def evaluate_lenet5(learning_rate=0.05, n_epochs=2000, nkerns=[50], batch_size=1, window_width=4,
maxSentLength=64, emb_size=300, hidden_size=200,
margin=0.5, L2_weight=0.0003, update_freq=1, norm_threshold=5.0, max_truncate=40):
maxSentLength=max_truncate+2*(window_width-1)
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/';
rng = numpy.random.RandomState(23455)
datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', max_truncate,maxSentLength)#vocab_size contain train, dev and test
#datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', maxSentLength)#vocab_size contain train, dev and test
mtPath='/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/MT/BLEU_NIST/'
mt_train, mt_test=load_mts_wikiQA(mtPath+'result_train/concate_2mt_train.txt', mtPath+'result_test/concate_2mt_test.txt')
wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores.txt', rootPath+'test_word_matching_scores.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt')
indices_train, trainY, trainLengths, normalized_train_length, trainLeftPad, trainRightPad= datasets[0]
indices_train_l=indices_train[::2,:]
indices_train_r=indices_train[1::2,:]
trainLengths_l=trainLengths[::2]
trainLengths_r=trainLengths[1::2]
normalized_train_length_l=normalized_train_length[::2]
normalized_train_length_r=normalized_train_length[1::2]
trainLeftPad_l=trainLeftPad[::2]
trainLeftPad_r=trainLeftPad[1::2]
trainRightPad_l=trainRightPad[::2]
trainRightPad_r=trainRightPad[1::2]
indices_test, testY, testLengths,normalized_test_length, testLeftPad, testRightPad= datasets[1]
indices_test_l=indices_test[::2,:]
indices_test_r=indices_test[1::2,:]
testLengths_l=testLengths[::2]
testLengths_r=testLengths[1::2]
normalized_test_length_l=normalized_test_length[::2]
normalized_test_length_r=normalized_test_length[1::2]
testLeftPad_l=testLeftPad[::2]
testLeftPad_r=testLeftPad[1::2]
testRightPad_l=testRightPad[::2]
testRightPad_r=testRightPad[1::2]
n_train_batches=indices_train_l.shape[0]/batch_size
n_test_batches=indices_test_l.shape[0]/batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)
test_batch_start=list(numpy.arange(n_test_batches)*batch_size)
indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
indices_train_l=T.cast(indices_train_l, 'int64')
indices_train_r=T.cast(indices_train_r, 'int64')
indices_test_l=T.cast(indices_test_l, 'int64')
indices_test_r=T.cast(indices_test_r, 'int64')
rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_embs_300d.txt')
#rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt')
embeddings=theano.shared(value=rand_values, borrow=True)
#cost_tmp=0
error_sum=0
# allocate symbolic variables for the data
index = T.lscalar()
x_index_l = T.lmatrix('x_index_l') # now, x is the index matrix, must be integer
x_index_r = T.lmatrix('x_index_r')
y = T.lvector('y')
left_l=T.lscalar()
right_l=T.lscalar()
left_r=T.lscalar()
right_r=T.lscalar()
length_l=T.lscalar()
length_r=T.lscalar()
norm_length_l=T.dscalar()
norm_length_r=T.dscalar()
mts=T.dmatrix()
wmf=T.dmatrix()
cost_tmp=T.dscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # this is the size of MNIST images
filter_size=(emb_size,window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_l_input = embeddings[x_index_l.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_r_input = embeddings[x_index_r.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
conv_W, conv_b=create_conv_para(rng, filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]))
#layer0_output = debug_print(layer0.output, 'layer0.output')
layer0_l = Conv_with_input_para(rng, input=layer0_l_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b)
layer0_r = Conv_with_input_para(rng, input=layer0_r_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b)
layer0_l_output=debug_print(layer0_l.output, 'layer0_l.output')
layer0_r_output=debug_print(layer0_r.output, 'layer0_r.output')
layer1=Average_Pooling_for_Top(rng, input_l=layer0_l_output, input_r=layer0_r_output, kern=nkerns[0],
left_l=left_l, right_l=right_l, left_r=left_r, right_r=right_r,
length_l=length_l+filter_size[1]-1, length_r=length_r+filter_size[1]-1,
dim=maxSentLength+filter_size[1]-1)
#layer2=HiddenLayer(rng, input=layer1_out, n_in=nkerns[0]*2, n_out=hidden_size, activation=T.tanh)
sum_uni_l=T.sum(layer0_l_input, axis=3).reshape((1, emb_size))
aver_uni_l=sum_uni_l/layer0_l_input.shape[3]
norm_uni_l=sum_uni_l/T.sqrt((sum_uni_l**2).sum())
sum_uni_r=T.sum(layer0_r_input, axis=3).reshape((1, emb_size))
aver_uni_r=sum_uni_r/layer0_r_input.shape[3]
norm_uni_r=sum_uni_r/T.sqrt((sum_uni_r**2).sum())
uni_cosine=cosine(sum_uni_l, sum_uni_r)
aver_uni_cosine=cosine(aver_uni_l, aver_uni_r)
uni_sigmoid_simi=debug_print(T.nnet.sigmoid(T.dot(norm_uni_l, norm_uni_r.T)).reshape((1,1)),'uni_sigmoid_simi')
'''
linear=Linear(sum_uni_l, sum_uni_r)
poly=Poly(sum_uni_l, sum_uni_r)
sigmoid=Sigmoid(sum_uni_l, sum_uni_r)
rbf=RBF(sum_uni_l, sum_uni_r)
gesd=GESD(sum_uni_l, sum_uni_r)
'''
eucli_1=1.0/(1.0+EUCLID(sum_uni_l, sum_uni_r))#25.2%
#eucli_1_exp=1.0/T.exp(EUCLID(sum_uni_l, sum_uni_r))
len_l=norm_length_l.reshape((1,1))
len_r=norm_length_r.reshape((1,1))
'''
len_l=length_l.reshape((1,1))
len_r=length_r.reshape((1,1))
'''
#length_gap=T.log(1+(T.sqrt((len_l-len_r)**2))).reshape((1,1))
#length_gap=T.sqrt((len_l-len_r)**2)
#layer3_input=mts
layer3_input=T.concatenate([#mts,
uni_cosine,#eucli_1_exp,#uni_sigmoid_simi, #norm_uni_l-(norm_uni_l+norm_uni_r)/2,#uni_cosine, #
layer1.output_cosine, #layer1.output_eucli_to_simi_exp,#layer1.output_sigmoid_simi,#layer1.output_vector_l-(layer1.output_vector_l+layer1.output_vector_r)/2,#layer1.output_cosine, #
len_l, len_r,wmf
], axis=1)#, layer2.output, layer1.output_cosine], axis=1)
#layer3_input=T.concatenate([mts,eucli, uni_cosine, len_l, len_r, norm_uni_l-(norm_uni_l+norm_uni_r)/2], axis=1)
#layer3=LogisticRegression(rng, input=layer3_input, n_in=11, n_out=2)
layer3=LogisticRegression(rng, input=layer3_input, n_in=(1)+(1)+2+2, n_out=2)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg =debug_print((layer3.W** 2).sum()+(conv_W** 2).sum(), 'L2_reg')#+(layer1.W** 2).sum()++(embeddings**2).sum()
cost_this =debug_print(layer3.negative_log_likelihood(y), 'cost_this')#+L2_weight*L2_reg
cost=debug_print((cost_this+cost_tmp)/update_freq+L2_weight*L2_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function([index], [layer3.prop_for_posi,layer3_input, y],
givens={
x_index_l: indices_test_l[index: index + batch_size],
x_index_r: indices_test_r[index: index + batch_size],
y: testY[index: index + batch_size],
left_l: testLeftPad_l[index],
right_l: testRightPad_l[index],
left_r: testLeftPad_r[index],
right_r: testRightPad_r[index],
length_l: testLengths_l[index],
length_r: testLengths_r[index],
norm_length_l: normalized_test_length_l[index],
norm_length_r: normalized_test_length_r[index],
mts: mt_test[index: index + batch_size],
wmf: wm_test[index: index + batch_size]}, on_unused_input='ignore')
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = layer3.params+ [conv_W, conv_b]#+[embeddings]# + layer1.params
params_conv = [conv_W, conv_b]
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i=debug_print(grad_i,'grad_i')
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([index,cost_tmp], cost, updates=updates,
givens={
x_index_l: indices_train_l[index: index + batch_size],
x_index_r: indices_train_r[index: index + batch_size],
y: trainY[index: index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index: index + batch_size],
wmf: wm_train[index: index + batch_size]}, on_unused_input='ignore')
train_model_predict = theano.function([index], [cost_this,layer3.errors(y), layer3_input, y],
givens={
x_index_l: indices_train_l[index: index + batch_size],
x_index_r: indices_train_r[index: index + batch_size],
y: trainY[index: index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index: index + batch_size],
wmf: wm_train[index: index + batch_size]}, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
epoch = 0
done_looping = False
svm_max=0.0
best_epoch=0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index=0
#shuffle(train_batch_start)#shuffle training data
cost_tmp=0.0
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index +1
minibatch_index=minibatch_index+1
#if epoch %2 ==0:
# batch_start=batch_start+remain_train
#time.sleep(0.5)
#print batch_start
if iter%update_freq != 0:
cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start)
#print 'layer3_input', layer3_input
cost_tmp+=cost_ij
error_sum+=error_ij
#print 'cost_acc ',cost_acc
#print 'cost_ij ', cost_ij
#print 'cost_tmp before update',cost_tmp
else:
cost_average= train_model(batch_start,cost_tmp)
#print 'layer3_input', layer3_input
error_sum=0
cost_tmp=0.0#reset for the next batch
#print 'cost_average ', cost_average
#print 'cost_this ',cost_this
#exit(0)
#exit(0)
if iter % n_train_batches == 0:
print 'training @ iter = '+str(iter)+' average cost: '+str(cost_average)+' error: '+str(error_sum)+'/'+str(update_freq)+' error rate: '+str(error_sum*1.0/update_freq)
#if iter ==1:
# exit(0)
if iter % validation_frequency == 0:
#write_file=open('log.txt', 'w')
test_probs=[]
test_y=[]
test_features=[]
for i in test_batch_start:
prob_i, layer3_input, y=test_model(i)
#test_losses = [test_model(i) for i in test_batch_start]
test_probs.append(prob_i[0][0])
test_y.append(y[0])
test_features.append(layer3_input[0])
MAP, MRR=compute_map_mrr(rootPath+'test_filtered.txt', test_probs)
#now, check MAP and MRR
print(('\t\t\t\t\t\tepoch %i, minibatch %i/%i, test MAP of best '
'model %f, MRR %f') %
(epoch, minibatch_index, n_train_batches,MAP, MRR))
#now, see the results of LR
#write_feature=open(rootPath+'feature_check.txt', 'w')
train_y=[]
train_features=[]
count=0
for batch_start in train_batch_start:
cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start)
train_y.append(y[0])
train_features.append(layer3_input[0])
#write_feature.write(str(batch_start)+' '+' '.join(map(str,layer3_input[0]))+'\n')
#count+=1
#write_feature.close()
clf = svm.SVC(C=1.0, kernel='linear')
clf.fit(train_features, train_y)
results_svm=clf.decision_function(test_features)
MAP_svm, MRR_svm=compute_map_mrr(rootPath+'test_filtered.txt', results_svm)
lr=LinearRegression().fit(train_features, train_y)
results_lr=lr.predict(test_features)
MAP_lr, MRR_lr=compute_map_mrr(rootPath+'test_filtered.txt', results_lr)
print '\t\t\t\t\t\t\tSVM, MAP: ', MAP_svm, ' MRR: ', MRR_svm, ' LR: ', MAP_lr, ' MRR: ', MRR_lr
if patience <= iter:
done_looping = True
break
#after each epoch, increase the batch_size
if epoch%2==1:
update_freq=update_freq*1
else:
update_freq=update_freq/1
#store the paras after epoch 15
if epoch ==15:
store_model_to_file(params_conv)
print 'Finished storing best conv params'
exit(0)
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.06,
n_epochs=2000,
nkerns=[50, 50],
batch_size=1,
window_width=[4, 4],
maxSentLength=64,
emb_size=300,
hidden_size=200,
margin=0.5,
L2_weight=0.0006,
update_freq=1,
norm_threshold=5.0,
max_truncate=40):
maxSentLength = max_truncate + 2 * (window_width[0] - 1)
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/'
rng = numpy.random.RandomState(23455)
datasets, vocab_size = load_wikiQA_corpus(
rootPath + 'vocab.txt', rootPath + 'WikiQA-train.txt',
rootPath + 'test_filtered.txt', max_truncate,
maxSentLength) #vocab_size contain train, dev and test
#datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', maxSentLength)#vocab_size contain train, dev and test
mtPath = '/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/MT/BLEU_NIST/'
mt_train, mt_test = load_mts_wikiQA(
mtPath + 'result_train/concate_2mt_train.txt',
mtPath + 'result_test/concate_2mt_test.txt')
wm_train, wm_test = load_wmf_wikiQA(
rootPath + 'train_word_matching_scores.txt',
rootPath + 'test_word_matching_scores.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt')
indices_train, trainY, trainLengths, normalized_train_length, trainLeftPad, trainRightPad = datasets[
0]
indices_train_l = indices_train[::2, :]
indices_train_r = indices_train[1::2, :]
trainLengths_l = trainLengths[::2]
trainLengths_r = trainLengths[1::2]
normalized_train_length_l = normalized_train_length[::2]
normalized_train_length_r = normalized_train_length[1::2]
trainLeftPad_l = trainLeftPad[::2]
trainLeftPad_r = trainLeftPad[1::2]
trainRightPad_l = trainRightPad[::2]
trainRightPad_r = trainRightPad[1::2]
indices_test, testY, testLengths, normalized_test_length, testLeftPad, testRightPad = datasets[
1]
indices_test_l = indices_test[::2, :]
indices_test_r = indices_test[1::2, :]
testLengths_l = testLengths[::2]
testLengths_r = testLengths[1::2]
normalized_test_length_l = normalized_test_length[::2]
normalized_test_length_r = normalized_test_length[1::2]
testLeftPad_l = testLeftPad[::2]
testLeftPad_r = testLeftPad[1::2]
testRightPad_l = testRightPad[::2]
testRightPad_r = testRightPad[1::2]
n_train_batches = indices_train_l.shape[0] / batch_size
n_test_batches = indices_test_l.shape[0] / batch_size
train_batch_start = list(numpy.arange(n_train_batches) * batch_size)
test_batch_start = list(numpy.arange(n_test_batches) * batch_size)
indices_train_l = theano.shared(numpy.asarray(indices_train_l,
dtype=theano.config.floatX),
borrow=True)
indices_train_r = theano.shared(numpy.asarray(indices_train_r,
dtype=theano.config.floatX),
borrow=True)
indices_test_l = theano.shared(numpy.asarray(indices_test_l,
dtype=theano.config.floatX),
borrow=True)
indices_test_r = theano.shared(numpy.asarray(indices_test_r,
dtype=theano.config.floatX),
borrow=True)
indices_train_l = T.cast(indices_train_l, 'int64')
indices_train_r = T.cast(indices_train_r, 'int64')
indices_test_l = T.cast(indices_test_l, 'int64')
indices_test_r = T.cast(indices_test_r, 'int64')
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
rand_values[0] = numpy.array(numpy.zeros(emb_size),
dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values = load_word2vec_to_init(rand_values,
rootPath + 'vocab_embs_300d.txt')
#rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt')
embeddings = theano.shared(value=rand_values, borrow=True)
#cost_tmp=0
error_sum = 0
# allocate symbolic variables for the data
index = T.lscalar()
x_index_l = T.lmatrix(
'x_index_l') # now, x is the index matrix, must be integer
x_index_r = T.lmatrix('x_index_r')
y = T.lvector('y')
left_l = T.lscalar()
right_l = T.lscalar()
left_r = T.lscalar()
right_r = T.lscalar()
length_l = T.lscalar()
length_r = T.lscalar()
norm_length_l = T.dscalar()
norm_length_r = T.dscalar()
mts = T.dmatrix()
wmf = T.dmatrix()
cost_tmp = T.dscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # this is the size of MNIST images
filter_size = (emb_size, window_width[0])
filter_size_2 = (nkerns[0], window_width[1])
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
length_after_wideConv = ishape[1] + filter_size[1] - 1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_l_input = embeddings[x_index_l.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_r_input = embeddings[x_index_r.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
conv_W, conv_b = create_conv_para(rng,
filter_shape=(nkerns[0], 1,
filter_size[0],
filter_size[1]))
load_model_from_file([conv_W, conv_b])
#layer0_output = debug_print(layer0.output, 'layer0.output')
layer0_l = Conv_with_input_para(rng,
input=layer0_l_input,
image_shape=(batch_size, 1, ishape[0],
ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0],
filter_size[1]),
W=conv_W,
b=conv_b)
layer0_r = Conv_with_input_para(rng,
input=layer0_r_input,
image_shape=(batch_size, 1, ishape[0],
ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0],
filter_size[1]),
W=conv_W,
b=conv_b)
layer0_l_output = debug_print(layer0_l.output, 'layer0_l.output')
layer0_r_output = debug_print(layer0_r.output, 'layer0_r.output')
layer0_para = [conv_W, conv_b]
layer1 = Average_Pooling(rng,
input_l=layer0_l_output,
input_r=layer0_r_output,
kern=nkerns[0],
left_l=left_l,
right_l=right_l,
left_r=left_r,
right_r=right_r,
length_l=length_l + filter_size[1] - 1,
length_r=length_r + filter_size[1] - 1,
dim=maxSentLength + filter_size[1] - 1,
window_size=window_width[0],
maxSentLength=maxSentLength)
conv2_W, conv2_b = create_conv_para(rng,
filter_shape=(nkerns[1], 1,
filter_size_2[0],
filter_size_2[1]))
#load_model_from_file([conv2_W, conv2_b])
layer2_l = Conv_with_input_para(
rng,
input=layer1.output_tensor_l,
image_shape=(batch_size, 1, nkerns[0], ishape[1]),
filter_shape=(nkerns[1], 1, filter_size_2[0], filter_size_2[1]),
W=conv2_W,
b=conv2_b)
layer2_r = Conv_with_input_para(
rng,
input=layer1.output_tensor_r,
image_shape=(batch_size, 1, nkerns[0], ishape[1]),
filter_shape=(nkerns[1], 1, filter_size_2[0], filter_size_2[1]),
W=conv2_W,
b=conv2_b)
layer2_para = [conv2_W, conv2_b]
layer3 = Average_Pooling_for_Top(rng,
input_l=layer2_l.output,
input_r=layer2_r.output,
kern=nkerns[1],
left_l=left_l,
right_l=right_l,
left_r=left_r,
right_r=right_r,
length_l=length_l + filter_size_2[1] - 1,
length_r=length_r + filter_size_2[1] - 1,
dim=maxSentLength + filter_size_2[1] - 1)
#layer2=HiddenLayer(rng, input=layer1_out, n_in=nkerns[0]*2, n_out=hidden_size, activation=T.tanh)
sum_uni_l = T.sum(layer0_l_input, axis=3).reshape((1, emb_size))
aver_uni_l = sum_uni_l / layer0_l_input.shape[3]
norm_uni_l = sum_uni_l / T.sqrt((sum_uni_l**2).sum())
sum_uni_r = T.sum(layer0_r_input, axis=3).reshape((1, emb_size))
aver_uni_r = sum_uni_r / layer0_r_input.shape[3]
norm_uni_r = sum_uni_r / T.sqrt((sum_uni_r**2).sum())
uni_cosine = cosine(sum_uni_l, sum_uni_r)
aver_uni_cosine = cosine(aver_uni_l, aver_uni_r)
uni_sigmoid_simi = debug_print(
T.nnet.sigmoid(T.dot(norm_uni_l, norm_uni_r.T)).reshape((1, 1)),
'uni_sigmoid_simi')
'''
linear=Linear(sum_uni_l, sum_uni_r)
poly=Poly(sum_uni_l, sum_uni_r)
sigmoid=Sigmoid(sum_uni_l, sum_uni_r)
rbf=RBF(sum_uni_l, sum_uni_r)
gesd=GESD(sum_uni_l, sum_uni_r)
'''
eucli_1 = 1.0 / (1.0 + EUCLID(sum_uni_l, sum_uni_r)) #25.2%
#eucli_1_exp=1.0/T.exp(EUCLID(sum_uni_l, sum_uni_r))
len_l = norm_length_l.reshape((1, 1))
len_r = norm_length_r.reshape((1, 1))
'''
len_l=length_l.reshape((1,1))
len_r=length_r.reshape((1,1))
'''
#length_gap=T.log(1+(T.sqrt((len_l-len_r)**2))).reshape((1,1))
#length_gap=T.sqrt((len_l-len_r)**2)
#layer3_input=mts
layer3_input = T.concatenate(
[ #mts,
uni_cosine, #eucli_1_exp,#uni_sigmoid_simi, #norm_uni_l-(norm_uni_l+norm_uni_r)/2,#uni_cosine, #
layer1.
output_cosine, #layer1.output_eucli_to_simi_exp,#layer1.output_sigmoid_simi,#layer1.output_vector_l-(layer1.output_vector_l+layer1.output_vector_r)/2,#layer1.output_cosine, #
layer3.output_cosine,
len_l,
len_r,
wmf
],
axis=1) #, layer2.output, layer1.output_cosine], axis=1)
#layer3_input=T.concatenate([mts,eucli, uni_cosine, len_l, len_r, norm_uni_l-(norm_uni_l+norm_uni_r)/2], axis=1)
#layer3=LogisticRegression(rng, input=layer3_input, n_in=11, n_out=2)
layer3 = LogisticRegression(rng,
input=layer3_input,
n_in=(1) + (1) + (1) + 2 + 2,
n_out=2)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg = debug_print(
(layer3.W**2).sum() + (conv2_W**2).sum(), 'L2_reg'
) #+(conv_W** 2).sum()+(layer1.W** 2).sum()++(embeddings**2).sum()
cost_this = debug_print(layer3.negative_log_likelihood(y),
'cost_this') #+L2_weight*L2_reg
cost = debug_print(
(cost_this + cost_tmp) / update_freq + L2_weight * L2_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function(
[index], [layer3.prop_for_posi, layer3_input, y],
givens={
x_index_l: indices_test_l[index:index + batch_size],
x_index_r: indices_test_r[index:index + batch_size],
y: testY[index:index + batch_size],
left_l: testLeftPad_l[index],
right_l: testRightPad_l[index],
left_r: testLeftPad_r[index],
right_r: testRightPad_r[index],
length_l: testLengths_l[index],
length_r: testLengths_r[index],
norm_length_l: normalized_test_length_l[index],
norm_length_r: normalized_test_length_r[index],
mts: mt_test[index:index + batch_size],
wmf: wm_test[index:index + batch_size]
},
on_unused_input='ignore')
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = layer3.params + layer2_para #+layer0_para
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i = debug_print(grad_i, 'grad_i')
acc = acc_i + T.sqr(grad_i)
updates.append(
(param_i,
param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function(
[index, cost_tmp],
cost,
updates=updates,
givens={
x_index_l: indices_train_l[index:index + batch_size],
x_index_r: indices_train_r[index:index + batch_size],
y: trainY[index:index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index:index + batch_size],
wmf: wm_train[index:index + batch_size]
},
on_unused_input='ignore')
train_model_predict = theano.function(
[index], [cost_this, layer3.errors(y), layer3_input, y],
givens={
x_index_l: indices_train_l[index:index + batch_size],
x_index_r: indices_train_r[index:index + batch_size],
y: trainY[index:index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index:index + batch_size],
wmf: wm_train[index:index + batch_size]
},
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches / 5, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
epoch = 0
done_looping = False
svm_max = 0.0
best_epoch = 0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index = 0
#shuffle(train_batch_start)#shuffle training data
cost_tmp = 0.0
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index + 1
minibatch_index = minibatch_index + 1
#if epoch %2 ==0:
# batch_start=batch_start+remain_train
#time.sleep(0.5)
#print batch_start
if iter % update_freq != 0:
cost_ij, error_ij, layer3_input, y = train_model_predict(
batch_start)
#print 'layer3_input', layer3_input
cost_tmp += cost_ij
error_sum += error_ij
#print 'cost_acc ',cost_acc
#print 'cost_ij ', cost_ij
#print 'cost_tmp before update',cost_tmp
else:
cost_average = train_model(batch_start, cost_tmp)
#print 'layer3_input', layer3_input
error_sum = 0
cost_tmp = 0.0 #reset for the next batch
#print 'cost_average ', cost_average
#print 'cost_this ',cost_this
#exit(0)
#exit(0)
if iter % n_train_batches == 0:
print 'training @ iter = ' + str(
iter) + ' average cost: ' + str(
cost_average) + ' error: ' + str(
error_sum) + '/' + str(
update_freq) + ' error rate: ' + str(
error_sum * 1.0 / update_freq)
#if iter ==1:
# exit(0)
if iter % validation_frequency == 0:
#write_file=open('log.txt', 'w')
test_probs = []
test_y = []
test_features = []
for i in test_batch_start:
prob_i, layer3_input, y = test_model(i)
#test_losses = [test_model(i) for i in test_batch_start]
test_probs.append(prob_i[0][0])
test_y.append(y[0])
test_features.append(layer3_input[0])
MAP, MRR = compute_map_mrr(rootPath + 'test_filtered.txt',
test_probs)
#now, check MAP and MRR
print(
('\t\t\t\t\t\tepoch %i, minibatch %i/%i, test MAP of best '
'model %f, MRR %f') %
(epoch, minibatch_index, n_train_batches, MAP, MRR))
#now, see the results of LR
#write_feature=open(rootPath+'feature_check.txt', 'w')
train_y = []
train_features = []
count = 0
for batch_start in train_batch_start:
cost_ij, error_ij, layer3_input, y = train_model_predict(
batch_start)
train_y.append(y[0])
train_features.append(layer3_input[0])
#write_feature.write(str(batch_start)+' '+' '.join(map(str,layer3_input[0]))+'\n')
#count+=1
#write_feature.close()
clf = svm.SVC(C=1.0, kernel='linear')
clf.fit(train_features, train_y)
results_svm = clf.decision_function(test_features)
MAP_svm, MRR_svm = compute_map_mrr(
rootPath + 'test_filtered.txt', results_svm)
lr = LinearRegression().fit(train_features, train_y)
results_lr = lr.predict(test_features)
MAP_lr, MRR_lr = compute_map_mrr(
rootPath + 'test_filtered.txt', results_lr)
print '\t\t\t\t\t\t\tSVM, MAP: ', MAP_svm, ' MRR: ', MRR_svm, ' LR: ', MAP_lr, ' MRR: ', MRR_lr
if patience <= iter:
done_looping = True
break
#after each epoch, increase the batch_size
if epoch % 2 == 1:
update_freq = update_freq * 1
else:
update_freq = update_freq / 1
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.0001, n_epochs=2000, nkerns=[256,256], batch_size=1, window_width=[4,4],
maxSentLength=64, emb_size=300, hidden_size=200,
margin=0.5, L2_weight=0.0006, Div_reg=0.06, update_freq=1, norm_threshold=5.0, max_truncate=40):
maxSentLength=max_truncate+2*(window_width[0]-1)
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/';
rng = numpy.random.RandomState(23455)
datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', max_truncate,maxSentLength)#vocab_size contain train, dev and test
#datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', maxSentLength)#vocab_size contain train, dev and test
mtPath='/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/MT/BLEU_NIST/'
mt_train, mt_test=load_mts_wikiQA(mtPath+'result_train/concate_2mt_train.txt', mtPath+'result_test/concate_2mt_test.txt')
wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores.txt', rootPath+'test_word_matching_scores.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt')
indices_train, trainY, trainLengths, normalized_train_length, trainLeftPad, trainRightPad= datasets[0]
indices_train_l=indices_train[::2,:]
indices_train_r=indices_train[1::2,:]
trainLengths_l=trainLengths[::2]
trainLengths_r=trainLengths[1::2]
normalized_train_length_l=normalized_train_length[::2]
normalized_train_length_r=normalized_train_length[1::2]
trainLeftPad_l=trainLeftPad[::2]
trainLeftPad_r=trainLeftPad[1::2]
trainRightPad_l=trainRightPad[::2]
trainRightPad_r=trainRightPad[1::2]
indices_test, testY, testLengths,normalized_test_length, testLeftPad, testRightPad= datasets[1]
indices_test_l=indices_test[::2,:]
indices_test_r=indices_test[1::2,:]
testLengths_l=testLengths[::2]
testLengths_r=testLengths[1::2]
normalized_test_length_l=normalized_test_length[::2]
normalized_test_length_r=normalized_test_length[1::2]
testLeftPad_l=testLeftPad[::2]
testLeftPad_r=testLeftPad[1::2]
testRightPad_l=testRightPad[::2]
testRightPad_r=testRightPad[1::2]
n_train_batches=indices_train_l.shape[0]/batch_size
n_test_batches=indices_test_l.shape[0]/batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)
test_batch_start=list(numpy.arange(n_test_batches)*batch_size)
indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
indices_train_l=T.cast(indices_train_l, 'int64')
indices_train_r=T.cast(indices_train_r, 'int64')
indices_test_l=T.cast(indices_test_l, 'int64')
indices_test_r=T.cast(indices_test_r, 'int64')
rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_embs_300d.txt')
embeddings=theano.shared(value=rand_values, borrow=True)
#cost_tmp=0
error_sum=0
# allocate symbolic variables for the data
index = T.lscalar()
x_index_l = T.lmatrix('x_index_l') # now, x is the index matrix, must be integer
x_index_r = T.lmatrix('x_index_r')
y = T.lvector('y')
left_l=T.lscalar()
right_l=T.lscalar()
left_r=T.lscalar()
right_r=T.lscalar()
length_l=T.lscalar()
length_r=T.lscalar()
norm_length_l=T.dscalar()
norm_length_r=T.dscalar()
mts=T.dmatrix()
wmf=T.dmatrix()
cost_tmp=T.dscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # this is the size of MNIST images
filter_size=(emb_size,window_width[0])
filter_size_2=(nkerns[0], window_width[1])
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_l_input = embeddings[x_index_l.flatten()].reshape((maxSentLength, emb_size)).transpose()
layer0_r_input = embeddings[x_index_r.flatten()].reshape((maxSentLength, emb_size)).transpose()
l_input_tensor=debug_print(Matrix_Bit_Shift(layer0_l_input[:,left_l:-right_l]), 'l_input_tensor')
r_input_tensor=debug_print(Matrix_Bit_Shift(layer0_r_input[:,left_r:-right_r]), 'r_input_tensor')
addition_l=T.sum(layer0_l_input[:,left_l:-right_l], axis=1)
addition_r=T.sum(layer0_r_input[:,left_r:-right_r], axis=1)
cosine_addition=cosine(addition_l, addition_r)
eucli_addition=1.0/(1.0+EUCLID(addition_l, addition_r))#25.2%
U, W, b=create_GRU_para(rng, emb_size, nkerns[0])
layer0_para=[U, W, b]
layer0_A1 = GRU_Batch_Tensor_Input(X=l_input_tensor, hidden_dim=nkerns[0],U=U,W=W,b=b,bptt_truncate=-1)
layer0_A2 = GRU_Batch_Tensor_Input(X=r_input_tensor, hidden_dim=nkerns[0],U=U,W=W,b=b,bptt_truncate=-1)
cosine_sent=cosine(layer0_A1.output_sent_rep, layer0_A2.output_sent_rep)
eucli_sent=1.0/(1.0+EUCLID(layer0_A1.output_sent_rep, layer0_A2.output_sent_rep))#25.2%
#ibm attentive pooling at extended sentence level
attention_matrix=compute_simi_feature_matrix_with_matrix(layer0_A1.output_matrix, layer0_A2.output_matrix, layer0_A1.dim, layer0_A2.dim, maxSentLength*(maxSentLength+1)/2)
# attention_vec_l_extended=T.nnet.softmax(T.max(attention_matrix, axis=1)).transpose()
# ibm_l_extended=layer0_A1.output_matrix.dot(attention_vec_l_extended).transpose()
# attention_vec_r_extended=T.nnet.softmax(T.max(attention_matrix, axis=0)).transpose()
# ibm_r_extended=layer0_A2.output_matrix.dot(attention_vec_r_extended).transpose()
# cosine_ibm_extended=cosine(ibm_l_extended, ibm_r_extended)
# eucli_ibm_extended=1.0/(1.0+EUCLID(ibm_l_extended, ibm_r_extended))#25.2%
#ibm attentive pooling at original sentence level
simi_matrix_sent=compute_simi_feature_matrix_with_matrix(layer0_A1.output_sent_hiddenstates, layer0_A2.output_sent_hiddenstates, length_l, length_r, maxSentLength)
attention_vec_l=T.nnet.softmax(T.max(simi_matrix_sent, axis=1)).transpose()
ibm_l=layer0_A1.output_sent_hiddenstates.dot(attention_vec_l).transpose()
attention_vec_r=T.nnet.softmax(T.max(simi_matrix_sent, axis=0)).transpose()
ibm_r=layer0_A2.output_sent_hiddenstates.dot(attention_vec_r).transpose()
cosine_ibm=cosine(ibm_l, ibm_r)
eucli_ibm=1.0/(1.0+EUCLID(ibm_l, ibm_r))#25.2%
l_max_attention=T.max(attention_matrix, axis=1)
neighborsArgSorted = T.argsort(l_max_attention)
kNeighborsArg = neighborsArgSorted[-3:]#only average the max 3 vectors
ll = T.sort(kNeighborsArg).flatten() # make y indices in acending lie
r_max_attention=T.max(attention_matrix, axis=0)
neighborsArgSorted_r = T.argsort(r_max_attention)
kNeighborsArg_r = neighborsArgSorted_r[-3:]#only average the max 3 vectors
rr = T.sort(kNeighborsArg_r).flatten() # make y indices in acending lie
l_max_min_attention=debug_print(layer0_A1.output_matrix[:,ll], 'l_max_min_attention')
r_max_min_attention=debug_print(layer0_A2.output_matrix[:,rr], 'r_max_min_attention')
U1, W1, b1=create_GRU_para(rng, nkerns[0], nkerns[1])
layer1_para=[U1, W1, b1]
layer1_A1=GRU_Matrix_Input(X=l_max_min_attention, word_dim=nkerns[0], hidden_dim=nkerns[1],U=U1,W=W1,b=b1,bptt_truncate=-1)
layer1_A2=GRU_Matrix_Input(X=r_max_min_attention, word_dim=nkerns[0], hidden_dim=nkerns[1],U=U1,W=W1,b=b1,bptt_truncate=-1)
vec_l=debug_print(layer1_A1.output_vector_last.reshape((1, nkerns[1])), 'vec_l')
vec_r=debug_print(layer1_A2.output_vector_last.reshape((1, nkerns[1])), 'vec_r')
# sum_uni_l=T.sum(layer0_l_input, axis=3).reshape((1, emb_size))
# aver_uni_l=sum_uni_l/layer0_l_input.shape[3]
# norm_uni_l=sum_uni_l/T.sqrt((sum_uni_l**2).sum())
# sum_uni_r=T.sum(layer0_r_input, axis=3).reshape((1, emb_size))
# aver_uni_r=sum_uni_r/layer0_r_input.shape[3]
# norm_uni_r=sum_uni_r/T.sqrt((sum_uni_r**2).sum())
#
uni_cosine=cosine(vec_l, vec_r)
# aver_uni_cosine=cosine(aver_uni_l, aver_uni_r)
# uni_sigmoid_simi=debug_print(T.nnet.sigmoid(T.dot(norm_uni_l, norm_uni_r.T)).reshape((1,1)),'uni_sigmoid_simi')
# '''
# linear=Linear(sum_uni_l, sum_uni_r)
# poly=Poly(sum_uni_l, sum_uni_r)
# sigmoid=Sigmoid(sum_uni_l, sum_uni_r)
# rbf=RBF(sum_uni_l, sum_uni_r)
# gesd=GESD(sum_uni_l, sum_uni_r)
# '''
eucli_1=1.0/(1.0+EUCLID(vec_l, vec_r))#25.2%
# #eucli_1_exp=1.0/T.exp(EUCLID(sum_uni_l, sum_uni_r))
#
len_l=norm_length_l.reshape((1,1))
len_r=norm_length_r.reshape((1,1))
#
# '''
# len_l=length_l.reshape((1,1))
# len_r=length_r.reshape((1,1))
# '''
#length_gap=T.log(1+(T.sqrt((len_l-len_r)**2))).reshape((1,1))
#length_gap=T.sqrt((len_l-len_r)**2)
#layer3_input=mts
layer3_input=T.concatenate([vec_l, vec_r,
uni_cosine,eucli_1,
cosine_addition, eucli_addition,
# cosine_sent, eucli_sent,
ibm_l.reshape((1, nkerns[0])), ibm_r.reshape((1, nkerns[0])), #2*nkerns[0]+
cosine_ibm, eucli_ibm,
len_l, len_r,wmf
], axis=1)#, layer2.output, layer1.output_cosine], axis=1)
#layer3_input=T.concatenate([mts,eucli, uni_cosine, len_l, len_r, norm_uni_l-(norm_uni_l+norm_uni_r)/2], axis=1)
#layer3=LogisticRegression(rng, input=layer3_input, n_in=11, n_out=2)
layer3=LogisticRegression(rng, input=layer3_input, n_in=(2*nkerns[1]+2)+2 +(2*nkerns[0]+2)+2+2, n_out=2)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg =debug_print((layer3.W** 2).sum()+(U** 2).sum()+(W** 2).sum()+(U1** 2).sum()+(W1** 2).sum(), 'L2_reg')#+(conv_W** 2).sum()+(layer1.W** 2).sum()++(embeddings**2).sum()
diversify_reg= Diversify_Reg(layer3.W.T)+Diversify_Reg(U[0])+Diversify_Reg(W[0])+Diversify_Reg(U1[0])+Diversify_Reg(W1[0])+Diversify_Reg(U[1])+Diversify_Reg(W[1])+Diversify_Reg(U1[1])+Diversify_Reg(W1[1])+Diversify_Reg(U[2])+Diversify_Reg(W[2])+Diversify_Reg(U1[2])+Diversify_Reg(W1[2])
cost_this =debug_print(layer3.negative_log_likelihood(y), 'cost_this')#+L2_weight*L2_reg
cost=debug_print((cost_this+cost_tmp)/update_freq+L2_weight*L2_reg+Div_reg*diversify_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function([index], [layer3.prop_for_posi,layer3_input, y],
givens={
x_index_l: indices_test_l[index: index + batch_size],
x_index_r: indices_test_r[index: index + batch_size],
y: testY[index: index + batch_size],
left_l: testLeftPad_l[index],
right_l: testRightPad_l[index],
left_r: testLeftPad_r[index],
right_r: testRightPad_r[index],
length_l: testLengths_l[index],
length_r: testLengths_r[index],
norm_length_l: normalized_test_length_l[index],
norm_length_r: normalized_test_length_r[index],
mts: mt_test[index: index + batch_size],
wmf: wm_test[index: index + batch_size]}, on_unused_input='ignore')
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = layer3.params+ layer1_para+layer0_para#+[embeddings]# + layer1.params
# params_conv = [conv_W, conv_b]
# accumulator=[]
# for para_i in params:
# eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
# accumulator.append(theano.shared(eps_p, borrow=True))
#
# # create a list of gradients for all model parameters
# grads = T.grad(cost, params)
#
# updates = []
# for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# grad_i=debug_print(grad_i,'grad_i')
# acc = acc_i + T.sqr(grad_i)
# updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
# updates.append((acc_i, acc))
def Adam(cost, params, lr=0.0002, b1=0.1, b2=0.001, e=1e-8):
updates = []
grads = T.grad(cost, params)
i = theano.shared(numpy.float64(0.))
i_t = i + 1.
fix1 = 1. - (1. - b1)**i_t
fix2 = 1. - (1. - b2)**i_t
lr_t = lr * (T.sqrt(fix2) / fix1)
for p, g in zip(params, grads):
m = theano.shared(p.get_value() * 0.)
v = theano.shared(p.get_value() * 0.)
m_t = (b1 * g) + ((1. - b1) * m)
v_t = (b2 * T.sqr(g)) + ((1. - b2) * v)
g_t = m_t / (T.sqrt(v_t) + e)
p_t = p - (lr_t * g_t)
updates.append((m, m_t))
updates.append((v, v_t))
updates.append((p, p_t))
updates.append((i, i_t))
return updates
updates=Adam(cost=cost, params=params, lr=learning_rate)
train_model = theano.function([index,cost_tmp], cost, updates=updates,
givens={
x_index_l: indices_train_l[index: index + batch_size],
x_index_r: indices_train_r[index: index + batch_size],
y: trainY[index: index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index: index + batch_size],
wmf: wm_train[index: index + batch_size]}, on_unused_input='ignore')
train_model_predict = theano.function([index], [cost_this,layer3.errors(y), layer3_input, y],
givens={
x_index_l: indices_train_l[index: index + batch_size],
x_index_r: indices_train_r[index: index + batch_size],
y: trainY[index: index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index: index + batch_size],
wmf: wm_train[index: index + batch_size]}, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
epoch = 0
done_looping = False
svm_max=0.0
best_epoch=0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index=0
#shuffle(train_batch_start)#shuffle training data
cost_tmp=0.0
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index +1
minibatch_index=minibatch_index+1
#if epoch %2 ==0:
# batch_start=batch_start+remain_train
#time.sleep(0.5)
# print batch_start
if iter%update_freq != 0:
cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start)
#print 'layer3_input', layer3_input
cost_tmp+=cost_ij
error_sum+=error_ij
#print 'cost_acc ',cost_acc
#print 'cost_ij ', cost_ij
#print 'cost_tmp before update',cost_tmp
else:
cost_average= train_model(batch_start,cost_tmp)
#print 'layer3_input', layer3_input
error_sum=0
cost_tmp=0.0#reset for the next batch
#print 'cost_average ', cost_average
#print 'cost_this ',cost_this
#exit(0)
#exit(0)
if iter % n_train_batches == 0:
print 'training @ iter = '+str(iter)+' average cost: '+str(cost_average)+' error: '+str(error_sum)+'/'+str(update_freq)+' error rate: '+str(error_sum*1.0/update_freq)
#if iter ==1:
# exit(0)
if iter % validation_frequency == 0:
#write_file=open('log.txt', 'w')
test_probs=[]
test_y=[]
test_features=[]
for i in test_batch_start:
prob_i, layer3_input, y=test_model(i)
#test_losses = [test_model(i) for i in test_batch_start]
test_probs.append(prob_i[0][0])
test_y.append(y[0])
test_features.append(layer3_input[0])
MAP, MRR=compute_map_mrr(rootPath+'test_filtered.txt', test_probs)
#now, check MAP and MRR
print(('\t\t\t\t\t\tepoch %i, minibatch %i/%i, test MAP of best '
'model %f, MRR %f') %
(epoch, minibatch_index, n_train_batches,MAP, MRR))
#now, see the results of LR
#write_feature=open(rootPath+'feature_check.txt', 'w')
train_y=[]
train_features=[]
count=0
for batch_start in train_batch_start:
cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start)
train_y.append(y[0])
train_features.append(layer3_input[0])
#write_feature.write(str(batch_start)+' '+' '.join(map(str,layer3_input[0]))+'\n')
#count+=1
#write_feature.close()
clf = svm.SVC(C=1.0, kernel='linear')
clf.fit(train_features, train_y)
results_svm=clf.decision_function(test_features)
MAP_svm, MRR_svm=compute_map_mrr(rootPath+'test_filtered.txt', results_svm)
lr=LinearRegression().fit(train_features, train_y)
results_lr=lr.predict(test_features)
MAP_lr, MRR_lr=compute_map_mrr(rootPath+'test_filtered.txt', results_lr)
print '\t\t\t\t\t\t\tSVM, MAP: ', MAP_svm, ' MRR: ', MRR_svm, ' LR: ', MAP_lr, ' MRR: ', MRR_lr
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
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=0.0001,
n_epochs=2000,
nkerns=[256, 256],
batch_size=1,
window_width=[4, 4],
maxSentLength=64,
emb_size=300,
hidden_size=200,
margin=0.5,
L2_weight=0.0006,
Div_reg=0.06,
update_freq=1,
norm_threshold=5.0,
max_truncate=40):
maxSentLength = max_truncate + 2 * (window_width[0] - 1)
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/'
rng = numpy.random.RandomState(23455)
datasets, vocab_size = load_wikiQA_corpus(
rootPath + 'vocab.txt', rootPath + 'WikiQA-train.txt',
rootPath + 'test_filtered.txt', max_truncate,
maxSentLength) #vocab_size contain train, dev and test
#datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', maxSentLength)#vocab_size contain train, dev and test
mtPath = '/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/MT/BLEU_NIST/'
mt_train, mt_test = load_mts_wikiQA(
mtPath + 'result_train/concate_2mt_train.txt',
mtPath + 'result_test/concate_2mt_test.txt')
wm_train, wm_test = load_wmf_wikiQA(
rootPath + 'train_word_matching_scores.txt',
rootPath + 'test_word_matching_scores.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt')
indices_train, trainY, trainLengths, normalized_train_length, trainLeftPad, trainRightPad = datasets[
0]
indices_train_l = indices_train[::2, :]
indices_train_r = indices_train[1::2, :]
trainLengths_l = trainLengths[::2]
trainLengths_r = trainLengths[1::2]
normalized_train_length_l = normalized_train_length[::2]
normalized_train_length_r = normalized_train_length[1::2]
trainLeftPad_l = trainLeftPad[::2]
trainLeftPad_r = trainLeftPad[1::2]
trainRightPad_l = trainRightPad[::2]
trainRightPad_r = trainRightPad[1::2]
indices_test, testY, testLengths, normalized_test_length, testLeftPad, testRightPad = datasets[
1]
indices_test_l = indices_test[::2, :]
indices_test_r = indices_test[1::2, :]
testLengths_l = testLengths[::2]
testLengths_r = testLengths[1::2]
normalized_test_length_l = normalized_test_length[::2]
normalized_test_length_r = normalized_test_length[1::2]
testLeftPad_l = testLeftPad[::2]
testLeftPad_r = testLeftPad[1::2]
testRightPad_l = testRightPad[::2]
testRightPad_r = testRightPad[1::2]
n_train_batches = indices_train_l.shape[0] / batch_size
n_test_batches = indices_test_l.shape[0] / batch_size
train_batch_start = list(numpy.arange(n_train_batches) * batch_size)
test_batch_start = list(numpy.arange(n_test_batches) * batch_size)
indices_train_l = theano.shared(numpy.asarray(indices_train_l,
dtype=theano.config.floatX),
borrow=True)
indices_train_r = theano.shared(numpy.asarray(indices_train_r,
dtype=theano.config.floatX),
borrow=True)
indices_test_l = theano.shared(numpy.asarray(indices_test_l,
dtype=theano.config.floatX),
borrow=True)
indices_test_r = theano.shared(numpy.asarray(indices_test_r,
dtype=theano.config.floatX),
borrow=True)
indices_train_l = T.cast(indices_train_l, 'int64')
indices_train_r = T.cast(indices_train_r, 'int64')
indices_test_l = T.cast(indices_test_l, 'int64')
indices_test_r = T.cast(indices_test_r, 'int64')
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
rand_values[0] = numpy.array(numpy.zeros(emb_size),
dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values = load_word2vec_to_init(rand_values,
rootPath + 'vocab_embs_300d.txt')
embeddings = theano.shared(value=rand_values, borrow=True)
#cost_tmp=0
error_sum = 0
# allocate symbolic variables for the data
index = T.lscalar()
x_index_l = T.lmatrix(
'x_index_l') # now, x is the index matrix, must be integer
x_index_r = T.lmatrix('x_index_r')
y = T.lvector('y')
left_l = T.lscalar()
right_l = T.lscalar()
left_r = T.lscalar()
right_r = T.lscalar()
length_l = T.lscalar()
length_r = T.lscalar()
norm_length_l = T.dscalar()
norm_length_r = T.dscalar()
mts = T.dmatrix()
wmf = T.dmatrix()
cost_tmp = T.dscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # this is the size of MNIST images
filter_size = (emb_size, window_width[0])
filter_size_2 = (nkerns[0], window_width[1])
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
length_after_wideConv = ishape[1] + filter_size[1] - 1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_l_input = embeddings[x_index_l.flatten()].reshape(
(maxSentLength, emb_size)).transpose()
layer0_r_input = embeddings[x_index_r.flatten()].reshape(
(maxSentLength, emb_size)).transpose()
l_input_tensor = debug_print(
Matrix_Bit_Shift(layer0_l_input[:, left_l:-right_l]), 'l_input_tensor')
r_input_tensor = debug_print(
Matrix_Bit_Shift(layer0_r_input[:, left_r:-right_r]), 'r_input_tensor')
addition_l = T.sum(layer0_l_input[:, left_l:-right_l], axis=1)
addition_r = T.sum(layer0_r_input[:, left_r:-right_r], axis=1)
cosine_addition = cosine(addition_l, addition_r)
eucli_addition = 1.0 / (1.0 + EUCLID(addition_l, addition_r)) #25.2%
U, W, b = create_GRU_para(rng, emb_size, nkerns[0])
layer0_para = [U, W, b]
layer0_A1 = GRU_Batch_Tensor_Input(X=l_input_tensor,
hidden_dim=nkerns[0],
U=U,
W=W,
b=b,
bptt_truncate=-1)
layer0_A2 = GRU_Batch_Tensor_Input(X=r_input_tensor,
hidden_dim=nkerns[0],
U=U,
W=W,
b=b,
bptt_truncate=-1)
cosine_sent = cosine(layer0_A1.output_sent_rep, layer0_A2.output_sent_rep)
eucli_sent = 1.0 / (1.0 + EUCLID(layer0_A1.output_sent_rep,
layer0_A2.output_sent_rep)) #25.2%
#ibm attentive pooling at extended sentence level
attention_matrix = compute_simi_feature_matrix_with_matrix(
layer0_A1.output_matrix, layer0_A2.output_matrix, layer0_A1.dim,
layer0_A2.dim,
maxSentLength * (maxSentLength + 1) / 2)
# attention_vec_l_extended=T.nnet.softmax(T.max(attention_matrix, axis=1)).transpose()
# ibm_l_extended=layer0_A1.output_matrix.dot(attention_vec_l_extended).transpose()
# attention_vec_r_extended=T.nnet.softmax(T.max(attention_matrix, axis=0)).transpose()
# ibm_r_extended=layer0_A2.output_matrix.dot(attention_vec_r_extended).transpose()
# cosine_ibm_extended=cosine(ibm_l_extended, ibm_r_extended)
# eucli_ibm_extended=1.0/(1.0+EUCLID(ibm_l_extended, ibm_r_extended))#25.2%
#ibm attentive pooling at original sentence level
simi_matrix_sent = compute_simi_feature_matrix_with_matrix(
layer0_A1.output_sent_hiddenstates, layer0_A2.output_sent_hiddenstates,
length_l, length_r, maxSentLength)
attention_vec_l = T.nnet.softmax(T.max(simi_matrix_sent,
axis=1)).transpose()
ibm_l = layer0_A1.output_sent_hiddenstates.dot(attention_vec_l).transpose()
attention_vec_r = T.nnet.softmax(T.max(simi_matrix_sent,
axis=0)).transpose()
ibm_r = layer0_A2.output_sent_hiddenstates.dot(attention_vec_r).transpose()
cosine_ibm = cosine(ibm_l, ibm_r)
eucli_ibm = 1.0 / (1.0 + EUCLID(ibm_l, ibm_r)) #25.2%
l_max_attention = T.max(attention_matrix, axis=1)
neighborsArgSorted = T.argsort(l_max_attention)
kNeighborsArg = neighborsArgSorted[-3:] #only average the max 3 vectors
ll = T.sort(kNeighborsArg).flatten() # make y indices in acending lie
r_max_attention = T.max(attention_matrix, axis=0)
neighborsArgSorted_r = T.argsort(r_max_attention)
kNeighborsArg_r = neighborsArgSorted_r[
-3:] #only average the max 3 vectors
rr = T.sort(kNeighborsArg_r).flatten() # make y indices in acending lie
l_max_min_attention = debug_print(layer0_A1.output_matrix[:, ll],
'l_max_min_attention')
r_max_min_attention = debug_print(layer0_A2.output_matrix[:, rr],
'r_max_min_attention')
U1, W1, b1 = create_GRU_para(rng, nkerns[0], nkerns[1])
layer1_para = [U1, W1, b1]
layer1_A1 = GRU_Matrix_Input(X=l_max_min_attention,
word_dim=nkerns[0],
hidden_dim=nkerns[1],
U=U1,
W=W1,
b=b1,
bptt_truncate=-1)
layer1_A2 = GRU_Matrix_Input(X=r_max_min_attention,
word_dim=nkerns[0],
hidden_dim=nkerns[1],
U=U1,
W=W1,
b=b1,
bptt_truncate=-1)
vec_l = debug_print(layer1_A1.output_vector_last.reshape((1, nkerns[1])),
'vec_l')
vec_r = debug_print(layer1_A2.output_vector_last.reshape((1, nkerns[1])),
'vec_r')
# sum_uni_l=T.sum(layer0_l_input, axis=3).reshape((1, emb_size))
# aver_uni_l=sum_uni_l/layer0_l_input.shape[3]
# norm_uni_l=sum_uni_l/T.sqrt((sum_uni_l**2).sum())
# sum_uni_r=T.sum(layer0_r_input, axis=3).reshape((1, emb_size))
# aver_uni_r=sum_uni_r/layer0_r_input.shape[3]
# norm_uni_r=sum_uni_r/T.sqrt((sum_uni_r**2).sum())
#
uni_cosine = cosine(vec_l, vec_r)
# aver_uni_cosine=cosine(aver_uni_l, aver_uni_r)
# uni_sigmoid_simi=debug_print(T.nnet.sigmoid(T.dot(norm_uni_l, norm_uni_r.T)).reshape((1,1)),'uni_sigmoid_simi')
# '''
# linear=Linear(sum_uni_l, sum_uni_r)
# poly=Poly(sum_uni_l, sum_uni_r)
# sigmoid=Sigmoid(sum_uni_l, sum_uni_r)
# rbf=RBF(sum_uni_l, sum_uni_r)
# gesd=GESD(sum_uni_l, sum_uni_r)
# '''
eucli_1 = 1.0 / (1.0 + EUCLID(vec_l, vec_r)) #25.2%
# #eucli_1_exp=1.0/T.exp(EUCLID(sum_uni_l, sum_uni_r))
#
len_l = norm_length_l.reshape((1, 1))
len_r = norm_length_r.reshape((1, 1))
#
# '''
# len_l=length_l.reshape((1,1))
# len_r=length_r.reshape((1,1))
# '''
#length_gap=T.log(1+(T.sqrt((len_l-len_r)**2))).reshape((1,1))
#length_gap=T.sqrt((len_l-len_r)**2)
#layer3_input=mts
layer3_input = T.concatenate(
[
vec_l,
vec_r,
uni_cosine,
eucli_1,
cosine_addition,
eucli_addition,
# cosine_sent, eucli_sent,
ibm_l.reshape((1, nkerns[0])),
ibm_r.reshape((1, nkerns[0])), #2*nkerns[0]+
cosine_ibm,
eucli_ibm,
len_l,
len_r,
wmf
],
axis=1) #, layer2.output, layer1.output_cosine], axis=1)
#layer3_input=T.concatenate([mts,eucli, uni_cosine, len_l, len_r, norm_uni_l-(norm_uni_l+norm_uni_r)/2], axis=1)
#layer3=LogisticRegression(rng, input=layer3_input, n_in=11, n_out=2)
layer3 = LogisticRegression(rng,
input=layer3_input,
n_in=(2 * nkerns[1] + 2) + 2 +
(2 * nkerns[0] + 2) + 2 + 2,
n_out=2)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg = debug_print(
(layer3.W**2).sum() + (U**2).sum() + (W**2).sum() + (U1**2).sum() +
(W1**2).sum(), 'L2_reg'
) #+(conv_W** 2).sum()+(layer1.W** 2).sum()++(embeddings**2).sum()
diversify_reg = Diversify_Reg(layer3.W.T) + Diversify_Reg(
U[0]) + Diversify_Reg(W[0]) + Diversify_Reg(U1[0]) + Diversify_Reg(
W1[0]) + Diversify_Reg(U[1]) + Diversify_Reg(W[1]) + Diversify_Reg(
U1[1]) + Diversify_Reg(W1[1]) + Diversify_Reg(
U[2]) + Diversify_Reg(W[2]) + Diversify_Reg(
U1[2]) + Diversify_Reg(W1[2])
cost_this = debug_print(layer3.negative_log_likelihood(y),
'cost_this') #+L2_weight*L2_reg
cost = debug_print((cost_this + cost_tmp) / update_freq +
L2_weight * L2_reg + Div_reg * diversify_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function(
[index], [layer3.prop_for_posi, layer3_input, y],
givens={
x_index_l: indices_test_l[index:index + batch_size],
x_index_r: indices_test_r[index:index + batch_size],
y: testY[index:index + batch_size],
left_l: testLeftPad_l[index],
right_l: testRightPad_l[index],
left_r: testLeftPad_r[index],
right_r: testRightPad_r[index],
length_l: testLengths_l[index],
length_r: testLengths_r[index],
norm_length_l: normalized_test_length_l[index],
norm_length_r: normalized_test_length_r[index],
mts: mt_test[index:index + batch_size],
wmf: wm_test[index:index + batch_size]
},
on_unused_input='ignore')
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = layer3.params + layer1_para + layer0_para #+[embeddings]# + layer1.params
# params_conv = [conv_W, conv_b]
# accumulator=[]
# for para_i in params:
# eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
# accumulator.append(theano.shared(eps_p, borrow=True))
#
# # create a list of gradients for all model parameters
# grads = T.grad(cost, params)
#
# updates = []
# for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# grad_i=debug_print(grad_i,'grad_i')
# acc = acc_i + T.sqr(grad_i)
# updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
# updates.append((acc_i, acc))
def Adam(cost, params, lr=0.0002, b1=0.1, b2=0.001, e=1e-8):
updates = []
grads = T.grad(cost, params)
i = theano.shared(numpy.float64(0.))
i_t = i + 1.
fix1 = 1. - (1. - b1)**i_t
fix2 = 1. - (1. - b2)**i_t
lr_t = lr * (T.sqrt(fix2) / fix1)
for p, g in zip(params, grads):
m = theano.shared(p.get_value() * 0.)
v = theano.shared(p.get_value() * 0.)
m_t = (b1 * g) + ((1. - b1) * m)
v_t = (b2 * T.sqr(g)) + ((1. - b2) * v)
g_t = m_t / (T.sqrt(v_t) + e)
p_t = p - (lr_t * g_t)
updates.append((m, m_t))
updates.append((v, v_t))
updates.append((p, p_t))
updates.append((i, i_t))
return updates
updates = Adam(cost=cost, params=params, lr=learning_rate)
train_model = theano.function(
[index, cost_tmp],
cost,
updates=updates,
givens={
x_index_l: indices_train_l[index:index + batch_size],
x_index_r: indices_train_r[index:index + batch_size],
y: trainY[index:index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index:index + batch_size],
wmf: wm_train[index:index + batch_size]
},
on_unused_input='ignore')
train_model_predict = theano.function(
[index], [cost_this, layer3.errors(y), layer3_input, y],
givens={
x_index_l: indices_train_l[index:index + batch_size],
x_index_r: indices_train_r[index:index + batch_size],
y: trainY[index:index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index:index + batch_size],
wmf: wm_train[index:index + batch_size]
},
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
epoch = 0
done_looping = False
svm_max = 0.0
best_epoch = 0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index = 0
#shuffle(train_batch_start)#shuffle training data
cost_tmp = 0.0
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index + 1
minibatch_index = minibatch_index + 1
#if epoch %2 ==0:
# batch_start=batch_start+remain_train
#time.sleep(0.5)
# print batch_start
if iter % update_freq != 0:
cost_ij, error_ij, layer3_input, y = train_model_predict(
batch_start)
#print 'layer3_input', layer3_input
cost_tmp += cost_ij
error_sum += error_ij
#print 'cost_acc ',cost_acc
#print 'cost_ij ', cost_ij
#print 'cost_tmp before update',cost_tmp
else:
cost_average = train_model(batch_start, cost_tmp)
#print 'layer3_input', layer3_input
error_sum = 0
cost_tmp = 0.0 #reset for the next batch
#print 'cost_average ', cost_average
#print 'cost_this ',cost_this
#exit(0)
#exit(0)
if iter % n_train_batches == 0:
print 'training @ iter = ' + str(
iter) + ' average cost: ' + str(
cost_average) + ' error: ' + str(
error_sum) + '/' + str(
update_freq) + ' error rate: ' + str(
error_sum * 1.0 / update_freq)
#if iter ==1:
# exit(0)
if iter % validation_frequency == 0:
#write_file=open('log.txt', 'w')
test_probs = []
test_y = []
test_features = []
for i in test_batch_start:
prob_i, layer3_input, y = test_model(i)
#test_losses = [test_model(i) for i in test_batch_start]
test_probs.append(prob_i[0][0])
test_y.append(y[0])
test_features.append(layer3_input[0])
MAP, MRR = compute_map_mrr(rootPath + 'test_filtered.txt',
test_probs)
#now, check MAP and MRR
print(
('\t\t\t\t\t\tepoch %i, minibatch %i/%i, test MAP of best '
'model %f, MRR %f') %
(epoch, minibatch_index, n_train_batches, MAP, MRR))
#now, see the results of LR
#write_feature=open(rootPath+'feature_check.txt', 'w')
train_y = []
train_features = []
count = 0
for batch_start in train_batch_start:
cost_ij, error_ij, layer3_input, y = train_model_predict(
batch_start)
train_y.append(y[0])
train_features.append(layer3_input[0])
#write_feature.write(str(batch_start)+' '+' '.join(map(str,layer3_input[0]))+'\n')
#count+=1
#write_feature.close()
clf = svm.SVC(C=1.0, kernel='linear')
clf.fit(train_features, train_y)
results_svm = clf.decision_function(test_features)
MAP_svm, MRR_svm = compute_map_mrr(
rootPath + 'test_filtered.txt', results_svm)
lr = LinearRegression().fit(train_features, train_y)
results_lr = lr.predict(test_features)
MAP_lr, MRR_lr = compute_map_mrr(
rootPath + 'test_filtered.txt', results_lr)
print '\t\t\t\t\t\t\tSVM, MAP: ', MAP_svm, ' MRR: ', MRR_svm, ' LR: ', MAP_lr, ' MRR: ', MRR_lr
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
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.))