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=0.5, 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=0.01, 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=0.01, 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=0.01, 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(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=0.01,
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=0.1, 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=2000, batch_size=200, test_batch_size=200, emb_size=300, hidden_size=20,
L2_weight=0.0001, p_len_limit=400, test_p_len_limit=40, q_len_limit=20, filter_size = [5,5], margin=0.85, 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)
word2id={}
#questions,paragraphs,q_masks,p_masks,labels, word2id
train_Q_list,train_para_list, train_Q_mask, train_para_mask, train_label_list, word2id=load_squad_cnn_rank_span_train(word2id, p_len_limit, q_len_limit)
train_size=len(train_para_list)
test_Q_list, test_para_list, test_Q_mask, test_para_mask, q_idlist, word2id, test_para_wordlist_list= load_squad_cnn_rank_span_dev(word2id, test_p_len_limit, q_len_limit)
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_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)
vocab_size = len(word2id)
print 'vocab size: ', vocab_size
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)
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)
# allocate symbolic variables for the data
# index = T.lscalar()
paragraph = T.imatrix('paragraph')
questions = T.imatrix('questions')
gold_indices= T.ivector() #batch
para_mask=T.fmatrix('para_mask')
q_mask=T.fmatrix('q_mask')
# paragraph = T.imatrix('paragraph')
# questions = T.imatrix('questions')
# 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)
######################
# 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, hidden_size)
U_e1_b, W_e1_b, b_e1_b=create_GRU_para(rng, 3*hidden_size, 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
# 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)
# 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)], 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)
#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.mean(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(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], cost, updates=updates,on_unused_input='ignore')
test_model = theano.function([paragraph, questions,para_mask, q_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])
#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()
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_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
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=0.5,
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=300,
test_batch_size=10000,
emb_size=50,
hidden_size=50,
L2_weight=0.0001,
para_len_limit=70,
q_len_limit=20,
pred_q_len_limit=50,
top_n_Qwords=1):
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/SQuAD/'
rng = np.random.RandomState(23455)
word2id = {}
train_para_list, train_para_mask, train_Q_list, train_Q_mask, train_start_list, train_end_list, _, word2id = load_QGQA(
word2id, para_len_limit, q_len_limit, top_n_Qwords, True)
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_para_mask, test_Q_list, test_Q_mask, test_start_list, test_end_list, _, word2id = load_QGQA(
word2id, para_len_limit, q_len_limit, top_n_Qwords, False)
test_size = len(test_para_list)
train_para_list = np.asarray(train_para_list, dtype='int32')
train_para_mask = np.asarray(train_para_mask, dtype=theano.config.floatX)
train_Q_list = np.asarray(train_Q_list, dtype='int32')
train_Q_mask = np.asarray(train_Q_mask, dtype=theano.config.floatX)
train_start_list = np.asarray(train_start_list, dtype='int32')
train_end_list = np.asarray(train_end_list, dtype='int32')
test_para_list = np.asarray(test_para_list, dtype='int32')
test_para_mask = np.asarray(test_para_mask, dtype=theano.config.floatX)
test_Q_list = np.asarray(test_Q_list, dtype='int32')
test_Q_mask = np.asarray(test_Q_mask, dtype=theano.config.floatX)
test_start_list = np.asarray(test_start_list, dtype='int32')
test_end_list = np.asarray(test_end_list, dtype='int32')
vocab_size = len(word2id) + 1
# shared_decoder_mask = [0]*vocab_size
# shared_decoder_mask[0]=1#we need this pad token in generated text
# for id in train_top_Q_wordids:
# shared_decoder_mask[id]=1
# shared_decoder_mask=theano.shared(value=np.asarray(shared_decoder_mask, dtype='int32'), borrow=True) #
rand_values = random_value_normal((vocab_size, 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_glove()
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings = theano.shared(value=rand_values, borrow=True)
train_top_Q_wordids = set()
wh_words = [
'What', 'Which', 'Where', 'When', 'Who', 'Whom', 'Whose', 'Why', 'How',
'far', 'many', 'much', 'long'
]
for word in wh_words:
idd = word2id.get(word)
if idd is not None:
train_top_Q_wordids.add(idd)
iddd = word2id.get(word.lower())
if iddd is not None:
train_top_Q_wordids.add(iddd)
paragraph = T.imatrix('paragraph')
questions_encoderIDs = T.imatrix() # is ground truth,
questions_decoderIDS = T.imatrix(
) #note we convert then from encoder vocab id to decoder vocab id
decoder_vocab = T.ivector()
decoder_mask = T.fmatrix() #(batch, decoder_vocab_size)
start_indices = T.ivector() #batch
end_indices = T.ivector() #batch
para_mask = T.fmatrix('para_mask')
q_mask = T.fmatrix('q_mask')
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
true_batch_size = paragraph.shape[0]
paragraph_input = embeddings[paragraph.flatten()].reshape(
(true_batch_size, para_len_limit,
emb_size)).dimshuffle(0, 2, 1) #(batch, emb_size, para_len)
q_input = embeddings[questions_encoderIDs.flatten()].reshape(
(true_batch_size, q_len_limit, emb_size)).dimshuffle(0, 2, 1)
decoder_vocab_embs = embeddings[decoder_vocab]
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)
batch_ids = T.arange(true_batch_size)
ans_heads = paragraph_reps_tensor3[batch_ids, :, start_indices]
ans_tails = paragraph_reps_tensor3[batch_ids, :, end_indices]
l_context_heads = paragraph_reps_tensor3[:, :, 0]
l_context_tails = paragraph_reps_tensor3[batch_ids, :, start_indices - 1]
r_context_heads = paragraph_reps_tensor3[batch_ids, :, end_indices + 1]
r_context_tails = paragraph_reps_tensor3[:, :, -1]
encoder_reps = T.concatenate([
l_context_heads, l_context_tails, ans_heads, ans_tails,
r_context_heads, r_context_tails
],
axis=1) #(batch, 6*2hidden_size)
decoder_para_dict = create_LSTM_para(rng, emb_size + 12 * hidden_size,
emb_size)
attention_para_dict1 = create_LSTM_para(rng, 2 * hidden_size, hidden_size)
attention_para_dict2 = create_LSTM_para(rng, 2 * hidden_size, hidden_size)
'''
train
'''
groundtruth_as_input = T.concatenate([
T.alloc(np.asarray(0., dtype=theano.config.floatX), true_batch_size,
emb_size, 1), q_input[:, :, :-1]
],
axis=2)
# decoder = LSTM_Decoder_Train_with_Mask(groundtruth_as_input, encoder_reps, decoder_vocab_embs, q_mask, emb_size, decoder_para_dict)
#X, Encoder_Tensor_Rep, Encoder_Mask, start_indices, end_indices, vocab_embs, Mask, emb_size, hidden_size, tparams, attention_para_dict1, attention_para_dict2
decoder = LSTM_Decoder_Train_with_Attention(
groundtruth_as_input, paragraph_reps_tensor3, para_mask, start_indices,
end_indices, decoder_vocab_embs, q_mask, emb_size, hidden_size,
decoder_para_dict, attention_para_dict1, attention_para_dict2)
prob_matrix = decoder.prob_matrix #(batch*senlen, decoder_vocab_size)
probs = prob_matrix[T.arange(true_batch_size * q_len_limit),
questions_decoderIDS.flatten()]
mask_probs = probs[(q_mask.flatten()).nonzero()]
#we shift question word ids so that in current step, the prob of previsouly predicted id gets lower and lower
shifted_question_ids = T.concatenate([
T.alloc(np.asarray(0, dtype='int32'), true_batch_size, 1),
questions_decoderIDS[:, :-1]
],
axis=1)
probs_to_minimize = prob_matrix[T.arange(true_batch_size * q_len_limit),
shifted_question_ids.flatten()]
mask_probs_to_minimize = probs_to_minimize[(q_mask.flatten()).nonzero()]
#loss train
loss = -T.mean(T.log(mask_probs)) + T.mean(T.exp(mask_probs_to_minimize))
cost = loss #+ConvGRU_1.error#
params = [embeddings] + paragraph_para + decoder_para_dict.values(
) + attention_para_dict1.values() + attention_para_dict2.values()
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))
# #test decoder mask
# raw_masks = T.zeros((true_batch_size, vocab_size), dtype='int32')
# x_axis = T.repeat(T.arange(true_batch_size).dimshuffle(0,'x'), paragraph.shape[1], axis=1)
# input_specific_masks = T.set_subtensor(raw_masks[x_axis.flatten(),paragraph.flatten()],1)
# overall_test_decoder_mask = T.or_(input_specific_masks, shared_decoder_mask.dimshuffle('x',0)) #(batch, vocab_size)
# overall_test_decoder_mask=(1.0-overall_test_decoder_mask)*(overall_test_decoder_mask-10)
'''
testing
'''
# test_decoder = LSTM_Decoder_Test_with_Mask(q_len_limit, encoder_reps, decoder_vocab_embs, emb_size, decoder_para_dict)
#nsteps, Encoder_Tensor_Rep, Encoder_Mask, start_indices, end_indices, vocab_embs, emb_size,hidden_size, tparams,attention_para_dict1, attention_para_dict2
test_decoder = LSTM_Decoder_Test_with_Attention(
pred_q_len_limit, paragraph_reps_tensor3, para_mask, start_indices,
end_indices, decoder_vocab_embs, decoder_mask, emb_size, hidden_size,
decoder_para_dict, attention_para_dict1, attention_para_dict2)
predictions = test_decoder.output_id_matrix #(batch, q_len_limit)
train_model = theano.function([
paragraph, questions_encoderIDs, questions_decoderIDS, decoder_vocab,
start_indices, end_indices, para_mask, q_mask
],
cost,
updates=updates,
on_unused_input='ignore')
test_model = theano.function([
paragraph, decoder_vocab, decoder_mask, start_indices, end_indices,
para_mask
],
predictions,
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
# remain_train=train_size%batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
n_test_batches = test_size / test_batch_size
remain_test = test_size % test_batch_size
test_batch_start = list(np.arange(n_test_batches) *
test_batch_size) + [test_size - remain_test]
max_bleuscore = 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
sub_Qs = train_Q_list[para_id:para_id + batch_size]
decoder_vocab_set = train_top_Q_wordids | set(
list(np.unique(sub_Qs)))
decoder_vocab_batch = sorted(
decoder_vocab_set) # a list of ids in order
map_encoderid2decoderid = {}
for encoderID in decoder_vocab_set:
decoderID = decoder_vocab_batch.index(encoderID)
map_encoderid2decoderid[encoderID] = decoderID
Decoder_train_Q_list = []
for id in sub_Qs.flatten():
Decoder_train_Q_list.append(map_encoderid2decoderid.get(id))
Decoder_train_Q_list = np.asarray(Decoder_train_Q_list,
dtype='int32').reshape(
(batch_size,
sub_Qs.shape[1]))
decoder_vocab_batch = np.asarray(decoder_vocab_batch,
dtype='int32')
cost_i += train_model(
train_para_list[para_id:para_id + batch_size],
train_Q_list[para_id:para_id + batch_size],
Decoder_train_Q_list, decoder_vocab_batch,
train_start_list[para_id:para_id + batch_size],
train_end_list[para_id:para_id + batch_size],
train_para_mask[para_id:para_id + batch_size],
train_Q_mask[para_id:para_id + batch_size])
#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()
outputfile = codecs.open('output.txt', 'w', 'utf-8')
referencefile = codecs.open('reference.txt', 'w', 'utf-8')
bleu_scores = []
for idd, test_para_id in enumerate(test_batch_start):
sub_Qs = test_Q_list[test_para_id:test_para_id +
test_batch_size]
decoder_vocab_set = train_top_Q_wordids | set(
list(np.unique(sub_Qs)))
decoder_vocab_batch = sorted(
decoder_vocab_set) # a list of ids in order
map_decoderid2encoderid = {}
for encoderID in decoder_vocab_set:
decoderID = decoder_vocab_batch.index(encoderID)
map_decoderid2encoderid[decoderID] = encoderID
if idd == len(test_batch_start) - 1:
true_test_batch_size = remain_test
else:
true_test_batch_size = test_batch_size
#decoder mask batch
decoder_mask_batch = []
for i in range(true_test_batch_size):
Q_i = test_Q_list[test_para_id + i]
decoder_vocab_Q = train_top_Q_wordids | set(
list(np.unique(Q_i)))
decoder_mask_ind = []
for ele in decoder_vocab_batch:
if ele in decoder_vocab_Q:
decoder_mask_ind.append(1.0)
else:
decoder_mask_ind.append(0.0)
decoder_mask_batch.append(decoder_mask_ind)
decoder_mask_batch = np.asarray(decoder_mask_batch,
dtype=theano.config.floatX)
decoder_vocab_batch = np.asarray(decoder_vocab_batch,
dtype='int32')
pred_id_in_batch = test_model(
test_para_list[test_para_id:test_para_id +
test_batch_size], decoder_vocab_batch,
decoder_mask_batch,
test_start_list[test_para_id:test_para_id +
test_batch_size],
test_end_list[test_para_id:test_para_id +
test_batch_size],
test_para_mask[test_para_id:test_para_id +
test_batch_size]) #(batch, senlen)
ground_truths = sub_Qs
ground_mask = test_Q_mask[test_para_id:test_para_id +
test_batch_size]
back_pred_id_in_batch = [
map_decoderid2encoderid.get(id)
for id in pred_id_in_batch.flatten()
]
for i in range(true_test_batch_size):
# print 'pred_id_in_batch[i]:', pred_id_in_batch[i]
refined_preds, refined_g = refine_decoder_predictions(
back_pred_id_in_batch[i *
pred_q_len_limit:(i + 1) *
pred_q_len_limit],
ground_truths[i], ground_mask[i])
# bleu_i = nltk.translate.bleu_score.sentence_bleu([refined_g], refined_preds)
# bleu_scores.append(bleu_i)
pred_q = ''
prev_w = ''
for id in refined_preds:
word = id2word.get(id)
if word.isalnum():
if word != prev_w:
pred_q += ' ' + word
prev_w = word
outputfile.write(pred_q + ' ?\n')
referencefile.write(
' '.join([id2word.get(id)
for id in refined_g]) + '\n')
# bleuscore = np.average(np.array(bleu_scores))
outputfile.close()
referencefile.close()
system('perl multi-bleu.perl reference.txt < output.txt')
# if max_bleuscore < bleuscore:
# max_bleuscore = bleuscore
# print '\t\t\t\t\t\t current bleu: ', bleuscore, ' ; max bleu:', max_bleuscore
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.))
