match_amount=len(pred_ans_set & q_gold_ans_set)
# # print 'q_gold_ans_set:', q_gold_ans_set
# # print 'pred_ans_set:', pred_ans_set
# if match_amount>0:
# exact_match+=match_amount*1.0/len(pred_ans_set)
F1_acc=F1_match/q_amount
exact_acc=exact_match/q_amount
if F1_acc> max_F1_acc:
max_F1_acc=F1_acc
if exact_acc> max_exact_acc:
max_exact_acc=exact_acc
if max_exact_acc > max_EM:
store_model_to_file(rootPath+'Best_Paras_conv_'+str(max_exact_acc), params)
print 'Finished storing best params at:', max_exact_acc
print 'current average F1:', F1_acc, '\t\tmax F1:', max_F1_acc, 'current exact:', exact_acc, '\t\tmax exact_acc:', max_exact_acc
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.01,
n_epochs=100,
emb_size=40,
batch_size=50,
describ_max_len=20,
type_size=12,
filter_size=[3, 5],
maxSentLen=100,
hidden_size=[300, 300]):
model_options = locals().copy()
print "model options", model_options
emb_root = '/save/wenpeng/datasets/LORELEI/multi-lingual-emb/'
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
srng = T.shared_randomstreams.RandomStreams(rng.randint(seed))
all_sentences, all_masks, all_labels, word2id = load_BBN_multi_labels_dataset(
maxlen=maxSentLen
) #minlen, include one label, at least one word in the sentence
label_sent, label_mask = load_SF_type_descriptions(word2id, type_size,
describ_max_len)
label_sent = np.asarray(label_sent, dtype='int32')
label_mask = np.asarray(label_mask, dtype=theano.config.floatX)
train_sents = np.asarray(all_sentences[0], dtype='int32')
train_masks = np.asarray(all_masks[0], dtype=theano.config.floatX)
train_labels = np.asarray(all_labels[0], dtype='int32')
train_size = len(train_labels)
dev_sents = np.asarray(all_sentences[1], dtype='int32')
dev_masks = np.asarray(all_masks[1], dtype=theano.config.floatX)
dev_labels = np.asarray(all_labels[1], dtype='int32')
dev_size = len(dev_labels)
test_sents = np.asarray(all_sentences[2], dtype='int32')
test_masks = np.asarray(all_masks[2], dtype=theano.config.floatX)
test_labels = np.asarray(all_labels[2], dtype='int32')
test_size = len(test_labels)
vocab_size = len(word2id) + 1 # add one zero pad index
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
rand_values[0] = np.array(np.zeros(emb_size), dtype=theano.config.floatX)
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_fasttext_multiple_word2vec_given_file([
emb_root + 'IL5-cca-wiki-lorelei-d40.eng.vec',
emb_root + 'IL5-cca-wiki-lorelei-d40.IL5.vec'
], 40)
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
#now, start to build the input form of the model
sents_id_matrix = T.imatrix('sents_id_matrix')
sents_mask = T.fmatrix('sents_mask')
labels = T.imatrix('labels') #batch*12
des_id_matrix = T.imatrix()
des_mask = T.fmatrix()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
common_input = embeddings[sents_id_matrix.flatten()].reshape(
(batch_size, maxSentLen, emb_size)).dimshuffle(
0, 2, 1) #the input format can be adapted into CNN or GRU or LSTM
bow_emb = T.sum(common_input * sents_mask.dimshuffle(0, 'x', 1), axis=2)
# bow_mean_emb = bow_emb/T.sum(sents_mask,axis=1).dimshuffle(0,'x')
des_input = embeddings[des_id_matrix.flatten()].reshape(
(type_size, describ_max_len, emb_size)).dimshuffle(0, 2, 1)
bow_des = T.sum(des_input * des_mask.dimshuffle(0, 'x', 1),
axis=2) #(tyope_size, emb_size)
conv_W, conv_b = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size, filter_size[0]))
conv_W2, conv_b2 = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size,
filter_size[1]))
multiCNN_para = [conv_W, conv_b, conv_W2, conv_b2]
conv_att_W, conv_att_b = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size,
filter_size[0]))
conv_W_context, conv_b_context = create_conv_para(
rng, filter_shape=(hidden_size[0], 1, emb_size, 1))
conv_att_W2, conv_att_b2 = create_conv_para(rng,
filter_shape=(hidden_size[0],
1, emb_size,
filter_size[1]))
conv_W_context2, conv_b_context2 = create_conv_para(
rng, filter_shape=(hidden_size[0], 1, emb_size, 1))
ACNN_para = [
conv_att_W, conv_att_b, conv_W_context, conv_att_W2, conv_att_b2,
conv_W_context2
]
# NN_para = multiCNN_para+ACNN_para
conv_model = Conv_with_Mask(
rng,
input_tensor3=common_input,
mask_matrix=sents_mask,
image_shape=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
W=conv_W,
b=conv_b
) #mutiple mask with the conv_out to set the features by UNK to zero
sent_embeddings = conv_model.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
conv_model2 = Conv_with_Mask(
rng,
input_tensor3=common_input,
mask_matrix=sents_mask,
image_shape=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[1]),
W=conv_W2,
b=conv_b2
) #mutiple mask with the conv_out to set the features by UNK to zero
sent_embeddings2 = conv_model2.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
LR_input = T.concatenate([sent_embeddings, sent_embeddings2, bow_emb],
axis=1)
LR_input_size = hidden_size[0] * 2 + emb_size
#classification layer, it is just mapping from a feature vector of size "hidden_size" to a vector of only two values: positive, negative
U_a = create_ensemble_para(
rng, 12, LR_input_size) # the weight matrix hidden_size*2
LR_b = theano.shared(value=np.zeros((12, ), dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
LR_para = [U_a, LR_b]
layer_LR = LogisticRegression(
rng, input=LR_input, n_in=LR_input_size, n_out=12, W=U_a, b=LR_b
) #basically it is a multiplication between weight matrix and input feature vector
score_matrix = T.nnet.sigmoid(layer_LR.before_softmax) #batch * 12
prob_pos = T.where(labels < 1, 1.0 - score_matrix, score_matrix)
loss = -T.mean(T.log(prob_pos))
'''
GRU
'''
U1, W1, b1 = create_GRU_para(rng, emb_size, hidden_size[0])
GRU_NN_para = [
U1, W1, b1
] #U1 includes 3 matrices, W1 also includes 3 matrices b1 is bias
# gru_input = common_input.dimshuffle((0,2,1)) #gru requires input (batch_size, emb_size, maxSentLen)
gru_layer = GRU_Batch_Tensor_Input_with_Mask(common_input, sents_mask,
hidden_size[0], U1, W1, b1)
gru_sent_embeddings = gru_layer.output_sent_rep # (batch_size, hidden_size)
LR_att_input = T.concatenate([gru_sent_embeddings, bow_emb], axis=1)
LR_att_input_size = hidden_size[0] + emb_size
#classification layer, it is just mapping from a feature vector of size "hidden_size" to a vector of only two values: positive, negative
U_att_a = create_ensemble_para(
rng, 12, LR_att_input_size) # the weight matrix hidden_size*2
LR_att_b = theano.shared(value=np.zeros((12, ),
dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
LR_att_para = [U_att_a, LR_att_b]
layer_att_LR = LogisticRegression(
rng,
input=LR_att_input,
n_in=LR_att_input_size,
n_out=12,
W=U_att_a,
b=LR_att_b
) #basically it is a multiplication between weight matrix and input feature vector
att_score_matrix = T.nnet.sigmoid(layer_att_LR.before_softmax) #batch * 12
att_prob_pos = T.where(labels < 1, 1.0 - att_score_matrix,
att_score_matrix)
att_loss = -T.mean(T.log(att_prob_pos))
'''
ACNN
'''
attentive_conv_layer = Attentive_Conv_for_Pair(
rng,
origin_input_tensor3=common_input,
origin_input_tensor3_r=common_input,
input_tensor3=common_input,
input_tensor3_r=common_input,
mask_matrix=sents_mask,
mask_matrix_r=sents_mask,
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[0], 1, emb_size, 1),
W=conv_att_W,
b=conv_att_b,
W_context=conv_W_context,
b_context=conv_b_context)
sent_att_embeddings = attentive_conv_layer.attentive_maxpool_vec_l
attentive_conv_layer2 = Attentive_Conv_for_Pair(
rng,
origin_input_tensor3=common_input,
origin_input_tensor3_r=common_input,
input_tensor3=common_input,
input_tensor3_r=common_input,
mask_matrix=sents_mask,
mask_matrix_r=sents_mask,
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[1]),
filter_shape_context=(hidden_size[0], 1, emb_size, 1),
W=conv_att_W2,
b=conv_att_b2,
W_context=conv_W_context2,
b_context=conv_b_context2)
sent_att_embeddings2 = attentive_conv_layer2.attentive_maxpool_vec_l
acnn_LR_input = T.concatenate(
[sent_att_embeddings, sent_att_embeddings2, bow_emb], axis=1)
acnn_LR_input_size = hidden_size[0] * 2 + emb_size
#classification layer, it is just mapping from a feature vector of size "hidden_size" to a vector of only two values: positive, negative
acnn_U_a = create_ensemble_para(
rng, 12, acnn_LR_input_size) # the weight matrix hidden_size*2
acnn_LR_b = theano.shared(value=np.zeros((12, ),
dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
acnn_LR_para = [acnn_U_a, acnn_LR_b]
acnn_layer_LR = LogisticRegression(
rng,
input=acnn_LR_input,
n_in=acnn_LR_input_size,
n_out=12,
W=acnn_U_a,
b=acnn_LR_b
) #basically it is a multiplication between weight matrix and input feature vector
acnn_score_matrix = T.nnet.sigmoid(
acnn_layer_LR.before_softmax) #batch * 12
acnn_prob_pos = T.where(labels < 1, 1.0 - acnn_score_matrix,
acnn_score_matrix)
acnn_loss = -T.mean(T.log(acnn_prob_pos))
'''
dataless cosine
'''
cosine_scores = normalize_matrix_rowwise(bow_emb).dot(
normalize_matrix_rowwise(bow_des).T)
cosine_score_matrix = T.nnet.sigmoid(
cosine_scores) #(batch_size, type_size)
params = multiCNN_para + LR_para + GRU_NN_para + LR_att_para + ACNN_para + acnn_LR_para # put all model parameters together
cost = loss + att_loss + acnn_loss + 1e-4 * ((conv_W**2).sum() +
(conv_W2**2).sum())
updates = Gradient_Cost_Para(cost, params, learning_rate)
'''
testing
'''
ensemble_NN_scores = T.max(T.concatenate([
att_score_matrix.dimshuffle('x', 0, 1),
score_matrix.dimshuffle('x', 0, 1),
acnn_score_matrix.dimshuffle('x', 0, 1)
],
axis=0),
axis=0)
ensemble_scores = 0.5 * ensemble_NN_scores + 0.5 * cosine_score_matrix
binarize_prob = T.where(ensemble_scores > 0.3, 1, 0)
#train_model = theano.function([sents_id_matrix, sents_mask, labels], cost, updates=updates, on_unused_input='ignore')
train_model = theano.function(
[sents_id_matrix, sents_mask, labels, des_id_matrix, des_mask],
cost,
updates=updates,
allow_input_downcast=True,
on_unused_input='ignore')
# dev_model = theano.function([sents_id_matrix, sents_mask, labels], layer_LR.errors(labels), allow_input_downcast=True, on_unused_input='ignore')
test_model = theano.function(
[sents_id_matrix, sents_mask, des_id_matrix, des_mask],
binarize_prob,
allow_input_downcast=True,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
n_train_batches = train_size / batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
# n_dev_batches=dev_size/batch_size
# dev_batch_start=list(np.arange(n_dev_batches)*batch_size)+[dev_size-batch_size]
n_test_batches = test_size / batch_size
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
# max_acc_dev=0.0
max_meanf1_test = 0.0
max_weightf1_test = 0.0
train_indices = range(train_size)
cost_i = 0.0
while epoch < n_epochs:
epoch = epoch + 1
random.Random(100).shuffle(train_indices)
iter_accu = 0
for batch_id in train_batch_start: #for each batch
# iter means how many batches have been run, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
train_id_batch = train_indices[batch_id:batch_id + batch_size]
cost_i += train_model(train_sents[train_id_batch],
train_masks[train_id_batch],
train_labels[train_id_batch], label_sent,
label_mask)
#after each 1000 batches, we test the performance of the model on all test data
if iter % 20 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
past_time = time.time()
error_sum = 0.0
all_pred_labels = []
all_gold_labels = []
for test_batch_id in test_batch_start: # for each test batch
pred_labels = test_model(
test_sents[test_batch_id:test_batch_id + batch_size],
test_masks[test_batch_id:test_batch_id + batch_size],
label_sent, label_mask)
gold_labels = test_labels[test_batch_id:test_batch_id +
batch_size]
# print 'pred_labels:', pred_labels
# print 'gold_labels;', gold_labels
all_pred_labels.append(pred_labels)
all_gold_labels.append(gold_labels)
all_pred_labels = np.concatenate(all_pred_labels)
all_gold_labels = np.concatenate(all_gold_labels)
test_mean_f1, test_weight_f1 = average_f1_two_array_by_col(
all_pred_labels, all_gold_labels)
if test_weight_f1 > max_weightf1_test:
max_weightf1_test = test_weight_f1
if test_mean_f1 > max_meanf1_test:
max_meanf1_test = test_mean_f1
print '\t\t\t\t\t\t\t\tcurrent f1s:', test_mean_f1, test_weight_f1, '\t\tmax_f1:', max_meanf1_test, max_weightf1_test
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.5, n_epochs=2000, batch_size=500, emb_size=300, hidden_size=300,
L2_weight=0.0001, para_len_limit=700, q_len_limit=40):
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/SQuAD/';
rng = numpy.random.RandomState(23455)
train_para_list, train_Q_list, train_label_list, train_para_mask, train_mask, word2id, train_feature_matrixlist=load_train(para_len_limit, q_len_limit)
train_size=len(train_para_list)
if train_size!=len(train_Q_list) or train_size!=len(train_label_list) or train_size!=len(train_para_mask):
print 'train_size!=len(Q_list) or train_size!=len(label_list) or train_size!=len(para_mask)'
exit(0)
test_para_list, test_Q_list, test_para_mask, test_mask, overall_vocab_size, overall_word2id, test_text_list, q_ansSet_list, test_feature_matrixlist= load_dev_or_test(word2id, para_len_limit, q_len_limit)
test_size=len(test_para_list)
if test_size!=len(test_Q_list) or test_size!=len(test_mask) or test_size!=len(test_para_mask):
print 'test_size!=len(test_Q_list) or test_size!=len(test_mask) or test_size!=len(test_para_mask)'
exit(0)
id2word = {y:x for x,y in overall_word2id.iteritems()}
word2vec=load_word2vec()
rand_values=random_value_normal((overall_vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
# rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings=theano.shared(value=rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
paragraph = T.imatrix('paragraph')
questions = T.imatrix('questions')
labels = T.imatrix('labels')
para_mask=T.fmatrix('para_mask')
q_mask=T.fmatrix('q_mask')
extraF=T.ftensor3('extraF') # should be in shape (batch, wordsize, 3)
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
paragraph_input = embeddings[paragraph.flatten()].reshape((paragraph.shape[0], paragraph.shape[1], emb_size)).transpose((0, 2,1)) # (batch_size, emb_size, maxparalen)
#
# # BdGRU(rng, str(0), shape, X, mask, is_train = 1, batch_size = 1, p = 0.5)
#
U1, W1, b1=create_GRU_para(rng, emb_size, hidden_size)
U1_b, W1_b, b1_b=create_GRU_para(rng, emb_size, hidden_size)
paragraph_para=[U1, W1, b1, U1_b, W1_b, b1_b]
paragraph_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=paragraph_input, Mask=para_mask, hidden_dim=hidden_size,U=U1,W=W1,b=b1,Ub=U1_b,Wb=W1_b,bb=b1_b)
para_reps=paragraph_model.output_tensor #(batch, emb, para_len)
Qs_emb = embeddings[questions.flatten()].reshape((questions.shape[0], questions.shape[1], emb_size)).transpose((0, 2,1)) #(#questions, emb_size, maxsenlength)
UQ, WQ, bQ=create_GRU_para(rng, emb_size, hidden_size)
UQ_b, WQ_b, bQ_b=create_GRU_para(rng, emb_size, hidden_size)
Q_para=[UQ, WQ, bQ, UQ_b, WQ_b, bQ_b]
questions_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=Qs_emb, Mask=q_mask, hidden_dim=hidden_size, U=UQ,W=WQ,b=bQ, Ub=UQ_b, Wb=WQ_b, bb=bQ_b)
questions_reps=questions_model.output_sent_rep_maxpooling.reshape((batch_size, 1, hidden_size)) #(batch, 2*out_size)
#questions_reps=T.repeat(questions_reps, para_reps.shape[2], axis=1)
#attention distributions
W_a1 = create_ensemble_para(rng, hidden_size, hidden_size)# init_weights((2*hidden_size, hidden_size))
W_a2 = create_ensemble_para(rng, hidden_size, hidden_size)
U_a = create_ensemble_para(rng, 2, hidden_size+3) # 3 extra features
norm_W_a1=normalize_matrix(W_a1)
norm_W_a2=normalize_matrix(W_a2)
norm_U_a=normalize_matrix(U_a)
LR_b = theano.shared(value=numpy.zeros((2,),
dtype=theano.config.floatX), # @UndefinedVariable
name='LR_b', borrow=True)
attention_paras=[W_a1, W_a2, U_a, LR_b]
transformed_para_reps=T.tanh(T.dot(para_reps.transpose((0, 2,1)), norm_W_a2))
transformed_q_reps=T.tanh(T.dot(questions_reps, norm_W_a1))
#transformed_q_reps=T.repeat(transformed_q_reps, transformed_para_reps.shape[1], axis=1)
add_both=0.5*(transformed_para_reps+transformed_q_reps)
prior_att=T.concatenate([add_both, normalize_matrix(extraF)], axis=2)
#prior_att=T.concatenate([transformed_para_reps, transformed_q_reps], axis=2)
valid_indices=para_mask.flatten().nonzero()[0]
layer3=LogisticRegression(rng, input=prior_att.reshape((batch_size*prior_att.shape[1], hidden_size+3)), n_in=hidden_size+3, n_out=2, W=norm_U_a, b=LR_b)
#error =layer3.negative_log_likelihood(labels.flatten()[valid_indices])
error = -T.mean(T.log(layer3.p_y_given_x)[valid_indices, labels.flatten()[valid_indices]])#[T.arange(y.shape[0]), y])
distributions=layer3.p_y_given_x[:,-1].reshape((batch_size, para_mask.shape[1]))
#distributions=layer3.y_pred.reshape((batch_size, para_mask.shape[1]))
masked_dis=distributions*para_mask
'''
strength = T.tanh(T.dot(prior_att, norm_U_a)) #(batch, #word, 1)
distributions=debug_print(strength.reshape((batch_size, paragraph.shape[1])), 'distributions')
para_mask=para_mask
masked_dis=distributions*para_mask
# masked_label=debug_print(labels*para_mask, 'masked_label')
# error=((masked_dis-masked_label)**2).mean()
label_mask=T.gt(labels,0.0)
neg_label_mask=T.lt(labels,0.0)
dis_masked=distributions*label_mask
remain_dis_masked=distributions*neg_label_mask
ans_size=T.sum(label_mask)
non_ans_size=T.sum(neg_label_mask)
pos_error=T.sum((dis_masked-label_mask)**2)/ans_size
neg_error=T.sum((remain_dis_masked-(-neg_label_mask))**2)/non_ans_size
error=pos_error+0.5*neg_error #(ans_size*1.0/non_ans_size)*
'''
# def AttentionLayer(q_rep, ext_M):
# theano_U_a=debug_print(norm_U_a, 'norm_U_a')
# prior_att=debug_print(T.nnet.sigmoid(T.dot(q_rep, norm_W_a1).reshape((1, hidden_size)) + T.dot(paragraph_model.output_matrix.transpose(), norm_W_a2)), 'prior_att')
# f __name__ == '__main__':
# prior_att=T.concatenate([prior_att, ext_M], axis=1)
#
# strength = debug_print(T.tanh(T.dot(prior_att, theano_U_a)), 'strength') #(#word, 1)
# return strength.transpose() #(1, #words)
# distributions, updates = theano.scan(
# AttentionLayer,
# sequences=[questions_reps,extraF] )
# distributions=debug_print(distributions.reshape((questions.shape[0],paragraph.shape[0])), 'distributions')
# labels=debug_print(labels, 'labels')
# label_mask=T.gt(labels,0.0)
# neg_label_mask=T.lt(labels,0.0)
# dis_masked=distributions*label_mask
# remain_dis_masked=distributions*neg_label_mask
# pos_error=((dis_masked-1)**2).mean()
# neg_error=((remain_dis_masked-(-1))**2).mean()
# error=pos_error+(T.sum(label_mask)*1.0/T.sum(neg_label_mask))*neg_error
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = [embeddings]+paragraph_para+Q_para+attention_paras
L2_reg =L2norm_paraList([embeddings,U1, W1, U1_b, W1_b,UQ, WQ, UQ_b, WQ_b, W_a1, W_a2, U_a])
#L2_reg = L2norm_paraList(params)
cost=error#+L2_weight*L2_reg
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / (T.sqrt(acc)+1e-8))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([paragraph, questions,labels, para_mask, q_mask, extraF], error, updates=updates,on_unused_input='ignore')
test_model = theano.function([paragraph, questions,para_mask, q_mask, extraF], masked_dis, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches=train_size/batch_size
# remain_train=train_size%batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)+[train_size-batch_size]
n_test_batches=test_size/batch_size
# remain_test=test_size%batch_size
test_batch_start=list(numpy.arange(n_test_batches)*batch_size)+[test_size-batch_size]
max_exact_acc=0.0
cost_i=0.0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#shuffle(train_batch_start)
iter_accu=0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
iter_accu+=1
# haha=para_mask[para_id:para_id+batch_size]
# print haha
# for i in range(batch_size):
# print len(haha[i])
cost_i+= train_model(
np.asarray(train_para_list[para_id:para_id+batch_size], dtype='int32'),
np.asarray(train_Q_list[para_id:para_id+batch_size], dtype='int32'),
np.asarray(train_label_list[para_id:para_id+batch_size], dtype='int32'),
np.asarray(train_para_mask[para_id:para_id+batch_size], dtype=theano.config.floatX),
np.asarray(train_mask[para_id:para_id+batch_size], dtype=theano.config.floatX),
np.asarray(train_feature_matrixlist[para_id:para_id+batch_size], dtype=theano.config.floatX))
#print iter
if iter%10==0:
print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
print 'Testing...'
past_time = time.time()
exact_match=0.0
q_amount=0
for test_para_id in test_batch_start:
distribution_matrix=test_model(
np.asarray(test_para_list[test_para_id:test_para_id+batch_size], dtype='int32'),
np.asarray(test_Q_list[test_para_id:test_para_id+batch_size], dtype='int32'),
np.asarray(test_para_mask[test_para_id:test_para_id+batch_size], dtype=theano.config.floatX),
np.asarray(test_mask[test_para_id:test_para_id+batch_size], dtype=theano.config.floatX),
np.asarray(test_feature_matrixlist[test_para_id:test_para_id+batch_size], dtype=theano.config.floatX))
# print distribution_matrix
test_para_wordlist_list=test_text_list[test_para_id:test_para_id+batch_size]
para_gold_ansset_list=q_ansSet_list[test_para_id:test_para_id+batch_size]
paralist_extra_features=test_feature_matrixlist[test_para_id:test_para_id+batch_size]
sub_para_mask=test_para_mask[test_para_id:test_para_id+batch_size]
para_len=len(test_para_wordlist_list[0])
if para_len!=len(distribution_matrix[0]):
print 'para_len!=len(distribution_matrix[0]):', para_len, len(distribution_matrix[0])
exit(0)
# q_size=len(distribution_matrix)
q_amount+=batch_size
# print q_size
# print test_para_word_list
for q in range(batch_size): #for each question
# if len(distribution_matrix[q])!=len(test_label_matrix[q]):
# print 'len(distribution_matrix[q])!=len(test_label_matrix[q]):', len(distribution_matrix[q]), len(test_label_matrix[q])
# else:
# ss=len(distribution_matrix[q])
# combine_list=[]
# for ii in range(ss):
# combine_list.append(str(distribution_matrix[q][ii])+'('+str(test_label_matrix[q][ii])+')')
# print combine_list
# exit(0)
# print 'distribution_matrix[q]:',distribution_matrix[q]
pred_ans=extract_ansList_attentionList(test_para_wordlist_list[q], distribution_matrix[q], np.asarray(paralist_extra_features[q], dtype=theano.config.floatX), sub_para_mask[q])
q_gold_ans_set=para_gold_ansset_list[q]
F1=MacroF1(pred_ans, q_gold_ans_set)
exact_match+=F1
# match_amount=len(pred_ans_set & q_gold_ans_set)
# # print 'q_gold_ans_set:', q_gold_ans_set
# # print 'pred_ans_set:', pred_ans_set
# if match_amount>0:
# exact_match+=match_amount*1.0/len(pred_ans_set)
exact_acc=exact_match/q_amount
if exact_acc> max_exact_acc:
max_exact_acc=exact_acc
print 'current average F1:', exact_acc, '\t\tmax F1:', max_exact_acc
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(term1_str, term2_str):
emb_size=300
filter_size=[3,3]
maxSentLen=40
hidden_size=[300,300]
max_term_len=4
p_mode = 'conc'
batch_size = 1
term1_def, source1 = load_concept_def(term1_str)
print '\n',term1_str, ':\t', term1_def,'\t', source1,'\n'
term2_def, source2 = load_concept_def(term2_str)
print '\n',term2_str, ':\t', term2_def, '\t', source2,'\n'
# exit(0)
word2id = load_word2id('/save/wenpeng/datasets/HypeNet/HyperDef_label_meta_best_para_word2id.pkl')
seed=1234
np.random.seed(seed)
rng = np.random.RandomState(seed) #random seed, control the model generates the same results
# all_sentences_l, all_masks_l, all_sentences_r, all_masks_r, all_word1,all_word2,all_word1_mask,all_word2_mask,all_labels, all_extra, word2id =load_wordnet_hyper_vs_all_with_words(maxlen=maxSentLen, wordlen=max_term_len) #minlen, include one label, at least one word in the sentence
# test_sents_l, test_masks_l, test_sents_r, test_masks_r, test_labels, word2id =load_ACE05_dataset(maxSentLen, word2id)
# test_sents_l, test_masks_l, test_sents_r, test_masks_r, test_word1,test_word2,test_word1_mask,test_word2_mask,test_labels, test_extra, word2id = load_EVAlution_hyper_vs_all_with_words(maxSentLen, word2id, wordlen=max_term_len)
test_sents_l, test_masks_l, test_sents_r, test_masks_r, test_word1,test_word2,test_word1_mask,test_word2_mask, test_extra, word2id = parse_individual_termPair(term1_str, term2_str, term1_def, term2_def, maxSentLen, word2id, wordlen=max_term_len)
# total_size = len(all_sentences_l)
# hold_test_size = 10000
# train_size = total_size - hold_test_size
# train_sents_l=np.asarray(all_sentences_l[:train_size], dtype='int32')
# dev_sents_l=np.asarray(all_sentences_l[1], dtype='int32')
# test_sents_l=np.asarray(all_sentences_l[-test_size:], dtype='int32')
test_sents_l=np.asarray(test_sents_l, dtype='int32')
# train_masks_l=np.asarray(all_masks_l[:train_size], dtype=theano.config.floatX)
# dev_masks_l=np.asarray(all_masks_l[1], dtype=theano.config.floatX)
# test_masks_l=np.asarray(all_masks_l[-test_size:], dtype=theano.config.floatX)
test_masks_l=np.asarray(test_masks_l, dtype=theano.config.floatX)
# train_sents_r=np.asarray(all_sentences_r[:train_size], dtype='int32')
# dev_sents_r=np.asarray(all_sentences_r[1] , dtype='int32')
# test_sents_r=np.asarray(all_sentences_r[-test_size:], dtype='int32')
test_sents_r=np.asarray(test_sents_r, dtype='int32')
# train_masks_r=np.asarray(all_masks_r[:train_size], dtype=theano.config.floatX)
# dev_masks_r=np.asarray(all_masks_r[1], dtype=theano.config.floatX)
# test_masks_r=np.asarray(all_masks_r[-test_size:], dtype=theano.config.floatX)
test_masks_r=np.asarray(test_masks_r, dtype=theano.config.floatX)
# train_word1=np.asarray(all_word1[:train_size], dtype='int32')
# train_word2=np.asarray(all_word2[:train_size], dtype='int32')
test_word1=np.asarray(test_word1, dtype='int32')
test_word2=np.asarray(test_word2, dtype='int32')
# train_word1_mask=np.asarray(all_word1_mask[:train_size], dtype=theano.config.floatX)
# train_word2_mask=np.asarray(all_word2_mask[:train_size], dtype=theano.config.floatX)
test_word1_mask=np.asarray(test_word1_mask, dtype=theano.config.floatX)
test_word2_mask=np.asarray(test_word2_mask, dtype=theano.config.floatX)
# train_labels_store=np.asarray(all_labels[:train_size], dtype='int32')
# dev_labels_store=np.asarray(all_labels[1], dtype='int32')
# test_labels_store=np.asarray(all_labels[-test_size:], dtype='int32')
# test_labels_store=np.asarray(test_labels, dtype='int32')
# train_extra=np.asarray(all_extra[:train_size], dtype=theano.config.floatX)
test_extra=np.asarray(test_extra, dtype=theano.config.floatX)
# train_size=len(train_labels_store)
# dev_size=len(dev_labels_store)
test_size=len(test_extra)
print ' test size: ', len(test_extra)
vocab_size=len(word2id)+1
rand_values=rng.normal(0.0, 0.01, (vocab_size, emb_size)) #generate a matrix by Gaussian distribution
#here, we leave code for loading word2vec to initialize words
# rand_values[0]=np.array(np.zeros(emb_size),dtype=theano.config.floatX)
# id2word = {y:x for x,y in word2id.iteritems()}
# word2vec=load_word2vec()
# rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
init_embeddings=theano.shared(value=np.array(rand_values,dtype=theano.config.floatX), borrow=True) #wrap up the python variable "rand_values" into theano variable
# store_model_to_file('/save/wenpeng/datasets/HypeNet/HyperDef_label_meta_best_para_embeddings', [init_embeddings])
# exit(0)
#now, start to build the input form of the model
sents_ids_l=T.imatrix()
sents_mask_l=T.fmatrix()
sents_ids_r=T.imatrix()
sents_mask_r=T.fmatrix()
word1_ids = T.imatrix()
word2_ids = T.imatrix()
word1_mask = T.fmatrix()
word2_mask = T.fmatrix()
extra = T.fvector()
# labels=T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
def embed_input(emb_matrix, sent_ids):
return emb_matrix[sent_ids.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1)
embed_input_l=embed_input(init_embeddings, sents_ids_l)#embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
embed_input_r=embed_input(init_embeddings, sents_ids_r)#embeddings[sents_ids_r.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1)
embed_word1 = init_embeddings[word1_ids.flatten()].reshape((batch_size,word1_ids.shape[1], emb_size))
embed_word2 = init_embeddings[word2_ids.flatten()].reshape((batch_size,word2_ids.shape[1], emb_size))
word1_embedding = T.sum(embed_word1*word1_mask.dimshuffle(0,1,'x'), axis=1)
word2_embedding = T.sum(embed_word2*word2_mask.dimshuffle(0,1,'x'), axis=1)
'''create_AttentiveConv_params '''
conv_W, conv_b=create_conv_para(rng, filter_shape=(hidden_size[1], 1, emb_size, filter_size[0]))
conv_W_context, conv_b_context=create_conv_para(rng, filter_shape=(hidden_size[1], 1, emb_size, 1))
NN_para=[conv_W, conv_b,conv_W_context]
'''
attentive convolution function
'''
term_vs_term_layer = Conv_for_Pair(rng,
origin_input_tensor3=embed_word1.dimshuffle(0,2,1),
origin_input_tensor3_r = embed_word2.dimshuffle(0,2,1),
input_tensor3=embed_word1.dimshuffle(0,2,1),
input_tensor3_r = embed_word2.dimshuffle(0,2,1),
mask_matrix = word1_mask,
mask_matrix_r = word2_mask,
image_shape=(batch_size, 1, emb_size, max_term_len),
image_shape_r = (batch_size, 1, emb_size, max_term_len),
filter_shape=(hidden_size[1], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[1], 1,emb_size, 1),
W=conv_W, b=conv_b,
W_context=conv_W_context, b_context=conv_b_context)
tt_embeddings_l = term_vs_term_layer.attentive_maxpool_vec_l
tt_embeddings_r = term_vs_term_layer.attentive_maxpool_vec_r
p_ww = T.concatenate([tt_embeddings_l,tt_embeddings_r,tt_embeddings_l*tt_embeddings_r,tt_embeddings_l-tt_embeddings_r], axis=1)
term_vs_def_layer = Conv_for_Pair(rng,
origin_input_tensor3=embed_word1.dimshuffle(0,2,1),
origin_input_tensor3_r = embed_input_r,
input_tensor3=embed_word1.dimshuffle(0,2,1),
input_tensor3_r = embed_input_r,
mask_matrix = word1_mask,
mask_matrix_r = sents_mask_r,
image_shape=(batch_size, 1, emb_size, max_term_len),
image_shape_r = (batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[1], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[1], 1,emb_size, 1),
W=conv_W, b=conv_b,
W_context=conv_W_context, b_context=conv_b_context)
td_embeddings_l = term_vs_def_layer.attentive_maxpool_vec_l
td_embeddings_r = term_vs_def_layer.attentive_maxpool_vec_r
p_wd = T.concatenate([td_embeddings_l,td_embeddings_r,td_embeddings_l*td_embeddings_r,td_embeddings_l-td_embeddings_r], axis=1)
def_vs_term_layer = Conv_for_Pair(rng,
origin_input_tensor3=embed_input_l,
origin_input_tensor3_r = embed_word2.dimshuffle(0,2,1),
input_tensor3=embed_input_l,
input_tensor3_r = embed_word2.dimshuffle(0,2,1),
mask_matrix = sents_mask_l,
mask_matrix_r = word2_mask,
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r = (batch_size, 1, emb_size, max_term_len),
filter_shape=(hidden_size[1], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[1], 1,emb_size, 1),
W=conv_W, b=conv_b,
W_context=conv_W_context, b_context=conv_b_context)
dt_embeddings_l = def_vs_term_layer.attentive_maxpool_vec_l
dt_embeddings_r = def_vs_term_layer.attentive_maxpool_vec_r
p_dw = T.concatenate([dt_embeddings_l,dt_embeddings_r,dt_embeddings_l*dt_embeddings_r,dt_embeddings_l-dt_embeddings_r], axis=1)
def_vs_def_layer = Conv_for_Pair(rng,
origin_input_tensor3=embed_input_l,
origin_input_tensor3_r = embed_input_r,
input_tensor3=embed_input_l,
input_tensor3_r = embed_input_r,
mask_matrix = sents_mask_l,
mask_matrix_r = sents_mask_r,
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r = (batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[1], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[1], 1,emb_size, 1),
W=conv_W, b=conv_b,
W_context=conv_W_context, b_context=conv_b_context)
dd_embeddings_l = def_vs_def_layer.attentive_maxpool_vec_l
dd_embeddings_r = def_vs_def_layer.attentive_maxpool_vec_r
p_dd = T.concatenate([dd_embeddings_l,dd_embeddings_r,dd_embeddings_l*dd_embeddings_r,dd_embeddings_l-dd_embeddings_r], axis=1)
if p_mode == 'conc':
p=T.concatenate([p_ww, p_wd, p_dw, p_dd], axis=1)
p_len = 4*4*hidden_size[1]
else:
p = T.max(T.concatenate([p_ww.dimshuffle('x',0,1),p_wd.dimshuffle('x',0,1),p_dw.dimshuffle('x',0,1),p_dd.dimshuffle('x',0,1)],axis=0), axis=0)
p_len =4*hidden_size[1]
"form input to LR classifier"
LR_input = T.concatenate([p,extra.dimshuffle(0,'x')],axis=1)
LR_input_size=p_len+1
U_a = create_ensemble_para(rng, 2, LR_input_size) # the weight matrix hidden_size*2
LR_b = theano.shared(value=np.zeros((2,),dtype=theano.config.floatX),name='LR_b', borrow=True) #bias for each target class
LR_para=[U_a, LR_b]
layer_LR=LogisticRegression(rng, input=LR_input, n_in=LR_input_size, n_out=2, W=U_a, b=LR_b) #basically it is a multiplication between weight matrix and input feature vector
params = NN_para+LR_para #[init_embeddings]
load_model_from_file('/save/wenpeng/datasets/HypeNet/HyperDef_label_meta_best_para_embeddings', [init_embeddings])
load_model_from_file('/save/wenpeng/datasets/HypeNet/HyperDef_label_meta_best_para_0.938730853392', params)
test_model = theano.function([sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, word1_ids,word2_ids,word1_mask,word2_mask,extra], [layer_LR.y_pred,layer_LR.prop_for_posi], allow_input_downcast=True, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... testing'
n_test_batches=test_size/batch_size
n_test_remain = test_size%batch_size
if n_test_remain!=0:
test_batch_start=list(np.arange(n_test_batches)*batch_size)+[test_size-batch_size]
else:
test_batch_start=list(np.arange(n_test_batches)*batch_size)
# max_acc_dev=0.0
# max_ap_test=0.0
# max_ap_topk_test=0.0
# max_f1=0.0
# cost_i=0.0
# train_indices = range(train_size)
for idd, test_batch_id in enumerate(test_batch_start): # for each test batch
pred_i, prob_i=test_model(
test_sents_l[test_batch_id:test_batch_id+batch_size],
test_masks_l[test_batch_id:test_batch_id+batch_size],
test_sents_r[test_batch_id:test_batch_id+batch_size],
test_masks_r[test_batch_id:test_batch_id+batch_size],
test_word1[test_batch_id:test_batch_id+batch_size],
test_word2[test_batch_id:test_batch_id+batch_size],
test_word1_mask[test_batch_id:test_batch_id+batch_size],
test_word2_mask[test_batch_id:test_batch_id+batch_size],
test_extra[test_batch_id:test_batch_id+batch_size])
print pred_i, prob_i
def evaluate_lenet5(learning_rate=0.1, n_epochs=2000, batch_size=500, test_batch_size=500, emb_size=10, hidden_size=10,
L2_weight=0.0001, margin=0.5,
train_size=4000000, test_size=1000,
max_context_len=25, max_span_len=7, max_q_len=40, max_EM=0.0):
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/SQuAD/';
rng = np.random.RandomState(23455)
word2id,train_questions,train_questions_mask,train_lefts,train_lefts_mask,train_spans,train_spans_mask,train_rights,train_rights_mask=load_SQUAD_hinrich(train_size, max_context_len, max_span_len, max_q_len)
test_ground_truth,all_candidates_f1,test_questions,test_questions_mask,test_lefts,test_lefts_mask,test_spans,test_spans_mask,test_rights,test_rights_mask=load_dev_hinrich(word2id, test_size, max_context_len, max_span_len, max_q_len)
overall_vocab_size=len(word2id)
print 'vocab size:', overall_vocab_size
rand_values=random_value_normal((overall_vocab_size+1, emb_size), theano.config.floatX, np.random.RandomState(1234))
# rand_values[0]=np.array(np.zeros(emb_size),dtype=theano.config.floatX)
# id2word = {y:x for x,y in word2id.iteritems()}
# word2vec=load_word2vec()
# rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings=theano.shared(value=rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
left=T.imatrix() #(2*batch, len)
left_mask=T.fmatrix() #(2*batch, len)
span=T.imatrix() #(2*batch, span_len)
span_mask=T.fmatrix() #(2*batch, span_len)
right=T.imatrix() #(2*batch, len)
right_mask=T.fmatrix() #(2*batch, len)
q=T.imatrix() #(2*batch, len_q)
q_mask=T.fmatrix() #(2*batch, len_q)
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
U1, W1, b1=create_GRU_para(rng, emb_size, hidden_size)
U1_b, W1_b, b1_b=create_GRU_para(rng, emb_size, hidden_size)
GRU1_para=[U1, W1, b1, U1_b, W1_b, b1_b]
U2, W2, b2=create_GRU_para(rng, hidden_size, hidden_size)
U2_b, W2_b, b2_b=create_GRU_para(rng, hidden_size, hidden_size)
GRU2_para=[U2, W2, b2, U2_b, W2_b, b2_b]
W_a1 = create_ensemble_para(rng, hidden_size, hidden_size)# init_weights((2*hidden_size, hidden_size))
W_a2 = create_ensemble_para(rng, hidden_size, hidden_size)
attend_para=[W_a1, W_a2]
params = [embeddings]+GRU1_para+attend_para+GRU2_para
# load_model_from_file(rootPath+'Best_Para_dim'+str(emb_size), params)
left_input = embeddings[left.flatten()].reshape((left.shape[0], left.shape[1], emb_size)).transpose((0, 2,1)) # (2*batch_size, emb_size, len_context)
span_input = embeddings[span.flatten()].reshape((span.shape[0], span.shape[1], emb_size)).transpose((0, 2,1)) # (2*batch_size, emb_size, len_span)
right_input = embeddings[right.flatten()].reshape((right.shape[0], right.shape[1], emb_size)).transpose((0, 2,1)) # (2*batch_size, emb_size, len_context)
q_input = embeddings[q.flatten()].reshape((q.shape[0], q.shape[1], emb_size)).transpose((0, 2,1)) # (2*batch_size, emb_size, len_q)
left_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=left_input, Mask=left_mask, hidden_dim=hidden_size,U=U1,W=W1,b=b1,Ub=U1_b,Wb=W1_b,bb=b1_b)
left_reps=left_model.output_tensor #(batch, emb, para_len)
span_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=span_input, Mask=span_mask, hidden_dim=hidden_size,U=U1,W=W1,b=b1,Ub=U1_b,Wb=W1_b,bb=b1_b)
span_reps=span_model.output_tensor #(batch, emb, para_len)
right_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=right_input, Mask=right_mask, hidden_dim=hidden_size,U=U1,W=W1,b=b1,Ub=U1_b,Wb=W1_b,bb=b1_b)
right_reps=right_model.output_tensor #(batch, emb, para_len)
q_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=q_input, Mask=q_mask, hidden_dim=hidden_size,U=U1,W=W1,b=b1,Ub=U1_b,Wb=W1_b,bb=b1_b)
q_reps=q_model.output_tensor #(batch, emb, para_len)
#interaction
left_reps_via_q_reps, q_reps_via_left_reps=attention_dot_prod_between_2tensors(left_reps, q_reps)
span_reps_via_q_reps, q_reps_via_span_reps=attention_dot_prod_between_2tensors(span_reps, q_reps)
right_reps_via_q_reps, q_reps_via_right_reps=attention_dot_prod_between_2tensors(right_reps, q_reps)
# q_reps_via_left_reps=attention_dot_prod_between_2tensors(q_reps, left_reps)
# q_reps_via_span_reps=attention_dot_prod_between_2tensors(q_reps, span_reps)
# q_reps_via_right_reps=attention_dot_prod_between_2tensors(q_reps, right_reps)
#combine
origin_W=normalize_matrix(W_a1)
attend_W=normalize_matrix(W_a2)
left_origin_reps=T.dot(left_reps.dimshuffle(0, 2,1), origin_W)
span_origin_reps=T.dot(span_reps.dimshuffle(0, 2,1), origin_W)
right_origin_reps=T.dot(right_reps.dimshuffle(0, 2,1), origin_W)
q_origin_reps=T.dot(q_reps.dimshuffle(0, 2,1), origin_W)
left_attend_q_reps=T.dot(q_reps_via_left_reps.dimshuffle(0, 2,1), attend_W)
span_attend_q_reps=T.dot(q_reps_via_span_reps.dimshuffle(0, 2,1), attend_W)
right_attend_q_reps=T.dot(q_reps_via_right_reps.dimshuffle(0, 2,1), attend_W)
q_attend_left_reps=T.dot(left_reps_via_q_reps.dimshuffle(0, 2,1), attend_W)
q_attend_span_reps=T.dot(span_reps_via_q_reps.dimshuffle(0, 2,1), attend_W)
q_attend_right_reps=T.dot(right_reps_via_q_reps.dimshuffle(0, 2,1), attend_W)
add_left=left_origin_reps+q_attend_left_reps #(2*batch, len ,hidden)
add_span=span_origin_reps+q_attend_span_reps
add_right=right_origin_reps+q_attend_right_reps
add_q_by_left=q_origin_reps+left_attend_q_reps
add_q_by_span=q_origin_reps+span_attend_q_reps
add_q_by_right=q_origin_reps+right_attend_q_reps
#second GRU
add_left_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_left.dimshuffle(0,2,1), Mask=left_mask, hidden_dim=hidden_size,U=U2,W=W2,b=b2,Ub=U2_b,Wb=W2_b,bb=b2_b)
add_left_reps=add_left_model.output_sent_rep_maxpooling #(batch, hidden_dim)
add_span_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_span.dimshuffle(0,2,1), Mask=span_mask, hidden_dim=hidden_size,U=U2,W=W2,b=b2,Ub=U2_b,Wb=W2_b,bb=b2_b)
add_span_reps=add_span_model.output_sent_rep_maxpooling #(batch, hidden_dim)
add_right_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_right.dimshuffle(0,2,1), Mask=right_mask, hidden_dim=hidden_size,U=U2,W=W2,b=b2,Ub=U2_b,Wb=W2_b,bb=b2_b)
add_right_reps=add_right_model.output_sent_rep_maxpooling #(batch, hidden_dim)
add_q_by_left_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_q_by_left.dimshuffle(0,2,1), Mask=q_mask, hidden_dim=hidden_size,U=U2,W=W2,b=b2,Ub=U2_b,Wb=W2_b,bb=b2_b)
add_q_by_left_reps=add_q_by_left_model.output_sent_rep_maxpooling #(batch, hidden_dim)
add_q_by_span_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_q_by_span.dimshuffle(0,2,1), Mask=q_mask, hidden_dim=hidden_size,U=U2,W=W2,b=b2,Ub=U2_b,Wb=W2_b,bb=b2_b)
add_q_by_span_reps=add_q_by_span_model.output_sent_rep_maxpooling #(batch, hidden_dim)
add_q_by_right_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_q_by_right.dimshuffle(0,2,1), Mask=q_mask, hidden_dim=hidden_size,U=U2,W=W2,b=b2,Ub=U2_b,Wb=W2_b,bb=b2_b)
add_q_by_right_reps=add_q_by_right_model.output_sent_rep_maxpooling #(batch, hidden_dim)
paragraph_concat=T.concatenate([add_left_reps, add_span_reps, add_right_reps], axis=1) #(batch, 3*hidden)
question_concat=T.concatenate([add_q_by_left_reps, add_q_by_span_reps, add_q_by_right_reps], axis=1) #(batch, 3*hidden)
simi_list=cosine_row_wise_twoMatrix(paragraph_concat, question_concat) #(2*batch)
pos_simi_vec=simi_list[::2]
neg_simi_vec=simi_list[1::2]
raw_loss=T.maximum(0.0, margin+neg_simi_vec-pos_simi_vec)
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
# L2_reg =L2norm_paraList([embeddings,U1, W1, U1_b, W1_b,UQ, WQ , UQ_b, WQ_b, W_a1, W_a2, U_a])
#L2_reg = L2norm_paraList(params)
cost=T.sum(raw_loss)#+ConvGRU_1.error#
accumulator=[]
for para_i in params:
eps_p=np.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / (T.sqrt(acc)+1e-8))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([left, left_mask, span, span_mask, right, right_mask, q, q_mask], cost, updates=updates,on_unused_input='ignore')
test_model = theano.function([left, left_mask, span, span_mask, right, right_mask, q, q_mask], simi_list, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = np.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches=train_size/batch_size #batch_size means how many pairs
remain_train=train_size%batch_size
# train_batch_start=list(np.arange(n_train_batches)*batch_size*2)+[train_size*2-batch_size*2] # always ou shu
if remain_train>0:
train_batch_start=list(np.arange(n_train_batches)*batch_size)+[train_size-batch_size]
else:
train_batch_start=list(np.arange(n_train_batches)*batch_size)
max_F1_acc=0.0
max_exact_acc=0.0
cost_i=0.0
train_odd_ids = list(np.arange(train_size)*2)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.shuffle(train_odd_ids)
iter_accu=0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
if iter%100==0:
print 'iter:', iter
iter_accu+=1
train_id_list=[[train_odd_id, train_odd_id+1] for train_odd_id in train_odd_ids[para_id:para_id+batch_size]]
train_id_list=sum(train_id_list,[])
# print train_id_list
cost_i+= train_model(
np.asarray([train_lefts[id] for id in train_id_list], dtype='int32'),
np.asarray([train_lefts_mask[id] for id in train_id_list], dtype=theano.config.floatX),
np.asarray([train_spans[id] for id in train_id_list], dtype='int32'),
np.asarray([train_spans_mask[id] for id in train_id_list], dtype=theano.config.floatX),
np.asarray([train_rights[id] for id in train_id_list], dtype='int32'),
np.asarray([train_rights_mask[id] for id in train_id_list], dtype=theano.config.floatX),
np.asarray([train_questions[id] for id in train_id_list], dtype='int32'),
np.asarray([train_questions_mask[id] for id in train_id_list], dtype=theano.config.floatX))
#print iter
if iter%100==0:
print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
print 'Testing...'
past_time = time.time()
exact_match=0.0
F1_match=0.0
for test_pair_id in range(test_size):
test_example_lefts=test_lefts[test_pair_id]
test_example_lefts_mask=test_lefts_mask[test_pair_id]
test_example_spans=test_spans[test_pair_id]
test_example_spans_mask=test_spans_mask[test_pair_id]
test_example_rights=test_rights[test_pair_id]
test_example_rights_mask=test_rights_mask[test_pair_id]
test_example_questions=test_questions[test_pair_id]
test_example_questions_mask=test_questions_mask[test_pair_id]
test_example_candidates_f1=all_candidates_f1[test_pair_id]
test_example_size=len(test_example_lefts)
# print 'test_pair_id, test_example_size:', test_pair_id, test_example_size
if test_example_size < test_batch_size:
#pad
pad_size=test_batch_size-test_example_size
test_example_lefts+=test_example_lefts[-1:]*pad_size
test_example_lefts_mask+=test_example_lefts_mask[-1:]*pad_size
test_example_spans+=test_example_spans[-1:]*pad_size
test_example_spans_mask+=test_example_spans_mask[-1:]*pad_size
test_example_rights+=test_example_rights[-1:]*pad_size
test_example_rights_mask+=test_example_rights_mask[-1:]*pad_size
test_example_questions+=test_example_questions[-1:]*pad_size
test_example_questions_mask+=test_example_questions_mask[-1:]*pad_size
test_example_candidates_f1+=test_example_candidates_f1[-1:]*pad_size
test_example_size=test_batch_size
n_test_batches=test_example_size/test_batch_size
n_test_remain=test_example_size%test_batch_size
if n_test_remain > 0:
test_batch_start=list(np.arange(n_test_batches)*test_batch_size)+[test_example_size-test_batch_size]
else:
test_batch_start=list(np.arange(n_test_batches)*test_batch_size)
all_simi_list=[]
all_cand_list=[]
for test_para_id in test_batch_start:
simi_return_vector=test_model(
np.asarray(test_example_lefts[test_para_id:test_para_id+test_batch_size], dtype='int32'),
np.asarray(test_example_lefts_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
np.asarray(test_example_spans[test_para_id:test_para_id+test_batch_size], dtype='int32'),
np.asarray(test_example_spans_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
np.asarray(test_example_rights[test_para_id:test_para_id+test_batch_size], dtype='int32'),
np.asarray(test_example_rights_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
np.asarray(test_example_questions[test_para_id:test_para_id+test_batch_size], dtype='int32'),
np.asarray(test_example_questions_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX))
candidate_f1_list=test_example_candidates_f1[test_para_id:test_para_id+test_batch_size]
all_simi_list+=list(simi_return_vector)
all_cand_list+=candidate_f1_list
top1_f1=all_cand_list[np.argsort(all_simi_list)[-1]]
# print top1_cand, test_ground_truth[test_pair_id]
if top1_f1 == 1.0:
exact_match+=1
# F1=macrof1(top1_cand, test_ground_truth[test_pair_id])
# print '\t\t\t', F1
F1_match+=top1_f1
# match_amount=len(pred_ans_set & q_gold_ans_set)
# # print 'q_gold_ans_set:', q_gold_ans_set
# # print 'pred_ans_set:', pred_ans_set
# if match_amount>0:
# exact_match+=match_amount*1.0/len(pred_ans_set)
F1_acc=F1_match/test_size
exact_acc=exact_match/test_size
if F1_acc> max_F1_acc:
max_F1_acc=F1_acc
# store_model_to_file(params, emb_size)
if exact_acc> max_exact_acc:
max_exact_acc=exact_acc
if max_exact_acc > max_EM:
store_model_to_file(rootPath+'Best_Para_'+str(max_exact_acc), params)
print 'Finished storing best params at:', max_exact_acc
print 'current average F1:', F1_acc, '\t\tmax F1:', max_F1_acc, 'current exact:', exact_acc, '\t\tmax exact_acc:', max_exact_acc
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.01,
n_epochs=100,
emb_size=40,
batch_size=50,
filter_size=[3, 5],
maxSentLen=100,
hidden_size=[300, 300]):
model_options = locals().copy()
print "model options", model_options
emb_root = '/save/wenpeng/datasets/LORELEI/multi-lingual-emb/'
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
srng = T.shared_randomstreams.RandomStreams(rng.randint(seed))
all_sentences, all_masks, all_labels, word2id = load_reliefweb_il5_12_multilabel(
maxlen=maxSentLen
) #minlen, include one label, at least one word in the sentence
train_sents = np.asarray(all_sentences[0], dtype='int32')
train_masks = np.asarray(all_masks[0], dtype=theano.config.floatX)
train_labels = np.asarray(all_labels[0], dtype='int32')
train_size = len(train_labels)
# dev_sents=all_sentences[1]
# dev_masks=all_masks[1]
# dev_labels=all_labels[1]
# dev_size=len(dev_labels)
test_sents = np.asarray(all_sentences[2], dtype='int32')
test_masks = np.asarray(all_masks[2], dtype=theano.config.floatX)
test_labels = np.asarray(all_labels[2], dtype='int32')
test_size = len(test_labels)
vocab_size = len(word2id) + 1 # add one zero pad index
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
#here, we leave code for loading word2vec to initialize words
rand_values[0] = np.array(np.zeros(emb_size), dtype=theano.config.floatX)
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_fasttext_multiple_word2vec_given_file([
emb_root + 'IL5-cca-wiki-lorelei-d40.eng.vec',
emb_root + 'IL5-cca-wiki-lorelei-d40.IL5.vec'
], 40)
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
#now, start to build the input form of the model
sents_id_matrix = T.imatrix('sents_id_matrix')
sents_mask = T.fmatrix('sents_mask')
labels = T.imatrix('labels')
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
common_input = embeddings[sents_id_matrix.flatten()].reshape(
(batch_size, maxSentLen, emb_size)).dimshuffle(
0, 2, 1) #the input format can be adapted into CNN or GRU or LSTM
bow_emb = T.sum(common_input * sents_mask.dimshuffle(0, 'x', 1), axis=2)
# conv_W, conv_b=create_conv_para(rng, filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]))
# conv_W2, conv_b2=create_conv_para(rng, filter_shape=(hidden_size[0], 1, emb_size, filter_size[1]))
# NN_para = [conv_W, conv_b, conv_W2, conv_b2]
# conv_model = Conv_with_Mask(rng, input_tensor3=common_input,
# mask_matrix = sents_mask,
# image_shape=(batch_size, 1, emb_size, maxSentLen),
# filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]), W=conv_W, b=conv_b) #mutiple mask with the conv_out to set the features by UNK to zero
# sent_embeddings=conv_model.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
#
# conv_model2 = Conv_with_Mask(rng, input_tensor3=common_input,
# mask_matrix = sents_mask,
# image_shape=(batch_size, 1, emb_size, maxSentLen),
# filter_shape=(hidden_size[0], 1, emb_size, filter_size[1]), W=conv_W2, b=conv_b2) #mutiple mask with the conv_out to set the features by UNK to zero
# sent_embeddings2=conv_model2.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
#
# LR_input = T.concatenate([sent_embeddings,sent_embeddings2, bow_emb], axis=1)
# LR_input_size = hidden_size[0]*2+emb_size
# #classification layer, it is just mapping from a feature vector of size "hidden_size" to a vector of only two values: positive, negative
# U_a = create_ensemble_para(rng, 8, LR_input_size) # the weight matrix hidden_size*2
# LR_b = theano.shared(value=np.zeros((8,),dtype=theano.config.floatX),name='LR_b', borrow=True) #bias for each target class
# LR_para=[U_a, LR_b]
# layer_LR=LogisticRegression(rng, input=LR_input, n_in=LR_input_size, n_out=8, W=U_a, b=LR_b) #basically it is a multiplication between weight matrix and input feature vector
# score_matrix = T.nnet.sigmoid(layer_LR.before_softmax) #batch * 8
# sub_labels = labels[:,:8]
# prob_pos = T.where( sub_labels < 1, 1.0-score_matrix, score_matrix)
# loss = -T.mean(T.log(prob_pos))
'''
GRU
'''
U1, W1, b1 = create_GRU_para(rng, emb_size, hidden_size[0])
GRU_NN_para = [
U1, W1, b1
] #U1 includes 3 matrices, W1 also includes 3 matrices b1 is bias
# gru_input = common_input.dimshuffle((0,2,1)) #gru requires input (batch_size, emb_size, maxSentLen)
gru_layer = GRU_Batch_Tensor_Input_with_Mask(common_input, sents_mask,
hidden_size[0], U1, W1, b1)
gru_sent_embeddings = gru_layer.maxpooling_vec #gru_layer.output_sent_rep # (batch_size, hidden_size)
LR_att_input = gru_sent_embeddings #T.concatenate([gru_sent_embeddings, bow_emb], axis=1)
LR_att_input_size = hidden_size[0]
#classification layer, it is just mapping from a feature vector of size "hidden_size" to a vector of only two values: positive, negative
U_att_a = create_ensemble_para(
rng, 8, LR_att_input_size) # the weight matrix hidden_size*2
LR_att_b = theano.shared(value=np.zeros((8, ), dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
LR_att_para = [U_att_a, LR_att_b]
layer_att_LR = LogisticRegression(
rng,
input=LR_att_input,
n_in=LR_att_input_size,
n_out=8,
W=U_att_a,
b=LR_att_b
) #basically it is a multiplication between weight matrix and input feature vector
att_score_matrix = T.nnet.sigmoid(layer_att_LR.before_softmax) #batch * 12
sub_labels = labels[:, :8]
att_prob_pos = T.where(sub_labels < 1, 1.0 - att_score_matrix,
att_score_matrix)
att_loss = -T.mean(T.log(att_prob_pos))
params = GRU_NN_para + LR_att_para # put all model parameters together
cost = att_loss #+Div_reg*diversify_reg#+L2_weight*L2_reg
updates = Gradient_Cost_Para(cost, params, learning_rate)
'''
testing
'''
binarize_prob = T.where(att_score_matrix > 0.3, 1, 0)
#train_model = theano.function([sents_id_matrix, sents_mask, labels], cost, updates=updates, on_unused_input='ignore')
train_model = theano.function([sents_id_matrix, sents_mask, labels],
cost,
updates=updates,
allow_input_downcast=True,
on_unused_input='ignore')
# dev_model = theano.function([sents_id_matrix, sents_mask, labels], layer_LR.errors(labels), allow_input_downcast=True, on_unused_input='ignore')
test_model = theano.function([sents_id_matrix, sents_mask],
binarize_prob,
allow_input_downcast=True,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
n_train_batches = train_size / batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
# n_dev_batches=dev_size/batch_size
# dev_batch_start=list(np.arange(n_dev_batches)*batch_size)+[dev_size-batch_size]
n_test_batches = test_size / batch_size
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
max_meanf1_test = 0.0
max_weightf1_test = 0.0
train_indices = range(train_size)
while epoch < n_epochs:
epoch = epoch + 1
random.Random(100).shuffle(train_indices)
iter_accu = 0
cost_i = 0.0
for batch_id in train_batch_start: #for each batch
# iter means how many batches have been run, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
train_id_batch = train_indices[batch_id:batch_id + batch_size]
# labels_matrix = []
# for lab in train_labels[train_id_batch]:
# vec = [0]*8
# vec[lab]=1
# labels_matrix.append(vec)
# labels_matrix = np.asarray(labels_matrix, dtype='int32')
cost_i += train_model(train_sents[train_id_batch],
train_masks[train_id_batch],
train_labels[train_id_batch])
#after each 1000 batches, we test the performance of the model on all test data
if iter % 20 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
past_time = time.time()
# error_sum=0.0
# for test_batch_id in test_batch_start: # for each test batch
# error_i, pred_labels=test_model(
# test_sents[test_batch_id:test_batch_id+batch_size],
# test_masks[test_batch_id:test_batch_id+batch_size])
# pred_labels=list(pred_labels)
# error_sum+=error_i
#
# test_accuracy=1.0-error_sum/(len(test_batch_start))
# if test_accuracy > max_acc_test:
# max_acc_test=test_accuracy
# print '\t\t\t\t\t\t\t\tcurrent testbacc:', test_accuracy, '\t\tmax_acc_test:', max_acc_test
error_sum = 0.0
all_pred_labels = []
all_gold_labels = []
for test_batch_id in test_batch_start: # for each test batch
pred_labels = test_model(
test_sents[test_batch_id:test_batch_id + batch_size],
test_masks[test_batch_id:test_batch_id + batch_size])
gold_labels = test_labels[test_batch_id:test_batch_id +
batch_size]
# gold_labels_matrix = []
# for lab in gold_labels:
# vec = [0]*8
# vec[lab]=1
# gold_labels_matrix.append(vec)
# gold_labels_matrix = np.asarray(gold_labels_matrix, dtype='int32')
all_pred_labels.append(pred_labels)
all_gold_labels.append(gold_labels)
all_pred_labels = np.concatenate(all_pred_labels)
all_gold_labels = np.concatenate(all_gold_labels)
test_mean_f1, test_weight_f1 = average_f1_two_array_by_col_firstK(
all_pred_labels, all_gold_labels, 8)
if test_weight_f1 > max_weightf1_test:
max_weightf1_test = test_weight_f1
if test_mean_f1 > max_meanf1_test:
max_meanf1_test = test_mean_f1
print '\t\t\t\t\t\t\t\tcurrent f1s:', test_mean_f1, test_weight_f1, '\t\tmax_f1:', max_meanf1_test, max_weightf1_test
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.02, n_epochs=4, L2_weight=1e-5, extra_size=4, emb_size=300, batch_size=100, filter_size=[3,3], maxSentLen=40, hidden_size=[300,300], max_term_len=4, p_mode = 'conc'):
model_options = locals().copy()
print "model options", model_options
seed=1234
np.random.seed(seed)
rng = np.random.RandomState(seed) #random seed, control the model generates the same results
# all_sentences_l, all_masks_l, all_sentences_r, all_masks_r, all_word1,all_word2,all_word1_mask,all_word2_mask,all_labels, all_extra, word2id =load_wordnet_hyper_vs_all_with_words(maxlen=maxSentLen, wordlen=max_term_len) #minlen, include one label, at least one word in the sentence
# test_sents_l, test_masks_l, test_sents_r, test_masks_r, test_labels, word2id =load_ACE05_dataset(maxSentLen, word2id)
word2id = load_word2id(root_dic+'LenciBenotto_word2id.pkl')
test_sents_l, test_masks_l, test_sents_r, test_masks_r, test_word1,test_word2,test_word1_mask,test_word2_mask,test_labels, test_extra, word2id, group_size_list = load_task_hyper_vs_all_with_allDefComb(LenciBenotto_file,maxSentLen, word2id, wordlen=max_term_len)
test_sents_l=np.asarray(test_sents_l, dtype='int32')
test_masks_l=np.asarray(test_masks_l, dtype=theano.config.floatX)
test_sents_r=np.asarray(test_sents_r, dtype='int32')
test_masks_r=np.asarray(test_masks_r, dtype=theano.config.floatX)
test_word1=np.asarray(test_word1, dtype='int32')
test_word2=np.asarray(test_word2, dtype='int32')
test_word1_mask=np.asarray(test_word1_mask, dtype=theano.config.floatX)
test_word2_mask=np.asarray(test_word2_mask, dtype=theano.config.floatX)
test_labels_store=np.asarray(test_labels, dtype='int32')
test_extra=np.asarray(test_extra, dtype=theano.config.floatX)
# train_size=len(train_labels_store)
# dev_size=len(dev_labels_store)
test_size=len(test_sents_l)
print ' test size: ', test_size
vocab_size=len(word2id)+1
rand_values=rng.normal(0.0, 0.01, (vocab_size, emb_size)) #generate a matrix by Gaussian distribution
rand_values[0]=np.array(np.zeros(emb_size),dtype=theano.config.floatX)
id2word = {y:x for x,y in word2id.iteritems()}
word2vec=load_word2vec()
rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
init_embeddings=theano.shared(value=np.array(rand_values,dtype=theano.config.floatX), borrow=True) #wrap up the python variable "rand_values" into theano variable
# load_model_from_file(root_dic+'Weeds_best_para_init_embeddings', [init_embeddings])
#now, start to build the input form of the model
sents_ids_l=T.imatrix()
sents_mask_l=T.fmatrix()
sents_ids_r=T.imatrix()
sents_mask_r=T.fmatrix()
word1_ids = T.imatrix()
word2_ids = T.imatrix()
word1_mask = T.fmatrix()
word2_mask = T.fmatrix()
extra = T.fvector()
labels=T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
def embed_input(emb_matrix, sent_ids):
return emb_matrix[sent_ids.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1)
embed_input_l=embed_input(init_embeddings, sents_ids_l)#embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
embed_input_r=embed_input(init_embeddings, sents_ids_r)#embeddings[sents_ids_r.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1)
embed_word1 = init_embeddings[word1_ids.flatten()].reshape((batch_size,word1_ids.shape[1], emb_size))
embed_word2 = init_embeddings[word2_ids.flatten()].reshape((batch_size,word2_ids.shape[1], emb_size))
word1_embedding = T.sum(embed_word1*word1_mask.dimshuffle(0,1,'x'), axis=1)
word2_embedding = T.sum(embed_word2*word2_mask.dimshuffle(0,1,'x'), axis=1)
'''create_AttentiveConv_params '''
conv_W, conv_b=create_conv_para(rng, filter_shape=(hidden_size[1], 1, emb_size, filter_size[0]))
conv_W_context, conv_b_context=create_conv_para(rng, filter_shape=(hidden_size[1], 1, emb_size, 1))
NN_para=[conv_W, conv_b,conv_W_context]
'''
attentive convolution function
'''
term_vs_term_layer = Conv_for_Pair(rng,
origin_input_tensor3=embed_word1.dimshuffle(0,2,1),
origin_input_tensor3_r = embed_word2.dimshuffle(0,2,1),
input_tensor3=embed_word1.dimshuffle(0,2,1),
input_tensor3_r = embed_word2.dimshuffle(0,2,1),
mask_matrix = word1_mask,
mask_matrix_r = word2_mask,
image_shape=(batch_size, 1, emb_size, max_term_len),
image_shape_r = (batch_size, 1, emb_size, max_term_len),
filter_shape=(hidden_size[1], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[1], 1,emb_size, 1),
W=conv_W, b=conv_b,
W_context=conv_W_context, b_context=conv_b_context)
tt_embeddings_l = term_vs_term_layer.attentive_maxpool_vec_l
tt_embeddings_r = term_vs_term_layer.attentive_maxpool_vec_r
p_ww = T.concatenate([tt_embeddings_l,tt_embeddings_r,tt_embeddings_l*tt_embeddings_r,tt_embeddings_l-tt_embeddings_r], axis=1)
term_vs_def_layer = Conv_for_Pair(rng,
origin_input_tensor3=embed_word1.dimshuffle(0,2,1),
origin_input_tensor3_r = embed_input_r,
input_tensor3=embed_word1.dimshuffle(0,2,1),
input_tensor3_r = embed_input_r,
mask_matrix = word1_mask,
mask_matrix_r = sents_mask_r,
image_shape=(batch_size, 1, emb_size, max_term_len),
image_shape_r = (batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[1], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[1], 1,emb_size, 1),
W=conv_W, b=conv_b,
W_context=conv_W_context, b_context=conv_b_context)
td_embeddings_l = term_vs_def_layer.attentive_maxpool_vec_l
td_embeddings_r = term_vs_def_layer.attentive_maxpool_vec_r
p_wd = T.concatenate([td_embeddings_l,td_embeddings_r,td_embeddings_l*td_embeddings_r,td_embeddings_l-td_embeddings_r], axis=1)
def_vs_term_layer = Conv_for_Pair(rng,
origin_input_tensor3=embed_input_l,
origin_input_tensor3_r = embed_word2.dimshuffle(0,2,1),
input_tensor3=embed_input_l,
input_tensor3_r = embed_word2.dimshuffle(0,2,1),
mask_matrix = sents_mask_l,
mask_matrix_r = word2_mask,
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r = (batch_size, 1, emb_size, max_term_len),
filter_shape=(hidden_size[1], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[1], 1,emb_size, 1),
W=conv_W, b=conv_b,
W_context=conv_W_context, b_context=conv_b_context)
dt_embeddings_l = def_vs_term_layer.attentive_maxpool_vec_l
dt_embeddings_r = def_vs_term_layer.attentive_maxpool_vec_r
p_dw = T.concatenate([dt_embeddings_l,dt_embeddings_r,dt_embeddings_l*dt_embeddings_r,dt_embeddings_l-dt_embeddings_r], axis=1)
def_vs_def_layer = Conv_for_Pair(rng,
origin_input_tensor3=embed_input_l,
origin_input_tensor3_r = embed_input_r,
input_tensor3=embed_input_l,
input_tensor3_r = embed_input_r,
mask_matrix = sents_mask_l,
mask_matrix_r = sents_mask_r,
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r = (batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[1], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[1], 1,emb_size, 1),
W=conv_W, b=conv_b,
W_context=conv_W_context, b_context=conv_b_context)
dd_embeddings_l = def_vs_def_layer.attentive_maxpool_vec_l
dd_embeddings_r = def_vs_def_layer.attentive_maxpool_vec_r
p_dd = T.concatenate([dd_embeddings_l,dd_embeddings_r,dd_embeddings_l*dd_embeddings_r,dd_embeddings_l-dd_embeddings_r], axis=1)
if p_mode == 'conc':
p=T.concatenate([p_ww, p_wd, p_dw, p_dd], axis=1)
p_len = 4*4*hidden_size[1]
else:
p = T.max(T.concatenate([p_ww.dimshuffle('x',0,1),p_wd.dimshuffle('x',0,1),p_dw.dimshuffle('x',0,1),p_dd.dimshuffle('x',0,1)],axis=0), axis=0)
p_len =4*hidden_size[1]
# HL_input = T.concatenate([p,cosine_matrix1_matrix2_rowwise(word1_embedding,word2_embedding).dimshuffle(0,'x'),extra.dimshuffle(0,'x')],axis=1)
# HL_input_size=p_len+1+1
#
# HL_layer_1=HiddenLayer(rng, input=HL_input, n_in=HL_input_size, n_out=hidden_size[1], activation=T.tanh)
"form input to LR classifier"
LR_input = T.concatenate([p,cosine_matrix1_matrix2_rowwise(word1_embedding,word2_embedding).dimshuffle(0,'x'),extra.dimshuffle(0,'x')],axis=1)
LR_input_size=p_len+1+1
# LR_input = HL_layer_1.output
# LR_input_size = hidden_size[1]
U_a = create_ensemble_para(rng, 2, LR_input_size) # the weight matrix hidden_size*2
LR_b = theano.shared(value=np.zeros((2,),dtype=theano.config.floatX),name='LR_b', borrow=True) #bias for each target class
LR_para=[U_a, LR_b]
layer_LR=LogisticRegression(rng, input=LR_input, n_in=LR_input_size, n_out=2, W=U_a, b=LR_b, bias=0.25) #basically it is a multiplication between weight matrix and input feature vector
loss=layer_LR.negative_log_likelihood(labels) #for classification task, we usually used negative log likelihood as loss, the lower the better.
# L2_reg = (conv_W**2).sum()+(conv_W_context**2).sum()+(U_a**2).sum()
params = NN_para+LR_para #[init_embeddings]
# load_model_from_file('/save/wenpeng/datasets/HypeNet/HyperDef_label_meta_best_para_0.938730853392', params)
load_model_from_file(root_dic+'LenciBenotto_best_para_0.557286573332', params)
'''
0.552587544259; current ap: 0.574037513126 ap@100 0.918481316424
0.557286573332; current ap: 0.576498645289 ap@100 0.909032657538
'''
test_model = theano.function([sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, word1_ids,word2_ids,word1_mask,word2_mask,extra], [layer_LR.y_pred,layer_LR.prop_for_posi], allow_input_downcast=True, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
n_test_batches=test_size/batch_size
n_test_remain = test_size%batch_size
if n_test_remain!=0:
test_batch_start=list(np.arange(n_test_batches)*batch_size)+[test_size-batch_size]
else:
test_batch_start=list(np.arange(n_test_batches)*batch_size)
# max_acc_dev=0.0
max_ap_test=0.0
max_ap_topk_test=0.0
max_f1=0.0
pred_labels =[]
probs = []
gold_labels =[]
error_sum=0.0
for idd, test_batch_id in enumerate(test_batch_start): # for each test batch
pred_i, prob_i=test_model(
test_sents_l[test_batch_id:test_batch_id+batch_size],
test_masks_l[test_batch_id:test_batch_id+batch_size],
test_sents_r[test_batch_id:test_batch_id+batch_size],
test_masks_r[test_batch_id:test_batch_id+batch_size],
test_word1[test_batch_id:test_batch_id+batch_size],
test_word2[test_batch_id:test_batch_id+batch_size],
test_word1_mask[test_batch_id:test_batch_id+batch_size],
test_word2_mask[test_batch_id:test_batch_id+batch_size],
test_extra[test_batch_id:test_batch_id+batch_size])
# error_sum+=error_i
pred_labels+=list(pred_i)
probs+=list(prob_i)
print len(test_sents_l), len(probs)
if n_test_remain !=0:
probs = probs[:(len(test_batch_start)-1)*batch_size]+probs[-n_test_remain:]
print len(test_sents_l), len(probs)
assert len(test_sents_l) == len(probs)
assert sum(group_size_list) == len(probs)
#max prob in group
max_probs = []
prior_size = 0
for i in range(len(group_size_list)):
sub_probs = probs[prior_size:prior_size+group_size_list[i]]
prior_size += group_size_list[i]
max_probs.append(max(sub_probs))
print len(group_size_list),len(max_probs),len(test_labels)
assert len(test_labels) == len(max_probs)
# test_acc=1.0-error_sum/(len(test_batch_start))
test_ap = apk(test_labels, max_probs, k=len(test_labels))
test_ap_top100 = apk(test_labels, max_probs, k=100)
# if test_ap > max_ap_test:
# max_ap_test=test_ap
# store_model_to_file('/save/wenpeng/datasets/EVALution/HyperDef_label_4ways_conc_test_on_EVA_allDefComb_best_para_'+str(max_ap_test), params)
# if test_ap_top100 > max_ap_topk_test:
# max_ap_topk_test=test_ap_top100
print '\t\tcurrent ap:', test_ap,'ap@100', test_ap_top100
def evaluate_lenet5(learning_rate=0.02,
n_epochs=4,
L2_weight=0.0000001,
extra_size=4,
emb_size=300,
batch_size=50,
filter_size=[3, 5],
maxSentLen=60,
hidden_size=[300, 300]):
model_options = locals().copy()
print "model options", model_options
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
all_sentences_l, all_masks_l, all_sentences_r, all_masks_r, all_labels, word2id = load_SNLI_dataset(
maxlen=maxSentLen
) #minlen, include one label, at least one word in the sentence
test_sents_l, test_masks_l, test_sents_r, test_masks_r, test_labels, word2id = load_NYT_dataset(
maxSentLen, word2id)
train_sents_l = np.asarray(all_sentences_l[0], dtype='int32')
dev_sents_l = np.asarray(all_sentences_l[1], dtype='int32')
test_sents_l = np.asarray(test_sents_l, dtype='int32')
train_masks_l = np.asarray(all_masks_l[0], dtype=theano.config.floatX)
dev_masks_l = np.asarray(all_masks_l[1], dtype=theano.config.floatX)
test_masks_l = np.asarray(test_masks_l, dtype=theano.config.floatX)
train_sents_r = np.asarray(all_sentences_r[0], dtype='int32')
dev_sents_r = np.asarray(all_sentences_r[1], dtype='int32')
test_sents_r = np.asarray(test_sents_r, dtype='int32')
train_masks_r = np.asarray(all_masks_r[0], dtype=theano.config.floatX)
dev_masks_r = np.asarray(all_masks_r[1], dtype=theano.config.floatX)
test_masks_r = np.asarray(test_masks_r, dtype=theano.config.floatX)
train_labels_store = np.asarray(all_labels[0], dtype='int32')
dev_labels_store = np.asarray(all_labels[1], dtype='int32')
test_labels_store = np.asarray(test_labels, dtype='int32')
train_size = len(train_labels_store)
dev_size = len(dev_labels_store)
test_size = len(test_labels_store)
print 'train size: ', train_size, ' dev size: ', dev_size, ' test size: ', test_size
vocab_size = len(word2id) + 1
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
#here, we leave code for loading word2vec to initialize words
rand_values[0] = np.array(np.zeros(emb_size), dtype=theano.config.floatX)
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_word2vec()
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
init_embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
#now, start to build the input form of the model
sents_ids_l = T.imatrix()
sents_mask_l = T.fmatrix()
sents_ids_r = T.imatrix()
sents_mask_r = T.fmatrix()
labels = T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
def embed_input(emb_matrix, sent_ids):
return emb_matrix[sent_ids.flatten()].reshape(
(batch_size, maxSentLen, emb_size)).dimshuffle(0, 2, 1)
embed_input_l = embed_input(
init_embeddings, sents_ids_l
) #embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
embed_input_r = embed_input(
init_embeddings, sents_ids_r
) #embeddings[sents_ids_r.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1)
'''create_AttentiveConv_params '''
conv_W, conv_b = create_conv_para(rng,
filter_shape=(hidden_size[1], 1,
hidden_size[0],
filter_size[0]))
conv_W_context, conv_b_context = create_conv_para(
rng, filter_shape=(hidden_size[1], 1, hidden_size[0], 1))
conv_W_2, conv_b_2 = create_conv_para(rng,
filter_shape=(hidden_size[1], 1,
hidden_size[0],
filter_size[1]))
conv_W_context_2, conv_b_context_2 = create_conv_para(
rng, filter_shape=(hidden_size[1], 1, hidden_size[0], 1))
NN_para = [
conv_W, conv_b, conv_W_context, conv_W_2, conv_b_2, conv_W_context_2
]
'''
attentive convolution function
'''
attentive_conv_layer = Conv_for_Pair(
rng,
origin_input_tensor3=embed_input_l,
origin_input_tensor3_r=embed_input_r,
input_tensor3=embed_input_l,
input_tensor3_r=embed_input_r,
mask_matrix=sents_mask_l,
mask_matrix_r=sents_mask_r,
image_shape=(batch_size, 1, hidden_size[0], maxSentLen),
image_shape_r=(batch_size, 1, hidden_size[0], maxSentLen),
filter_shape=(hidden_size[1], 1, hidden_size[0], filter_size[0]),
filter_shape_context=(hidden_size[1], 1, hidden_size[0], 1),
W=conv_W,
b=conv_b,
W_context=conv_W_context,
b_context=conv_b_context)
attentive_sent_embeddings_l = attentive_conv_layer.attentive_maxpool_vec_l
attentive_sent_embeddings_r = attentive_conv_layer.attentive_maxpool_vec_r
attentive_conv_layer_2 = Conv_for_Pair(
rng,
origin_input_tensor3=embed_input_l,
origin_input_tensor3_r=embed_input_r,
input_tensor3=embed_input_l,
input_tensor3_r=embed_input_r,
mask_matrix=sents_mask_l,
mask_matrix_r=sents_mask_r,
image_shape=(batch_size, 1, hidden_size[0], maxSentLen),
image_shape_r=(batch_size, 1, hidden_size[0], maxSentLen),
filter_shape=(hidden_size[1], 1, hidden_size[0], filter_size[1]),
filter_shape_context=(hidden_size[1], 1, hidden_size[0], 1),
W=conv_W_2,
b=conv_b_2,
W_context=conv_W_context_2,
b_context=conv_b_context_2)
attentive_sent_embeddings_l_2 = attentive_conv_layer_2.attentive_maxpool_vec_l
attentive_sent_embeddings_r_2 = attentive_conv_layer_2.attentive_maxpool_vec_r
#form input to HL layers
HL_layer_1_input = T.concatenate([
attentive_sent_embeddings_l, attentive_sent_embeddings_r,
attentive_sent_embeddings_l * attentive_sent_embeddings_r,
attentive_sent_embeddings_l_2, attentive_sent_embeddings_r_2,
attentive_sent_embeddings_l_2 * attentive_sent_embeddings_r_2
],
axis=1)
HL_layer_1_input_size = 6 * hidden_size[1]
HL_layer_1 = HiddenLayer(rng,
input=HL_layer_1_input,
n_in=HL_layer_1_input_size,
n_out=hidden_size[1],
activation=T.nnet.relu)
HL_layer_2 = HiddenLayer(rng,
input=HL_layer_1.output,
n_in=hidden_size[1],
n_out=hidden_size[1],
activation=T.nnet.relu)
# LR_input_size=HL_layer_1_input_size+2*hidden_size[0]
"form input to LR classifier"
LR_input = T.tanh(
T.concatenate([HL_layer_1_input, HL_layer_1.output, HL_layer_2.output],
axis=1))
LR_input_size = HL_layer_1_input_size + 2 * hidden_size[1]
U_a = create_ensemble_para(
rng, 3, LR_input_size) # the weight matrix hidden_size*2
LR_b = theano.shared(value=np.zeros((3, ), dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
LR_para = [U_a, LR_b]
layer_LR = LogisticRegression(
rng, input=LR_input, n_in=LR_input_size, n_out=3, W=U_a, b=LR_b
) #basically it is a multiplication between weight matrix and input feature vector
loss = layer_LR.negative_log_likelihood(
labels
) #for classification task, we usually used negative log likelihood as loss, the lower the better.
'''
testing
'''
test_preds = T.argmax(layer_LR.p_y_given_x, axis=1)
transfered_preds = T.eq(test_preds, 2)
test_error = T.mean(T.neq(transfered_preds, labels))
params = [init_embeddings
] + NN_para + HL_layer_1.params + HL_layer_2.params + LR_para
cost = loss
updates = Gradient_Cost_Para(cost, params, learning_rate)
train_model = theano.function(
[sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels],
cost,
updates=updates,
allow_input_downcast=True,
on_unused_input='ignore')
test_model = theano.function(
[sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels],
[test_error, transfered_preds],
allow_input_downcast=True,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
n_train_batches = train_size / batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
n_dev_batches = dev_size / batch_size
dev_batch_start = list(
np.arange(n_dev_batches) * batch_size) + [dev_size - batch_size]
n_test_batches = test_size / batch_size
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
max_acc_dev = 0.0
max_acc_test = 0.0
max_f1 = 0.0
cost_i = 0.0
train_indices = range(train_size)
while epoch < n_epochs:
epoch = epoch + 1
random.Random(100).shuffle(
train_indices
) #shuffle training set for each new epoch, is supposed to promote performance, but not garrenteed
iter_accu = 0
for batch_id in train_batch_start: #for each batch
# iter means how many batches have been run, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
train_id_batch = train_indices[batch_id:batch_id + batch_size]
cost_i += train_model(train_sents_l[train_id_batch],
train_masks_l[train_id_batch],
train_sents_r[train_id_batch],
train_masks_r[train_id_batch],
train_labels_store[train_id_batch])
#after each 1000 batches, we test the performance of the model on all test data
if iter % 100 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
past_time = time.time()
pred_labels = []
gold_labels = []
error_sum = 0.0
for idd, test_batch_id in enumerate(
test_batch_start): # for each test batch
error_i, pred_labels_i = test_model(
test_sents_l[test_batch_id:test_batch_id + batch_size],
test_masks_l[test_batch_id:test_batch_id + batch_size],
test_sents_r[test_batch_id:test_batch_id + batch_size],
test_masks_r[test_batch_id:test_batch_id + batch_size],
test_labels_store[test_batch_id:test_batch_id +
batch_size])
error_sum += error_i
pred_labels += list(pred_labels_i)
gold_labels += list(
test_labels_store[test_batch_id:test_batch_id +
batch_size])
test_acc = 1.0 - error_sum / (len(test_batch_start))
test_f1 = f1_score_2_binary_list(
gold_labels, pred_labels) #, average='binary')
if test_acc > max_acc_test:
max_acc_test = test_acc
if test_f1 > max_f1:
max_f1 = test_f1
# store_model_to_file('/mounts/data/proj/wenpeng/Dataset/StanfordEntailment/model_para_five_copies_'+str(max_acc_test), params)
print '\t\tcurrent acc:', test_acc, ' ; ', '\t\tmax_acc:', max_acc_test, '\t\t test_f1:', test_f1, '\t\tmax F1:', max_f1
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
return max_acc_test
def evaluate_lenet5(learning_rate=0.0001, n_epochs=2000, batch_size=20, test_batch_size=200, emb_size=300, hidden_size=300,
L2_weight=0.0001, para_len_limit=400, q_len_limit=40, max_EM=50.302743615):
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/SQuAD/';
rng = numpy.random.RandomState(23455)
# glove_vocab=set(word2vec.keys())
train_para_list, train_Q_list, train_label_list, train_para_mask, train_mask, word2id, train_feature_matrixlist=load_train(para_len_limit, q_len_limit)
train_size=len(train_para_list)
if train_size!=len(train_Q_list) or train_size!=len(train_label_list) or train_size!=len(train_para_mask):
print 'train_size!=len(Q_list) or train_size!=len(label_list) or train_size!=len(para_mask)'
exit(0)
test_para_list, test_Q_list, test_Q_list_word, test_para_mask, test_mask, overall_vocab_size, overall_word2id, test_text_list, q_ansSet_list, test_feature_matrixlist, q_idlist= load_dev_or_test(word2id, para_len_limit, q_len_limit)
test_size=len(test_para_list)
if test_size!=len(test_Q_list) or test_size!=len(test_mask) or test_size!=len(test_para_mask):
print 'test_size!=len(test_Q_list) or test_size!=len(test_mask) or test_size!=len(test_para_mask)'
exit(0)
rand_values=random_value_normal((overall_vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
# rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
# id2word = {y:x for x,y in overall_word2id.iteritems()}
# word2vec=load_glove()
# rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings=theano.shared(value=rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
paragraph = T.imatrix('paragraph')
questions = T.imatrix('questions')
# labels = T.imatrix('labels') #(batch, para_len)
gold_indices= T.ivector() #batch
para_mask=T.fmatrix('para_mask')
q_mask=T.fmatrix('q_mask')
extraF=T.ftensor3('extraF') # should be in shape (batch, wordsize, 3)
is_train = T.iscalar()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
true_batch_size=paragraph.shape[0]
norm_extraF=normalize_matrix(extraF)
U1, W1, b1=create_GRU_para(rng, emb_size, hidden_size)
U1_b, W1_b, b1_b=create_GRU_para(rng, emb_size, hidden_size)
paragraph_para=[U1, W1, b1, U1_b, W1_b, b1_b]
U_e1, W_e1, b_e1=create_GRU_para(rng, 3*hidden_size+3, hidden_size)
U_e1_b, W_e1_b, b_e1_b=create_GRU_para(rng, 3*hidden_size+3, hidden_size)
paragraph_para_e1=[U_e1, W_e1, b_e1, U_e1_b, W_e1_b, b_e1_b]
UQ, WQ, bQ=create_GRU_para(rng, emb_size, hidden_size)
UQ_b, WQ_b, bQ_b=create_GRU_para(rng, emb_size, hidden_size)
Q_para=[UQ, WQ, bQ, UQ_b, WQ_b, bQ_b]
# W_a1 = create_ensemble_para(rng, hidden_size, hidden_size)# init_weights((2*hidden_size, hidden_size))
# W_a2 = create_ensemble_para(rng, hidden_size, hidden_size)
U_a = create_ensemble_para(rng, 1, 2*hidden_size) # 3 extra features
# LR_b = theano.shared(value=numpy.zeros((2,),
# dtype=theano.config.floatX), # @UndefinedVariable
# name='LR_b', borrow=True)
HL_paras=[U_a]
params = [embeddings]+paragraph_para+Q_para+paragraph_para_e1+HL_paras
load_model_from_file(rootPath+'Best_Paras_conv_50.302743614', params)
paragraph_input = embeddings[paragraph.flatten()].reshape((true_batch_size, paragraph.shape[1], emb_size)).transpose((0, 2,1)) # (batch_size, emb_size, maxparalen)
concate_paragraph_input=T.concatenate([paragraph_input, norm_extraF.dimshuffle((0,2,1))], axis=1)
paragraph_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=paragraph_input, Mask=para_mask, hidden_dim=hidden_size,U=U1,W=W1,b=b1,Ub=U1_b,Wb=W1_b,bb=b1_b)
para_reps=paragraph_model.output_tensor #(batch, emb, para_len)
# #LSTM
# fwd_LSTM_para_dict=create_LSTM_para(rng, emb_size, hidden_size)
# bwd_LSTM_para_dict=create_LSTM_para(rng, emb_size, hidden_size)
# paragraph_para=fwd_LSTM_para_dict.values()+ bwd_LSTM_para_dict.values()# .values returns a list of parameters
# paragraph_model=Bd_LSTM_Batch_Tensor_Input_with_Mask(paragraph_input, para_mask, hidden_size, fwd_LSTM_para_dict, bwd_LSTM_para_dict)
# para_reps=paragraph_model.output_tensor
Qs_emb = embeddings[questions.flatten()].reshape((true_batch_size, questions.shape[1], emb_size)).transpose((0, 2,1)) #(#questions, emb_size, maxsenlength)
questions_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=Qs_emb, Mask=q_mask, hidden_dim=hidden_size, U=UQ,W=WQ,b=bQ, Ub=UQ_b, Wb=WQ_b, bb=bQ_b)
questions_reps_tensor=questions_model.output_tensor
questions_reps=questions_model.output_sent_rep_maxpooling.reshape((true_batch_size, 1, hidden_size)) #(batch, 1, hidden)
questions_reps=T.repeat(questions_reps, para_reps.shape[2], axis=1) #(batch, para_len, hidden)
# #LSTM for questions
# fwd_LSTM_q_dict=create_LSTM_para(rng, emb_size, hidden_size)
# bwd_LSTM_q_dict=create_LSTM_para(rng, emb_size, hidden_size)
# Q_para=fwd_LSTM_q_dict.values()+ bwd_LSTM_q_dict.values()# .values returns a list of parameters
# questions_model=Bd_LSTM_Batch_Tensor_Input_with_Mask(Qs_emb, q_mask, hidden_size, fwd_LSTM_q_dict, bwd_LSTM_q_dict)
# questions_reps_tensor=questions_model.output_tensor
#
def example_in_batch(para_matrix, q_matrix):
#assume both are (hidden, len)
transpose_para_matrix=para_matrix.T
interaction_matrix=T.dot(transpose_para_matrix, q_matrix) #(para_len, q_len)
norm_interaction_matrix=T.nnet.softmax(interaction_matrix)
# norm_interaction_matrix=T.maximum(0.0, interaction_matrix)
return T.dot(q_matrix, norm_interaction_matrix.T)/T.sum(norm_interaction_matrix.T, axis=0).dimshuffle('x',0) #(len, para_len)
batch_q_reps, updates = theano.scan(fn=example_in_batch,
outputs_info=None,
sequences=[para_reps, questions_reps_tensor]) #batch_q_reps (batch, hidden, para_len)
#para_reps, batch_q_reps, questions_reps.dimshuffle(0,2,1), all are in (batch, hidden , para_len)
ensemble_para_reps_tensor=T.concatenate([para_reps, batch_q_reps, questions_reps.dimshuffle(0,2,1), norm_extraF.dimshuffle(0,2,1)], axis=1) #(batch, 3*hidden+3, para_len)
para_ensemble_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=ensemble_para_reps_tensor, Mask=para_mask, hidden_dim=hidden_size,U=U_e1,W=W_e1,b=b_e1,Ub=U_e1_b,Wb=W_e1_b,bb=b_e1_b)
para_reps_tensor4score=para_ensemble_model.output_tensor #(batch, hidden ,para_len)
para_reps_tensor4score = dropout_standard(is_train, para_reps_tensor4score, 0.2, rng)
#for span reps
span_1=T.concatenate([para_reps_tensor4score, para_reps_tensor4score], axis=1) #(batch, 2*hidden ,para_len)
span_2=T.concatenate([para_reps_tensor4score[:,:,:-1], para_reps_tensor4score[:,:,1:]], axis=1) #(batch, 2*hidden ,para_len-1)
span_3=T.concatenate([para_reps_tensor4score[:,:,:-2], para_reps_tensor4score[:,:,2:]], axis=1) #(batch, 2*hidden ,para_len-2)
span_4=T.concatenate([para_reps_tensor4score[:,:,:-3], para_reps_tensor4score[:,:,3:]], axis=1) #(batch, 2*hidden ,para_len-3)
span_5=T.concatenate([para_reps_tensor4score[:,:,:-4], para_reps_tensor4score[:,:,4:]], axis=1) #(batch, 2*hidden ,para_len-4)
span_6=T.concatenate([para_reps_tensor4score[:,:,:-5], para_reps_tensor4score[:,:,5:]], axis=1) #(batch, 2*hidden ,para_len-5)
span_7=T.concatenate([para_reps_tensor4score[:,:,:-6], para_reps_tensor4score[:,:,6:]], axis=1) #(batch, 2*hidden ,para_len-6)
span_8=T.concatenate([para_reps_tensor4score[:,:,:-7], para_reps_tensor4score[:,:,7:]], axis=1) #(batch, 2*hidden ,para_len-7)
span_9=T.concatenate([para_reps_tensor4score[:,:,:-8], para_reps_tensor4score[:,:,8:]], axis=1) #(batch, 2*hidden ,para_len-8)
span_10=T.concatenate([para_reps_tensor4score[:,:,:-9], para_reps_tensor4score[:,:,9:]], axis=1) #(batch, 2*hidden ,para_len-9)
span_11=T.concatenate([para_reps_tensor4score[:,:,:-10], para_reps_tensor4score[:,:,10:]], axis=1) #(batch, 2*hidden ,para_len-10)
span_12=T.concatenate([para_reps_tensor4score[:,:,:-11], para_reps_tensor4score[:,:,11:]], axis=1) #(batch, 2*hidden ,para_len-11)
span_13=T.concatenate([para_reps_tensor4score[:,:,:-12], para_reps_tensor4score[:,:,12:]], axis=1) #(batch, 2*hidden ,para_len-12)
span_reps=T.concatenate([span_1, span_2, span_3, span_4, span_5, span_6, span_7,
span_8, span_9, span_10, span_11, span_12, span_13], axis=2) #(batch, 2*hidden, 13*para_len-78)
test_span_reps=T.concatenate([span_1, span_2, span_3, span_4, span_5, span_6, span_7], axis=2) #(batch, 2*hidden, 5*para_len-10) #, span_6, span_7
#score each span reps
norm_U_a=normalize_matrix(U_a)
span_scores_tensor=T.dot(span_reps.dimshuffle(0,2,1), norm_U_a) #(batch, 13*para_len-78, 1)
span_scores=T.nnet.softmax(span_scores_tensor.reshape((true_batch_size, 13*paragraph.shape[1]-78))) #(batch, 7*para_len-21)
loss=-T.sum(T.log(span_scores[T.arange(true_batch_size), gold_indices]))
test_span_scores_tensor=T.dot(test_span_reps.dimshuffle(0,2,1), norm_U_a) #(batch, 7*para_len-21, 1)
test_span_scores=T.nnet.softmax(test_span_scores_tensor.reshape((true_batch_size, 7*paragraph.shape[1]-21))) #(batch, 7*para_len-21)
test_return=T.argmax(test_span_scores, axis=1) #batch
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
# L2_reg =L2norm_paraList([embeddings,U1, W1, U1_b, W1_b,UQ, WQ , UQ_b, WQ_b, W_a1, W_a2, U_a])
# L2_reg = L2norm_paraList([embeddings])
cost=loss#+ConvGRU_1.error#
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / (T.sqrt(acc)+1e-8))) #AdaGrad
updates.append((acc_i, acc))
# updates=Adam(cost, params, lr=0.0001)
train_model = theano.function([paragraph, questions,gold_indices, para_mask, q_mask, extraF, is_train], cost, updates=updates,on_unused_input='ignore')
test_model = theano.function([paragraph, questions,para_mask, q_mask, extraF, is_train], test_return, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches=train_size/batch_size
# remain_train=train_size%batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)+[train_size-batch_size]
n_test_batches=test_size/test_batch_size
# remain_test=test_size%batch_size
test_batch_start=list(numpy.arange(n_test_batches)*test_batch_size)+[test_size-test_batch_size]
max_F1_acc=0.0
max_exact_acc=0.0
cost_i=0.0
train_ids = range(train_size)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.shuffle(train_ids)
iter_accu=0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
iter_accu+=1
# haha=para_mask[para_id:para_id+batch_size]
# print haha
# for i in range(batch_size):
# print len(haha[i])
cost_i+= train_model(
numpy.asarray([train_para_list[id] for id in train_ids[para_id:para_id+batch_size]], dtype='int32'),
numpy.asarray([train_Q_list[id] for id in train_ids[para_id:para_id+batch_size]], dtype='int32'),
numpy.asarray([train_label_list[id] for id in train_ids[para_id:para_id+batch_size]], dtype='int32'),
numpy.asarray([train_para_mask[id] for id in train_ids[para_id:para_id+batch_size]], dtype=theano.config.floatX),
numpy.asarray([train_mask[id] for id in train_ids[para_id:para_id+batch_size]], dtype=theano.config.floatX),
numpy.asarray([train_feature_matrixlist[id] for id in train_ids[para_id:para_id+batch_size]], dtype=theano.config.floatX),
1)
#print iter
if iter%10==0:
print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
print 'Testing...'
past_time = time.time()
# writefile=codecs.open(rootPath+'predictions.txt', 'w', 'utf-8')
# writefile.write('{')
pred_dict={}
# exact_match=0.0
# F1_match=0.0
q_amount=0
for test_para_id in test_batch_start:
batch_predict_ids=test_model(
numpy.asarray(test_para_list[test_para_id:test_para_id+test_batch_size], dtype='int32'),
numpy.asarray(test_Q_list[test_para_id:test_para_id+test_batch_size], dtype='int32'),
numpy.asarray(test_para_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
numpy.asarray(test_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
numpy.asarray(test_feature_matrixlist[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
0)
# print distribution_matrix
test_para_wordlist_list=test_text_list[test_para_id:test_para_id+test_batch_size]
# para_gold_ansset_list=q_ansSet_list[test_para_id:test_para_id+test_batch_size]
q_ids_batch=q_idlist[test_para_id:test_para_id+test_batch_size]
# print 'q_ids_batch:', q_ids_batch
# paralist_extra_features=test_feature_matrixlist[test_para_id:test_para_id+batch_size]
# sub_para_mask=test_para_mask[test_para_id:test_para_id+batch_size]
# para_len=len(test_para_wordlist_list[0])
# if para_len!=len(distribution_matrix[0]):
# print 'para_len!=len(distribution_matrix[0]):', para_len, len(distribution_matrix[0])
# exit(0)
# q_size=len(distribution_matrix)
q_amount+=test_batch_size
# print q_size
# print test_para_word_list
# Q_list_inword=test_Q_list_word[test_para_id:test_para_id+test_batch_size]
for q in range(test_batch_size): #for each question
# if len(distribution_matrix[q])!=len(test_label_matrix[q]):
# print 'len(distribution_matrix[q])!=len(test_label_matrix[q]):', len(distribution_matrix[q]), len(test_label_matrix[q])
# else:
# ss=len(distribution_matrix[q])
# combine_list=[]
# for ii in range(ss):
# combine_list.append(str(distribution_matrix[q][ii])+'('+str(test_label_matrix[q][ii])+')')
# print combine_list
# exit(0)
# print 'distribution_matrix[q]:',distribution_matrix[q]
pred_ans=decode_predict_id(batch_predict_ids[q], test_para_wordlist_list[q])
q_id=q_ids_batch[q]
pred_dict[q_id]=pred_ans
# writefile.write('"'+str(q_id)+'": "'+pred_ans+'", ')
# pred_ans=extract_ansList_attentionList(test_para_wordlist_list[q], distribution_matrix[q], numpy.asarray(paralist_extra_features[q], dtype=theano.config.floatX), sub_para_mask[q], Q_list_inword[q])
# q_gold_ans_set=para_gold_ansset_list[q]
# # print test_para_wordlist_list[q]
# # print Q_list_inword[q]
# # print pred_ans.encode('utf8'), q_gold_ans_set
# if pred_ans in q_gold_ans_set:
# exact_match+=1
# F1=MacroF1(pred_ans, q_gold_ans_set)
# F1_match+=F1
with codecs.open(rootPath+'predictions.txt', 'w', 'utf-8') as outfile:
json.dump(pred_dict, outfile)
F1_acc, exact_acc = standard_eval(rootPath+'dev-v1.1.json', rootPath+'predictions.txt')
# F1_acc=F1_match/q_amount
# exact_acc=exact_match/q_amount
if F1_acc> max_F1_acc:
max_F1_acc=F1_acc
if exact_acc> max_exact_acc:
max_exact_acc=exact_acc
if max_exact_acc > max_EM:
store_model_to_file(rootPath+'Best_Paras_conv_'+str(max_exact_acc), params)
print 'Finished storing best params at:', max_exact_acc
print 'current average F1:', F1_acc, '\t\tmax F1:', max_F1_acc, 'current exact:', exact_acc, '\t\tmax exact_acc:', max_exact_acc
# os.system('python evaluate-v1.1.py '+rootPath+'dev-v1.1.json '+rootPath+'predictions.txt')
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.01,
n_epochs=100,
L2_weight=0.000001,
drop_p=0.05,
emb_size=300,
hidden_size=500,
HL_hidden_size=500,
batch_size=5,
filter_size=[3, 5, 7],
maxSentLen=180,
comment=''):
model_options = locals().copy()
print "model options", model_options
rng = np.random.RandomState(
1234) #random seed, control the model generates the same results
srng = RandomStreams(rng.randint(999999))
all_sentences, all_masks, all_labels, word2id = load_yelp_dataset(
maxlen=maxSentLen, minlen=2
) #minlen, include one label, at least one word in the sentence
train_sents = np.asarray(all_sentences[0], dtype='int32')
train_masks = np.asarray(all_masks[0], dtype=theano.config.floatX)
train_labels = np.asarray(all_labels[0], dtype='int32')
train_size = len(train_labels)
dev_sents = np.asarray(all_sentences[1], dtype='int32')
dev_masks = np.asarray(all_masks[1], dtype=theano.config.floatX)
dev_labels = np.asarray(all_labels[1], dtype='int32')
dev_size = len(dev_labels)
test_sents = np.asarray(all_sentences[2], dtype='int32')
test_masks = np.asarray(all_masks[2], dtype=theano.config.floatX)
test_labels = np.asarray(all_labels[2], dtype='int32')
test_size = len(test_labels)
vocab_size = len(word2id) + 1 # add one zero pad index
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
#here, we leave code for loading word2vec to initialize words
rand_values[0] = np.array(np.zeros(emb_size), dtype=theano.config.floatX)
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_word2vec_file('glove.840B.300d.txt') #
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
#now, start to build the input form of the model
sents_id_matrix = T.imatrix('sents_id_matrix')
sents_mask = T.fmatrix('sents_mask')
labels = T.ivector('labels')
train_flag = T.iscalar()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
common_input = embeddings[sents_id_matrix.flatten()].reshape(
(batch_size, maxSentLen, emb_size)).dimshuffle(
0, 2, 1) #the input format can be adapted into CNN or GRU or LSTM
# drop_common_input = dropout_layer(srng, common_input, drop_p, train_flag)
bow = T.sum(common_input * sents_mask.dimshuffle(0, 'x', 1),
axis=2) #(batch, emb_size)
gate_filter_shape = (emb_size, 1, emb_size, 1)
conv_W_2_pre, conv_b_2_pre = create_conv_para(
rng, filter_shape=gate_filter_shape)
conv_W_2_gate, conv_b_2_gate = create_conv_para(
rng, filter_shape=gate_filter_shape)
conv_W, conv_b = create_conv_para(rng,
filter_shape=(hidden_size, 1, emb_size,
filter_size[0]))
conv_W_context, conv_b_context = create_conv_para(
rng, filter_shape=(hidden_size, 1, emb_size, 1))
conv_W2, conv_b2 = create_conv_para(rng,
filter_shape=(hidden_size, 1, emb_size,
filter_size[1]))
conv_W2_context, conv_b2_context = create_conv_para(
rng, filter_shape=(hidden_size, 1, emb_size, 1))
# conv_W3, conv_b3=create_conv_para(rng, filter_shape=(hidden_size, 1, emb_size, filter_size[2]))
# conv_W3_context, conv_b3_context=create_conv_para(rng, filter_shape=(hidden_size, 1, emb_size, 1))
# conv_W_into_matrix=conv_W.reshape((conv_W.shape[0], conv_W.shape[2]*conv_W.shape[3]))
soft_att_W_big, soft_att_b_big = create_HiddenLayer_para(
rng, emb_size * 2, emb_size)
soft_att_W_small, _ = create_HiddenLayer_para(rng, emb_size, 1)
soft_att_W2_big, soft_att_b2_big = create_HiddenLayer_para(
rng, emb_size * 2, emb_size)
soft_att_W2_small, _ = create_HiddenLayer_para(rng, emb_size, 1)
# soft_att_W3_big, soft_att_b3_big = create_HiddenLayer_para(rng, emb_size*2, emb_size)
# soft_att_W3_small, _ = create_HiddenLayer_para(rng, emb_size, 1)
NN_para = [
conv_W_2_pre,
conv_b_2_pre,
conv_W_2_gate,
conv_b_2_gate,
conv_W,
conv_b,
conv_W_context,
conv_W2,
conv_b2,
conv_W2_context,
# conv_W3, conv_b3,conv_W3_context,
soft_att_W_big,
soft_att_b_big,
soft_att_W_small,
soft_att_W2_big,
soft_att_b2_big,
soft_att_W2_small
# soft_att_W3_big, soft_att_b3_big,soft_att_W3_small
] #,conv_W3, conv_b3,conv_W3_context]
conv_layer_1_gate_l = Conv_with_Mask_with_Gate(
rng,
input_tensor3=common_input,
mask_matrix=sents_mask,
image_shape=(batch_size, 1, emb_size, maxSentLen),
filter_shape=gate_filter_shape,
W=conv_W_2_pre,
b=conv_b_2_pre,
W_gate=conv_W_2_gate,
b_gate=conv_b_2_gate)
advanced_sent_tensor3 = conv_layer_1_gate_l.output_tensor3
# conv_layer_pair = Conv_for_Pair(rng,
# origin_input_tensor3=advanced_sent_tensor3,
# origin_input_tensor3_r = advanced_sent_tensor3,
# input_tensor3=advanced_sent_tensor3,
# input_tensor3_r = advanced_sent_tensor3,
# mask_matrix = sents_mask,
# mask_matrix_r = sents_mask,
# image_shape=(batch_size, 1, emb_size, maxSentLen),
# image_shape_r = (batch_size, 1, emb_size, maxSentLen),
# filter_shape=(hidden_size, 1, emb_size, filter_size[0]),
# filter_shape_context=(hidden_size, 1, emb_size, 1),
# W=conv_W, b=conv_b,
# W_context=conv_W_context, b_context=conv_b_context)
conv_layer_pair = Conv_for_Pair_SoftAttend(
rng,
origin_input_tensor3=advanced_sent_tensor3,
origin_input_tensor3_r=advanced_sent_tensor3,
input_tensor3=advanced_sent_tensor3,
input_tensor3_r=advanced_sent_tensor3,
mask_matrix=sents_mask,
mask_matrix_r=sents_mask,
filter_shape=(hidden_size, 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size, 1, emb_size, 1),
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r=(batch_size, 1, emb_size, maxSentLen),
W=conv_W,
b=conv_b,
W_context=conv_W_context,
b_context=conv_b_context,
soft_att_W_big=soft_att_W_big,
soft_att_b_big=soft_att_b_big,
soft_att_W_small=soft_att_W_small)
# conv_layer_2_pair = Conv_for_Pair(rng,
# origin_input_tensor3=advanced_sent_tensor3,
# origin_input_tensor3_r = advanced_sent_tensor3,
# input_tensor3=advanced_sent_tensor3,
# input_tensor3_r = advanced_sent_tensor3,
# mask_matrix = sents_mask,
# mask_matrix_r = sents_mask,
# image_shape=(batch_size, 1, emb_size, maxSentLen),
# image_shape_r = (batch_size, 1, emb_size, maxSentLen),
# filter_shape=(hidden_size, 1, emb_size, filter_size[1]),
# filter_shape_context=(hidden_size, 1, emb_size, 1),
# W=conv_W2, b=conv_b2,
# W_context=conv_W2_context, b_context=conv_b2_context)
conv_layer_2_pair = Conv_for_Pair_SoftAttend(
rng,
origin_input_tensor3=advanced_sent_tensor3,
origin_input_tensor3_r=advanced_sent_tensor3,
input_tensor3=advanced_sent_tensor3,
input_tensor3_r=advanced_sent_tensor3,
mask_matrix=sents_mask,
mask_matrix_r=sents_mask,
filter_shape=(hidden_size, 1, emb_size, filter_size[1]),
filter_shape_context=(hidden_size, 1, emb_size, 1),
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r=(batch_size, 1, emb_size, maxSentLen),
W=conv_W2,
b=conv_b2,
W_context=conv_W2_context,
b_context=conv_b2_context,
soft_att_W_big=soft_att_W2_big,
soft_att_b_big=soft_att_b2_big,
soft_att_W_small=soft_att_W2_small)
# conv_layer_3_pair = Conv_for_Pair_SoftAttend(rng,
# origin_input_tensor3=advanced_sent_tensor3,
# origin_input_tensor3_r=advanced_sent_tensor3,
# input_tensor3=advanced_sent_tensor3,
# input_tensor3_r=advanced_sent_tensor3,
# mask_matrix=sents_mask,
# mask_matrix_r=sents_mask,
# filter_shape=(hidden_size, 1, emb_size, filter_size[2]),
# filter_shape_context=(hidden_size, 1, emb_size, 1),
# image_shape=(batch_size, 1, emb_size, maxSentLen),
# image_shape_r= (batch_size, 1, emb_size, maxSentLen),
# W=conv_W3, b=conv_b3,
# W_context=conv_W3_context, b_context=conv_b3_context,
# soft_att_W_big=soft_att_W3_big, soft_att_b_big=soft_att_b3_big,
# soft_att_W_small=soft_att_W3_small)
# biased_sent_embeddings = conv_layer_pair.biased_attentive_maxpool_vec_l
sent_embeddings = conv_layer_pair.maxpool_vec_l
att_sent_embeddings = conv_layer_pair.attentive_maxpool_vec_l
sent_embeddings_2 = conv_layer_2_pair.maxpool_vec_l
att_sent_embeddings_2 = conv_layer_2_pair.attentive_maxpool_vec_l
#classification layer, it is just mapping from a feature vector of size "hidden_size" to a vector of only two values: positive, negative
HL_input = T.concatenate(
[
bow, sent_embeddings, att_sent_embeddings, sent_embeddings_2,
att_sent_embeddings_2
# sent_embeddings_3,att_sent_embeddings_3,
],
axis=1)
HL_input_size = hidden_size * 4 + emb_size
HL_layer_1_W, HL_layer_1_b = create_HiddenLayer_para(
rng, HL_input_size, HL_hidden_size)
HL_layer_1_params = [HL_layer_1_W, HL_layer_1_b]
HL_layer_1 = HiddenLayer(rng,
input=HL_input,
n_in=HL_input_size,
n_out=HL_hidden_size,
W=HL_layer_1_W,
b=HL_layer_1_b,
activation=T.nnet.relu)
# HL_layer_1_output = dropout_layer(srng, HL_layer_1.output, drop_p, train_flag)
HL_layer_2_W, HL_layer_2_b = create_HiddenLayer_para(
rng, HL_hidden_size, HL_hidden_size)
HL_layer_2_params = [HL_layer_2_W, HL_layer_2_b]
HL_layer_2 = HiddenLayer(rng,
input=HL_layer_1.output,
n_in=HL_hidden_size,
n_out=HL_hidden_size,
W=HL_layer_2_W,
b=HL_layer_2_b,
activation=T.nnet.relu)
# HL_layer_2_output = dropout_layer(srng, HL_layer_2.output, drop_p, train_flag)
LR_input = T.concatenate([HL_input, HL_layer_1.output, HL_layer_2.output],
axis=1)
# drop_LR_input = dropout_layer(srng, LR_input, drop_p, train_flag)
LR_input_size = HL_input_size + 2 * HL_hidden_size
U_a = create_ensemble_para(
rng, 5, LR_input_size) # the weight matrix hidden_size*2
# norm_W_a = normalize_matrix(U_a)
LR_b = theano.shared(value=np.zeros((5, ), dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
LR_para = [U_a, LR_b]
layer_LR = LogisticRegression(
rng, input=LR_input, n_in=LR_input_size, n_out=5, W=U_a, b=LR_b
) #basically it is a multiplication between weight matrix and input feature vector
loss = layer_LR.negative_log_likelihood(
labels
) #for classification task, we usually used negative log likelihood as loss, the lower the better.
params = [
embeddings
] + NN_para + HL_layer_1_params + HL_layer_2_params + LR_para # put all model parameters together
L2_reg = L2norm_paraList([
embeddings, conv_W_2_pre, conv_W_2_gate, conv_W, conv_W_context,
conv_W2, conv_W2_context, soft_att_W_big, soft_att_W_small,
soft_att_W2_big, soft_att_W2_small, HL_layer_1_W, HL_layer_2_W, U_a
])
# diversify_reg= Diversify_Reg(U_a.T)+Diversify_Reg(conv_W_into_matrix)
cost = loss #+L2_weight*L2_reg
grads = T.grad(
cost, params) # create a list of gradients for all model parameters
accumulator = []
for para_i in params:
eps_p = np.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
acc = acc_i + T.sqr(grad_i)
updates.append(
(param_i, param_i - learning_rate * grad_i /
(T.sqrt(acc) + 1e-8))) #1e-8 is add to get rid of zero division
updates.append((acc_i, acc))
#train_model = theano.function([sents_id_matrix, sents_mask, labels], cost, updates=updates, on_unused_input='ignore')
train_model = theano.function(
[sents_id_matrix, sents_mask, labels, train_flag],
cost,
updates=updates,
allow_input_downcast=True,
on_unused_input='ignore')
dev_model = theano.function(
[sents_id_matrix, sents_mask, labels, train_flag],
layer_LR.errors(labels),
allow_input_downcast=True,
on_unused_input='ignore')
test_model = theano.function(
[sents_id_matrix, sents_mask, labels, train_flag],
[layer_LR.errors(labels), layer_LR.y_pred],
allow_input_downcast=True,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
n_train_batches = train_size / batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
n_dev_batches = dev_size / batch_size
dev_batch_start = list(
np.arange(n_dev_batches) * batch_size) + [dev_size - batch_size]
n_test_batches = test_size / batch_size
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
max_acc_dev = 0.0
max_acc_test = 0.0
cost_i = 0.0
train_indices = range(train_size)
while epoch < n_epochs:
epoch = epoch + 1
# combined = zip(train_sents, train_masks, train_labels)
random.Random(200).shuffle(
train_indices
) #shuffle training set for each new epoch, is supposed to promote performance, but not garrenteed
iter_accu = 0
for batch_id in train_batch_start: #for each batch
# iter means how many batches have been run, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
train_id_batch = train_indices[batch_id:batch_id + batch_size]
cost_i += train_model(train_sents[train_id_batch],
train_masks[train_id_batch],
train_labels[train_id_batch], 1)
#after each 1000 batches, we test the performance of the model on all test data
if iter % 2000 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
past_time = time.time()
error_sum = 0.0
# writefile=open('log.'+nn+'.senti.preditions.txt', 'w')
for test_batch_id in test_batch_start: # for each test batch
error_i, pred_labels = test_model(
test_sents[test_batch_id:test_batch_id + batch_size],
test_masks[test_batch_id:test_batch_id + batch_size],
test_labels[test_batch_id:test_batch_id + batch_size],
0)
# pred_labels=list(pred_labels)
# if test_batch_id !=test_batch_start[-1]:
# writefile.write('\n'.join(map(str,pred_labels))+'\n')
# else:
# writefile.write('\n'.join(map(str,pred_labels[-test_size%batch_size:])))
error_sum += error_i
# writefile.close()
test_accuracy = 1.0 - error_sum / (len(test_batch_start))
if test_accuracy > max_acc_test:
max_acc_test = test_accuracy
print '\t\tcurrent testbacc:', test_accuracy, '\t\t\t\t\tmax_acc_test:', max_acc_test
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
return max_acc_test