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=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=0.0001, n_epochs=2000, batch_size=20, test_batch_size=200, emb_size=300, hidden_size=300,
L2_weight=0.0001, para_len_limit=400, q_len_limit=40, max_EM=50.302743615):
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/SQuAD/';
rng = numpy.random.RandomState(23455)
# glove_vocab=set(word2vec.keys())
train_para_list, train_Q_list, train_label_list, train_para_mask, train_mask, word2id, train_feature_matrixlist=load_train(para_len_limit, q_len_limit)
train_size=len(train_para_list)
if train_size!=len(train_Q_list) or train_size!=len(train_label_list) or train_size!=len(train_para_mask):
print 'train_size!=len(Q_list) or train_size!=len(label_list) or train_size!=len(para_mask)'
exit(0)
test_para_list, test_Q_list, test_Q_list_word, test_para_mask, test_mask, overall_vocab_size, overall_word2id, test_text_list, q_ansSet_list, test_feature_matrixlist, q_idlist= load_dev_or_test(word2id, para_len_limit, q_len_limit)
test_size=len(test_para_list)
if test_size!=len(test_Q_list) or test_size!=len(test_mask) or test_size!=len(test_para_mask):
print 'test_size!=len(test_Q_list) or test_size!=len(test_mask) or test_size!=len(test_para_mask)'
exit(0)
rand_values=random_value_normal((overall_vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
# rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
# id2word = {y:x for x,y in overall_word2id.iteritems()}
# word2vec=load_glove()
# rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings=theano.shared(value=rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
paragraph = T.imatrix('paragraph')
questions = T.imatrix('questions')
# labels = T.imatrix('labels') #(batch, para_len)
gold_indices= T.ivector() #batch
para_mask=T.fmatrix('para_mask')
q_mask=T.fmatrix('q_mask')
extraF=T.ftensor3('extraF') # should be in shape (batch, wordsize, 3)
is_train = T.iscalar()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
true_batch_size=paragraph.shape[0]
norm_extraF=normalize_matrix(extraF)
U1, W1, b1=create_GRU_para(rng, emb_size, hidden_size)
U1_b, W1_b, b1_b=create_GRU_para(rng, emb_size, hidden_size)
paragraph_para=[U1, W1, b1, U1_b, W1_b, b1_b]
U_e1, W_e1, b_e1=create_GRU_para(rng, 3*hidden_size+3, hidden_size)
U_e1_b, W_e1_b, b_e1_b=create_GRU_para(rng, 3*hidden_size+3, hidden_size)
paragraph_para_e1=[U_e1, W_e1, b_e1, U_e1_b, W_e1_b, b_e1_b]
UQ, WQ, bQ=create_GRU_para(rng, emb_size, hidden_size)
UQ_b, WQ_b, bQ_b=create_GRU_para(rng, emb_size, hidden_size)
Q_para=[UQ, WQ, bQ, UQ_b, WQ_b, bQ_b]
# W_a1 = create_ensemble_para(rng, hidden_size, hidden_size)# init_weights((2*hidden_size, hidden_size))
# W_a2 = create_ensemble_para(rng, hidden_size, hidden_size)
U_a = create_ensemble_para(rng, 1, 2*hidden_size) # 3 extra features
# LR_b = theano.shared(value=numpy.zeros((2,),
# dtype=theano.config.floatX), # @UndefinedVariable
# name='LR_b', borrow=True)
HL_paras=[U_a]
params = [embeddings]+paragraph_para+Q_para+paragraph_para_e1+HL_paras
load_model_from_file(rootPath+'Best_Paras_conv_50.302743614', params)
paragraph_input = embeddings[paragraph.flatten()].reshape((true_batch_size, paragraph.shape[1], emb_size)).transpose((0, 2,1)) # (batch_size, emb_size, maxparalen)
concate_paragraph_input=T.concatenate([paragraph_input, norm_extraF.dimshuffle((0,2,1))], axis=1)
paragraph_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=paragraph_input, Mask=para_mask, hidden_dim=hidden_size,U=U1,W=W1,b=b1,Ub=U1_b,Wb=W1_b,bb=b1_b)
para_reps=paragraph_model.output_tensor #(batch, emb, para_len)
# #LSTM
# fwd_LSTM_para_dict=create_LSTM_para(rng, emb_size, hidden_size)
# bwd_LSTM_para_dict=create_LSTM_para(rng, emb_size, hidden_size)
# paragraph_para=fwd_LSTM_para_dict.values()+ bwd_LSTM_para_dict.values()# .values returns a list of parameters
# paragraph_model=Bd_LSTM_Batch_Tensor_Input_with_Mask(paragraph_input, para_mask, hidden_size, fwd_LSTM_para_dict, bwd_LSTM_para_dict)
# para_reps=paragraph_model.output_tensor
Qs_emb = embeddings[questions.flatten()].reshape((true_batch_size, questions.shape[1], emb_size)).transpose((0, 2,1)) #(#questions, emb_size, maxsenlength)
questions_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=Qs_emb, Mask=q_mask, hidden_dim=hidden_size, U=UQ,W=WQ,b=bQ, Ub=UQ_b, Wb=WQ_b, bb=bQ_b)
questions_reps_tensor=questions_model.output_tensor
questions_reps=questions_model.output_sent_rep_maxpooling.reshape((true_batch_size, 1, hidden_size)) #(batch, 1, hidden)
questions_reps=T.repeat(questions_reps, para_reps.shape[2], axis=1) #(batch, para_len, hidden)
# #LSTM for questions
# fwd_LSTM_q_dict=create_LSTM_para(rng, emb_size, hidden_size)
# bwd_LSTM_q_dict=create_LSTM_para(rng, emb_size, hidden_size)
# Q_para=fwd_LSTM_q_dict.values()+ bwd_LSTM_q_dict.values()# .values returns a list of parameters
# questions_model=Bd_LSTM_Batch_Tensor_Input_with_Mask(Qs_emb, q_mask, hidden_size, fwd_LSTM_q_dict, bwd_LSTM_q_dict)
# questions_reps_tensor=questions_model.output_tensor
#
def example_in_batch(para_matrix, q_matrix):
#assume both are (hidden, len)
transpose_para_matrix=para_matrix.T
interaction_matrix=T.dot(transpose_para_matrix, q_matrix) #(para_len, q_len)
norm_interaction_matrix=T.nnet.softmax(interaction_matrix)
# norm_interaction_matrix=T.maximum(0.0, interaction_matrix)
return T.dot(q_matrix, norm_interaction_matrix.T)/T.sum(norm_interaction_matrix.T, axis=0).dimshuffle('x',0) #(len, para_len)
batch_q_reps, updates = theano.scan(fn=example_in_batch,
outputs_info=None,
sequences=[para_reps, questions_reps_tensor]) #batch_q_reps (batch, hidden, para_len)
#para_reps, batch_q_reps, questions_reps.dimshuffle(0,2,1), all are in (batch, hidden , para_len)
ensemble_para_reps_tensor=T.concatenate([para_reps, batch_q_reps, questions_reps.dimshuffle(0,2,1), norm_extraF.dimshuffle(0,2,1)], axis=1) #(batch, 3*hidden+3, para_len)
para_ensemble_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=ensemble_para_reps_tensor, Mask=para_mask, hidden_dim=hidden_size,U=U_e1,W=W_e1,b=b_e1,Ub=U_e1_b,Wb=W_e1_b,bb=b_e1_b)
para_reps_tensor4score=para_ensemble_model.output_tensor #(batch, hidden ,para_len)
para_reps_tensor4score = dropout_standard(is_train, para_reps_tensor4score, 0.2, rng)
#for span reps
span_1=T.concatenate([para_reps_tensor4score, para_reps_tensor4score], axis=1) #(batch, 2*hidden ,para_len)
span_2=T.concatenate([para_reps_tensor4score[:,:,:-1], para_reps_tensor4score[:,:,1:]], axis=1) #(batch, 2*hidden ,para_len-1)
span_3=T.concatenate([para_reps_tensor4score[:,:,:-2], para_reps_tensor4score[:,:,2:]], axis=1) #(batch, 2*hidden ,para_len-2)
span_4=T.concatenate([para_reps_tensor4score[:,:,:-3], para_reps_tensor4score[:,:,3:]], axis=1) #(batch, 2*hidden ,para_len-3)
span_5=T.concatenate([para_reps_tensor4score[:,:,:-4], para_reps_tensor4score[:,:,4:]], axis=1) #(batch, 2*hidden ,para_len-4)
span_6=T.concatenate([para_reps_tensor4score[:,:,:-5], para_reps_tensor4score[:,:,5:]], axis=1) #(batch, 2*hidden ,para_len-5)
span_7=T.concatenate([para_reps_tensor4score[:,:,:-6], para_reps_tensor4score[:,:,6:]], axis=1) #(batch, 2*hidden ,para_len-6)
span_8=T.concatenate([para_reps_tensor4score[:,:,:-7], para_reps_tensor4score[:,:,7:]], axis=1) #(batch, 2*hidden ,para_len-7)
span_9=T.concatenate([para_reps_tensor4score[:,:,:-8], para_reps_tensor4score[:,:,8:]], axis=1) #(batch, 2*hidden ,para_len-8)
span_10=T.concatenate([para_reps_tensor4score[:,:,:-9], para_reps_tensor4score[:,:,9:]], axis=1) #(batch, 2*hidden ,para_len-9)
span_11=T.concatenate([para_reps_tensor4score[:,:,:-10], para_reps_tensor4score[:,:,10:]], axis=1) #(batch, 2*hidden ,para_len-10)
span_12=T.concatenate([para_reps_tensor4score[:,:,:-11], para_reps_tensor4score[:,:,11:]], axis=1) #(batch, 2*hidden ,para_len-11)
span_13=T.concatenate([para_reps_tensor4score[:,:,:-12], para_reps_tensor4score[:,:,12:]], axis=1) #(batch, 2*hidden ,para_len-12)
span_reps=T.concatenate([span_1, span_2, span_3, span_4, span_5, span_6, span_7,
span_8, span_9, span_10, span_11, span_12, span_13], axis=2) #(batch, 2*hidden, 13*para_len-78)
test_span_reps=T.concatenate([span_1, span_2, span_3, span_4, span_5, span_6, span_7], axis=2) #(batch, 2*hidden, 5*para_len-10) #, span_6, span_7
#score each span reps
norm_U_a=normalize_matrix(U_a)
span_scores_tensor=T.dot(span_reps.dimshuffle(0,2,1), norm_U_a) #(batch, 13*para_len-78, 1)
span_scores=T.nnet.softmax(span_scores_tensor.reshape((true_batch_size, 13*paragraph.shape[1]-78))) #(batch, 7*para_len-21)
loss=-T.sum(T.log(span_scores[T.arange(true_batch_size), gold_indices]))
test_span_scores_tensor=T.dot(test_span_reps.dimshuffle(0,2,1), norm_U_a) #(batch, 7*para_len-21, 1)
test_span_scores=T.nnet.softmax(test_span_scores_tensor.reshape((true_batch_size, 7*paragraph.shape[1]-21))) #(batch, 7*para_len-21)
test_return=T.argmax(test_span_scores, axis=1) #batch
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
# L2_reg =L2norm_paraList([embeddings,U1, W1, U1_b, W1_b,UQ, WQ , UQ_b, WQ_b, W_a1, W_a2, U_a])
# L2_reg = L2norm_paraList([embeddings])
cost=loss#+ConvGRU_1.error#
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / (T.sqrt(acc)+1e-8))) #AdaGrad
updates.append((acc_i, acc))
# updates=Adam(cost, params, lr=0.0001)
train_model = theano.function([paragraph, questions,gold_indices, para_mask, q_mask, extraF, is_train], cost, updates=updates,on_unused_input='ignore')
test_model = theano.function([paragraph, questions,para_mask, q_mask, extraF, is_train], test_return, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches=train_size/batch_size
# remain_train=train_size%batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)+[train_size-batch_size]
n_test_batches=test_size/test_batch_size
# remain_test=test_size%batch_size
test_batch_start=list(numpy.arange(n_test_batches)*test_batch_size)+[test_size-test_batch_size]
max_F1_acc=0.0
max_exact_acc=0.0
cost_i=0.0
train_ids = range(train_size)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.shuffle(train_ids)
iter_accu=0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
iter_accu+=1
# haha=para_mask[para_id:para_id+batch_size]
# print haha
# for i in range(batch_size):
# print len(haha[i])
cost_i+= train_model(
numpy.asarray([train_para_list[id] for id in train_ids[para_id:para_id+batch_size]], dtype='int32'),
numpy.asarray([train_Q_list[id] for id in train_ids[para_id:para_id+batch_size]], dtype='int32'),
numpy.asarray([train_label_list[id] for id in train_ids[para_id:para_id+batch_size]], dtype='int32'),
numpy.asarray([train_para_mask[id] for id in train_ids[para_id:para_id+batch_size]], dtype=theano.config.floatX),
numpy.asarray([train_mask[id] for id in train_ids[para_id:para_id+batch_size]], dtype=theano.config.floatX),
numpy.asarray([train_feature_matrixlist[id] for id in train_ids[para_id:para_id+batch_size]], dtype=theano.config.floatX),
1)
#print iter
if iter%10==0:
print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
print 'Testing...'
past_time = time.time()
# writefile=codecs.open(rootPath+'predictions.txt', 'w', 'utf-8')
# writefile.write('{')
pred_dict={}
# exact_match=0.0
# F1_match=0.0
q_amount=0
for test_para_id in test_batch_start:
batch_predict_ids=test_model(
numpy.asarray(test_para_list[test_para_id:test_para_id+test_batch_size], dtype='int32'),
numpy.asarray(test_Q_list[test_para_id:test_para_id+test_batch_size], dtype='int32'),
numpy.asarray(test_para_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
numpy.asarray(test_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
numpy.asarray(test_feature_matrixlist[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
0)
# print distribution_matrix
test_para_wordlist_list=test_text_list[test_para_id:test_para_id+test_batch_size]
# para_gold_ansset_list=q_ansSet_list[test_para_id:test_para_id+test_batch_size]
q_ids_batch=q_idlist[test_para_id:test_para_id+test_batch_size]
# print 'q_ids_batch:', q_ids_batch
# paralist_extra_features=test_feature_matrixlist[test_para_id:test_para_id+batch_size]
# sub_para_mask=test_para_mask[test_para_id:test_para_id+batch_size]
# para_len=len(test_para_wordlist_list[0])
# if para_len!=len(distribution_matrix[0]):
# print 'para_len!=len(distribution_matrix[0]):', para_len, len(distribution_matrix[0])
# exit(0)
# q_size=len(distribution_matrix)
q_amount+=test_batch_size
# print q_size
# print test_para_word_list
# Q_list_inword=test_Q_list_word[test_para_id:test_para_id+test_batch_size]
for q in range(test_batch_size): #for each question
# if len(distribution_matrix[q])!=len(test_label_matrix[q]):
# print 'len(distribution_matrix[q])!=len(test_label_matrix[q]):', len(distribution_matrix[q]), len(test_label_matrix[q])
# else:
# ss=len(distribution_matrix[q])
# combine_list=[]
# for ii in range(ss):
# combine_list.append(str(distribution_matrix[q][ii])+'('+str(test_label_matrix[q][ii])+')')
# print combine_list
# exit(0)
# print 'distribution_matrix[q]:',distribution_matrix[q]
pred_ans=decode_predict_id(batch_predict_ids[q], test_para_wordlist_list[q])
q_id=q_ids_batch[q]
pred_dict[q_id]=pred_ans
# writefile.write('"'+str(q_id)+'": "'+pred_ans+'", ')
# pred_ans=extract_ansList_attentionList(test_para_wordlist_list[q], distribution_matrix[q], numpy.asarray(paralist_extra_features[q], dtype=theano.config.floatX), sub_para_mask[q], Q_list_inword[q])
# q_gold_ans_set=para_gold_ansset_list[q]
# # print test_para_wordlist_list[q]
# # print Q_list_inword[q]
# # print pred_ans.encode('utf8'), q_gold_ans_set
# if pred_ans in q_gold_ans_set:
# exact_match+=1
# F1=MacroF1(pred_ans, q_gold_ans_set)
# F1_match+=F1
with codecs.open(rootPath+'predictions.txt', 'w', 'utf-8') as outfile:
json.dump(pred_dict, outfile)
F1_acc, exact_acc = standard_eval(rootPath+'dev-v1.1.json', rootPath+'predictions.txt')
# F1_acc=F1_match/q_amount
# exact_acc=exact_match/q_amount
if F1_acc> max_F1_acc:
max_F1_acc=F1_acc
if exact_acc> max_exact_acc:
max_exact_acc=exact_acc
if max_exact_acc > max_EM:
store_model_to_file(rootPath+'Best_Paras_conv_'+str(max_exact_acc), params)
print 'Finished storing best params at:', max_exact_acc
print 'current average F1:', F1_acc, '\t\tmax F1:', max_F1_acc, 'current exact:', exact_acc, '\t\tmax exact_acc:', max_exact_acc
# os.system('python evaluate-v1.1.py '+rootPath+'dev-v1.1.json '+rootPath+'predictions.txt')
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
