def evaluate_lenet5(learning_rate=0.02,
n_epochs=100,
emb_size=300,
batch_size=10,
filter_size=[3, 5],
maxSentLen=40,
hidden_size=[300, 300]):
model_options = locals().copy()
print "model options", model_options
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
srng = T.shared_randomstreams.RandomStreams(rng.randint(seed))
all_sentences, all_masks, all_labels, word2id = load_BBN_multi_labels_dataset(
maxlen=maxSentLen
) #minlen, include one label, at least one word in the sentence
train_sents = np.asarray(all_sentences[0], dtype='int32')
train_masks = np.asarray(all_masks[0], dtype=theano.config.floatX)
train_labels = np.asarray(all_labels[0], dtype='int32')
train_size = len(train_labels)
dev_sents = np.asarray(all_sentences[1], dtype='int32')
dev_masks = np.asarray(all_masks[1], dtype=theano.config.floatX)
dev_labels = np.asarray(all_labels[1], dtype='int32')
dev_size = len(dev_labels)
test_sents = np.asarray(all_sentences[2], dtype='int32')
test_masks = np.asarray(all_masks[2], dtype=theano.config.floatX)
test_labels = np.asarray(all_labels[2], dtype='int32')
test_size = len(test_labels)
vocab_size = len(word2id) + 1 # add one zero pad index
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
rand_values[0] = np.array(np.zeros(emb_size), dtype=theano.config.floatX)
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_word2vec()
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
#now, start to build the input form of the model
sents_id_matrix = T.imatrix('sents_id_matrix')
sents_mask = T.fmatrix('sents_mask')
labels = T.imatrix('labels') #batch*12
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
common_input = embeddings[sents_id_matrix.flatten()].reshape(
(batch_size, maxSentLen, emb_size)).dimshuffle(
0, 2, 1) #the input format can be adapted into CNN or GRU or LSTM
conv_W, conv_b = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size, filter_size[0]))
conv_W2, conv_b2 = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size,
filter_size[1]))
NN_para = [conv_W, conv_b, conv_W2, conv_b2]
conv_model = Conv_with_Mask(
rng,
input_tensor3=common_input,
mask_matrix=sents_mask,
image_shape=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
W=conv_W,
b=conv_b
) #mutiple mask with the conv_out to set the features by UNK to zero
sent_embeddings = conv_model.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
conv_model2 = Conv_with_Mask(
rng,
input_tensor3=common_input,
mask_matrix=sents_mask,
image_shape=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[1]),
W=conv_W2,
b=conv_b2
) #mutiple mask with the conv_out to set the features by UNK to zero
sent_embeddings2 = conv_model2.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
LR_input = T.concatenate([sent_embeddings, sent_embeddings2], axis=1)
LR_input_size = hidden_size[0] * 2
#classification layer, it is just mapping from a feature vector of size "hidden_size" to a vector of only two values: positive, negative
U_a = create_ensemble_para(
rng, 12, LR_input_size) # the weight matrix hidden_size*2
LR_b = theano.shared(value=np.zeros((12, ), dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
LR_para = [U_a, LR_b]
layer_LR = LogisticRegression(
rng, input=LR_input, n_in=LR_input_size, n_out=12, W=U_a, b=LR_b
) #basically it is a multiplication between weight matrix and input feature vector
score_matrix = T.nnet.sigmoid(layer_LR.before_softmax) #batch * 12
prob_pos = T.where(labels < 1, 1.0 - score_matrix, score_matrix)
loss = -T.mean(T.log(prob_pos))
# loss=layer_LR.negative_log_likelihood(labels) #for classification task, we usually used negative log likelihood as loss, the lower the better.
params = [embeddings
] + NN_para + LR_para # put all model parameters together
cost = loss #+Div_reg*diversify_reg#+L2_weight*L2_reg
updates = Gradient_Cost_Para(cost, params, learning_rate)
'''
testing
'''
binarize_prob = T.where(score_matrix > 0.5, 1, 0)
#train_model = theano.function([sents_id_matrix, sents_mask, labels], cost, updates=updates, on_unused_input='ignore')
train_model = theano.function([sents_id_matrix, sents_mask, labels],
cost,
updates=updates,
allow_input_downcast=True,
on_unused_input='ignore')
# dev_model = theano.function([sents_id_matrix, sents_mask, labels], layer_LR.errors(labels), allow_input_downcast=True, on_unused_input='ignore')
test_model = theano.function([sents_id_matrix, sents_mask],
binarize_prob,
allow_input_downcast=True,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
n_train_batches = train_size / batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
# n_dev_batches=dev_size/batch_size
# dev_batch_start=list(np.arange(n_dev_batches)*batch_size)+[dev_size-batch_size]
n_test_batches = test_size / batch_size
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
# max_acc_dev=0.0
max_meanf1_test = 0.0
max_weightf1_test = 0.0
train_indices = range(train_size)
while epoch < n_epochs:
epoch = epoch + 1
random.Random(100).shuffle(train_indices)
iter_accu = 0
cost_i = 0.0
for batch_id in train_batch_start: #for each batch
# iter means how many batches have been run, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
train_id_batch = train_indices[batch_id:batch_id + batch_size]
cost_i += train_model(train_sents[train_id_batch],
train_masks[train_id_batch],
train_labels[train_id_batch])
#after each 1000 batches, we test the performance of the model on all test data
if iter % 20 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
past_time = time.time()
error_sum = 0.0
all_pred_labels = []
all_gold_labels = []
for test_batch_id in test_batch_start: # for each test batch
pred_labels = test_model(
test_sents[test_batch_id:test_batch_id + batch_size],
test_masks[test_batch_id:test_batch_id + batch_size])
gold_labels = test_labels[test_batch_id:test_batch_id +
batch_size]
all_pred_labels.append(pred_labels)
all_gold_labels.append(gold_labels)
all_pred_labels = np.concatenate(all_pred_labels)
all_gold_labels = np.concatenate(all_gold_labels)
test_mean_f1, test_weight_f1 = average_f1_two_array_by_col(
all_pred_labels, all_gold_labels)
if test_weight_f1 > max_weightf1_test:
max_weightf1_test = test_weight_f1
if test_mean_f1 > max_meanf1_test:
max_meanf1_test = test_mean_f1
print '\t\t\t\t\t\t\t\tcurrent f1s:', test_mean_f1, test_weight_f1, '\t\tmax_f1:', max_meanf1_test, max_weightf1_test
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
return max_acc_test
def evaluate_lenet5(learning_rate=0.02,
n_epochs=100,
emb_size=300,
batch_size=50,
filter_size=[3],
sent_len=40,
claim_len=40,
cand_size=10,
hidden_size=[300, 300],
max_pred_pick=5):
model_options = locals().copy()
print "model options", model_options
pred_id2label = {1: 'SUPPORTS', 0: 'REFUTES', 2: 'NOT ENOUGH INFO'}
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
srng = T.shared_randomstreams.RandomStreams(rng.randint(seed))
"load raw data"
train_sents, train_sent_masks, train_sent_labels, train_claims, train_claim_mask, train_labels, word2id = load_fever_train(
sent_len, claim_len, cand_size)
train_3th_sents, train_3th_sent_masks, train_3th_sent_labels, train_3th_claims, train_3th_claim_mask, train_3th_labels, word2id = load_fever_train_NoEnoughInfo(
sent_len, claim_len, cand_size, word2id)
test_sents, test_sent_masks, test_sent_labels, test_claims, test_claim_mask, test_sent_names, test_ground_names, test_labels, word2id = load_fever_dev(
sent_len, claim_len, cand_size, word2id)
test_3th_sents, test_3th_sent_masks, test_3th_sent_labels, test_3th_claims, test_3th_claim_mask, test_3th_labels, word2id = load_fever_dev_NoEnoughInfo(
sent_len, claim_len, cand_size, word2id)
train_sents = np.asarray(train_sents, dtype='int32')
train_3th_sents = np.asarray(train_3th_sents, dtype='int32')
joint_train_sents = np.concatenate((train_sents, train_3th_sents))
test_sents = np.asarray(test_sents, dtype='int32')
test_3th_sents = np.asarray(test_3th_sents, dtype='int32')
joint_test_sents = np.concatenate((test_sents, test_3th_sents))
train_sent_masks = np.asarray(train_sent_masks, dtype=theano.config.floatX)
train_3th_sent_masks = np.asarray(train_3th_sent_masks,
dtype=theano.config.floatX)
joint_train_sent_masks = np.concatenate(
(train_sent_masks, train_3th_sent_masks))
test_sent_masks = np.asarray(test_sent_masks, dtype=theano.config.floatX)
test_3th_sent_masks = np.asarray(test_3th_sent_masks,
dtype=theano.config.floatX)
joint_test_sent_masks = np.concatenate(
(test_sent_masks, test_3th_sent_masks))
train_sent_labels = np.asarray(train_sent_labels, dtype='int32')
train_3th_sent_labels = np.asarray(train_3th_sent_labels, dtype='int32')
joint_train_sent_labels = np.concatenate(
(train_sent_labels, train_3th_sent_labels))
test_sent_labels = np.asarray(test_sent_labels, dtype='int32')
test_3th_sent_labels = np.asarray(test_3th_sent_labels, dtype='int32')
joint_test_sent_labels = np.concatenate(
(test_sent_labels, test_3th_sent_labels))
train_claims = np.asarray(train_claims, dtype='int32')
train_3th_claims = np.asarray(train_3th_claims, dtype='int32')
joint_train_claims = np.concatenate((train_claims, train_3th_claims))
test_claims = np.asarray(test_claims, dtype='int32')
test_3th_claims = np.asarray(test_3th_claims, dtype='int32')
joint_test_claims = np.concatenate((test_claims, test_3th_claims))
train_claim_mask = np.asarray(train_claim_mask, dtype=theano.config.floatX)
train_3th_claim_mask = np.asarray(train_3th_claim_mask,
dtype=theano.config.floatX)
joint_train_claim_mask = np.concatenate(
(train_claim_mask, train_3th_claim_mask))
test_claim_mask = np.asarray(test_claim_mask, dtype=theano.config.floatX)
test_3th_claim_mask = np.asarray(test_3th_claim_mask,
dtype=theano.config.floatX)
joint_test_claim_mask = np.concatenate(
(test_claim_mask, test_3th_claim_mask))
train_labels = np.asarray(train_labels, dtype='int32')
train_3th_labels = np.asarray(train_3th_labels, dtype='int32')
joint_train_labels = np.concatenate((train_labels, train_3th_labels))
test_labels = np.asarray(test_labels, dtype='int32')
test_3th_labels = np.asarray(test_3th_labels, dtype='int32')
joint_test_labels = np.concatenate((test_labels, test_3th_labels))
joint_train_size = len(joint_train_claims)
joint_test_size = len(joint_test_claims)
train_size = len(train_claims)
test_size = len(test_claims)
test_3th_size = len(test_3th_claims)
vocab_size = len(word2id) + 1
print 'joint_train size: ', joint_train_size, ' joint_test size: ', joint_test_size
print 'train size: ', train_size, ' test size: ', test_size
print 'vocab size: ', vocab_size
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_word2vec()
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
init_embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
"now, start to build the input form of the model"
sents_ids = T.itensor3() #(batch, cand_size, sent_len)
sents_mask = T.ftensor3()
sents_labels = T.imatrix() #(batch, cand_size)
claim_ids = T.imatrix() #(batch, claim_len)
claim_mask = T.fmatrix()
joint_sents_ids = T.itensor3() #(batch, cand_size, sent_len)
joint_sents_mask = T.ftensor3()
joint_sents_labels = T.imatrix() #(batch, cand_size)
joint_claim_ids = T.imatrix() #(batch, claim_len)
joint_claim_mask = T.fmatrix()
joint_labels = T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
embed_input_sents = init_embeddings[sents_ids.flatten(
)].reshape((batch_size * cand_size, sent_len, emb_size)).dimshuffle(
0, 2, 1
) #embed_input(init_embeddings, sents_ids_l)#embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
embed_input_claim = init_embeddings[claim_ids.flatten()].reshape(
(batch_size, claim_len, emb_size)).dimshuffle(0, 2, 1)
conv_W, conv_b = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size, filter_size[0]))
att_conv_W, att_conv_b = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size,
filter_size[0]))
conv_W_context, conv_b_context = create_conv_para(
rng, filter_shape=(hidden_size[0], 1, emb_size, 1))
NN_para = [conv_W, conv_b, att_conv_W, att_conv_b, conv_W_context]
conv_model_sents = Conv_with_Mask(
rng,
input_tensor3=embed_input_sents,
mask_matrix=sents_mask.reshape(
(sents_mask.shape[0] * sents_mask.shape[1], sents_mask.shape[2])),
image_shape=(batch_size * cand_size, 1, emb_size, sent_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
W=conv_W,
b=conv_b
) #mutiple mask with the conv_out to set the features by UNK to zero
sent_embeddings = conv_model_sents.maxpool_vec #(batch_size*cand_size, hidden_size) # each sentence then have an embedding of length hidden_size
batch_sent_emb = sent_embeddings.reshape(
(batch_size, cand_size, hidden_size[0]))
conv_model_claims = Conv_with_Mask(
rng,
input_tensor3=embed_input_claim,
mask_matrix=claim_mask,
image_shape=(batch_size, 1, emb_size, claim_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
W=conv_W,
b=conv_b
) #mutiple mask with the conv_out to set the features by UNK to zero
claim_embeddings = conv_model_claims.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
batch_claim_emb = T.repeat(claim_embeddings.dimshuffle(0, 'x', 1),
cand_size,
axis=1)
# concate_claim_sent = T.concatenate([batch_claim_emb,batch_sent_emb ], axis=2)
# concate_2_matrix = concate_claim_sent.reshape((batch_size*cand_size, hidden_size[0]*2))
concate_claim_sent = T.concatenate([
batch_claim_emb, batch_sent_emb,
T.sum(batch_claim_emb * batch_sent_emb, axis=2).dimshuffle(0, 1, 'x')
],
axis=2)
concate_2_matrix = concate_claim_sent.reshape(
(batch_size * cand_size, hidden_size[0] * 2 + 1))
LR_input = concate_2_matrix
LR_input_size = hidden_size[0] * 2 + 1
#classification layer, it is just mapping from a feature vector of size "hidden_size" to a vector of only two values: positive, negative
U_a = create_ensemble_para(
rng, 1, LR_input_size) # the weight matrix hidden_size*2
# LR_b = theano.shared(value=np.zeros((8,),dtype=theano.config.floatX),name='LR_b', borrow=True) #bias for each target class
LR_para = [U_a]
# layer_LR=LogisticRegression(rng, input=LR_input, n_in=LR_input_size, n_out=8, W=U_a, b=LR_b) #basically it is a multiplication between weight matrix and input feature vector
score_matrix = T.nnet.sigmoid(LR_input.dot(U_a)) #batch * 12
inter_matrix = score_matrix.reshape((batch_size, cand_size))
# inter_sent_claim = T.batched_dot(batch_sent_emb, batch_claim_emb) #(batch_size, cand_size, 1)
# inter_matrix = T.nnet.sigmoid(inter_sent_claim.reshape((batch_size, cand_size)))
'''
maybe 1.0-inter_matrix can be rewritten into 1/e^(inter_matrix)
'''
# prob_pos = T.where( sents_labels < 1, 1.0-inter_matrix, inter_matrix)
# loss = -T.mean(T.log(prob_pos))
#f1 as loss
batch_overlap = T.sum(sents_labels * inter_matrix, axis=1)
batch_recall = batch_overlap / T.sum(sents_labels, axis=1)
batch_precision = batch_overlap / T.sum(inter_matrix, axis=1)
batch_f1 = 2.0 * batch_recall * batch_precision / (batch_recall +
batch_precision)
loss = -T.mean(T.log(batch_f1))
# loss = T.nnet.nnet.binary_crossentropy(inter_matrix, sents_labels).mean()
'''
training task2, predict 3 labels
'''
joint_embed_input_sents = init_embeddings[joint_sents_ids.flatten(
)].reshape((batch_size * cand_size, sent_len, emb_size)).dimshuffle(
0, 2, 1
) #embed_input(init_embeddings, sents_ids_l)#embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
joint_embed_input_claim = init_embeddings[
joint_claim_ids.flatten()].reshape(
(batch_size, claim_len, emb_size)).dimshuffle(0, 2, 1)
joint_conv_model_sents = Conv_with_Mask(
rng,
input_tensor3=joint_embed_input_sents,
mask_matrix=joint_sents_mask.reshape(
(joint_sents_mask.shape[0] * joint_sents_mask.shape[1],
joint_sents_mask.shape[2])),
image_shape=(batch_size * cand_size, 1, emb_size, sent_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
W=conv_W,
b=conv_b
) #mutiple mask with the conv_out to set the features by UNK to zero
joint_sent_embeddings = joint_conv_model_sents.maxpool_vec #(batch_size*cand_size, hidden_size) # each sentence then have an embedding of length hidden_size
joint_batch_sent_emb = joint_sent_embeddings.reshape(
(batch_size, cand_size, hidden_size[0]))
joint_premise_emb = T.sum(joint_batch_sent_emb *
joint_sents_labels.dimshuffle(0, 1, 'x'),
axis=1) #(batch, hidden_size)
joint_conv_model_claims = Conv_with_Mask(
rng,
input_tensor3=joint_embed_input_claim,
mask_matrix=joint_claim_mask,
image_shape=(batch_size, 1, emb_size, claim_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
W=conv_W,
b=conv_b
) #mutiple mask with the conv_out to set the features by UNK to zero
joint_claim_embeddings = joint_conv_model_claims.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
joint_premise_hypo_emb = T.concatenate(
[joint_premise_emb, joint_claim_embeddings],
axis=1) #(batch, 2*hidden_size)
'''
attentive conv in task2
'''
joint_sents_tensor3 = joint_embed_input_sents.dimshuffle(0, 2, 1).reshape(
(batch_size, cand_size * sent_len, emb_size))
joint_sents_dot = T.batched_dot(
joint_sents_tensor3, joint_sents_tensor3.dimshuffle(
0, 2, 1)) #(batch_size, cand_size*sent_len, cand_size*sent_len)
joint_sents_dot_2_matrix = T.nnet.softmax(
joint_sents_dot.reshape(
(batch_size * cand_size * sent_len, cand_size * sent_len)))
joint_sents_context = T.batched_dot(
joint_sents_dot_2_matrix.reshape(
(batch_size, cand_size * sent_len, cand_size * sent_len)),
joint_sents_tensor3) #(batch_size, cand_size*sent_len, emb_size)
joint_add_sents_context = joint_embed_input_sents + joint_sents_context.reshape(
(batch_size * cand_size, sent_len, emb_size)
).dimshuffle(
0, 2, 1
) #T.concatenate([joint_embed_input_sents, joint_sents_context.reshape((batch_size*cand_size, sent_len, emb_size)).dimshuffle(0,2,1)], axis=1) #(batch_size*cand_size, 2*emb_size, sent_len)
attentive_conv_layer = Attentive_Conv_for_Pair_easy_version(
rng,
input_tensor3=
joint_add_sents_context, #batch_size*cand_size, 2*emb_size, sent_len
input_tensor3_r=T.repeat(joint_embed_input_claim, cand_size, axis=0),
mask_matrix=joint_sents_mask.reshape(
(joint_sents_mask.shape[0] * joint_sents_mask.shape[1],
joint_sents_mask.shape[2])),
mask_matrix_r=T.repeat(joint_claim_mask, cand_size, axis=0),
image_shape=(batch_size * cand_size, 1, emb_size, sent_len),
image_shape_r=(batch_size * cand_size, 1, emb_size, claim_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[0], 1, emb_size, 1),
W=att_conv_W,
b=att_conv_b,
W_context=conv_W_context,
b_context=conv_b_context)
attentive_sent_embeddings_l = attentive_conv_layer.attentive_maxpool_vec_l.reshape(
(batch_size, cand_size,
hidden_size[0])) #(batch_size*cand_size, hidden_size)
attentive_sent_embeddings_r = attentive_conv_layer.attentive_maxpool_vec_r.reshape(
(batch_size, cand_size, hidden_size[0]))
masked_sents_attconv = attentive_sent_embeddings_l * joint_sents_labels.dimshuffle(
0, 1, 'x')
masked_claim_attconv = attentive_sent_embeddings_r * joint_sents_labels.dimshuffle(
0, 1, 'x')
fine_max = T.concatenate([
T.max(masked_sents_attconv, axis=1),
T.max(masked_claim_attconv, axis=1)
],
axis=1) #(batch, 2*hidden)
# fine_sum = T.concatenate([T.sum(masked_sents_attconv, axis=1),T.sum(masked_claim_attconv, axis=1)],axis=1) #(batch, 2*hidden)
"Logistic Regression layer"
joint_LR_input = T.concatenate([joint_premise_hypo_emb, fine_max], axis=1)
joint_LR_input_size = 2 * hidden_size[0] + 2 * hidden_size[0]
joint_U_a = create_ensemble_para(rng, 3,
joint_LR_input_size) # (input_size, 3)
joint_LR_b = theano.shared(value=np.zeros((3, ),
dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
joint_LR_para = [joint_U_a, joint_LR_b]
joint_layer_LR = LogisticRegression(
rng,
input=joint_LR_input,
n_in=joint_LR_input_size,
n_out=3,
W=joint_U_a,
b=joint_LR_b
) #basically it is a multiplication between weight matrix and input feature vector
joint_loss = joint_layer_LR.negative_log_likelihood(
joint_labels
) #for classification task, we usually used negative log likelihood as loss, the lower the better.
'''
testing
'''
# binarize_prob = T.where( inter_matrix > 0.5, 1, 0) #(batch_size, cand_size
masked_inter_matrix = inter_matrix * sents_labels #(batch, cand_size)
test_premise_emb = T.sum(batch_sent_emb *
masked_inter_matrix.dimshuffle(0, 1, 'x'),
axis=1)
test_premise_hypo_emb = T.concatenate([test_premise_emb, claim_embeddings],
axis=1)
#fine-maxsum
sents_tensor3 = embed_input_sents.dimshuffle(0, 2, 1).reshape(
(batch_size, cand_size * sent_len, emb_size))
sents_dot = T.batched_dot(sents_tensor3, sents_tensor3.dimshuffle(
0, 2, 1)) #(batch_size, cand_size*sent_len, cand_size*sent_len)
sents_dot_2_matrix = T.nnet.softmax(
sents_dot.reshape(
(batch_size * cand_size * sent_len, cand_size * sent_len)))
sents_context = T.batched_dot(
sents_dot_2_matrix.reshape(
(batch_size, cand_size * sent_len, cand_size * sent_len)),
sents_tensor3) #(batch_size, cand_size*sent_len, emb_size)
add_sents_context = embed_input_sents + sents_context.reshape(
(batch_size * cand_size, sent_len, emb_size)
).dimshuffle(
0, 2, 1
) #T.concatenate([embed_input_sents, sents_context.reshape((batch_size*cand_size, sent_len, emb_size)).dimshuffle(0,2,1)], axis=1) #(batch_size*cand_size, 2*emb_size, sent_len)
test_attentive_conv_layer = Attentive_Conv_for_Pair_easy_version(
rng,
input_tensor3=
add_sents_context, #batch_size*cand_size, 2*emb_size, sent_len
input_tensor3_r=T.repeat(embed_input_claim, cand_size, axis=0),
mask_matrix=sents_mask.reshape(
(sents_mask.shape[0] * sents_mask.shape[1], sents_mask.shape[2])),
mask_matrix_r=T.repeat(claim_mask, cand_size, axis=0),
image_shape=(batch_size * cand_size, 1, emb_size, sent_len),
image_shape_r=(batch_size * cand_size, 1, emb_size, claim_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[0], 1, emb_size, 1),
W=att_conv_W,
b=att_conv_b,
W_context=conv_W_context,
b_context=conv_b_context)
# attentive_sent_embeddings_l = attentive_conv_layer.attentive_maxpool_vec_l #(batch_size*cand_size, hidden_size)
# attentive_sent_embeddings_r = attentive_conv_layer.attentive_maxpool_vec_r
test_attentive_sent_embeddings_l = test_attentive_conv_layer.attentive_maxpool_vec_l.reshape(
(batch_size, cand_size,
hidden_size[0])) #(batch_size*cand_size, hidden_size)
test_attentive_sent_embeddings_r = test_attentive_conv_layer.attentive_maxpool_vec_r.reshape(
(batch_size, cand_size, hidden_size[0]))
test_masked_sents_attconv = test_attentive_sent_embeddings_l * masked_inter_matrix.dimshuffle(
0, 1, 'x')
test_masked_claim_attconv = test_attentive_sent_embeddings_r * masked_inter_matrix.dimshuffle(
0, 1, 'x')
test_fine_max = T.concatenate([
T.max(test_masked_sents_attconv, axis=1),
T.max(test_masked_claim_attconv, axis=1)
],
axis=1) #(batch, 2*hidden)
# test_fine_sum = T.concatenate([T.sum(test_masked_sents_attconv, axis=1),T.sum(test_masked_claim_attconv, axis=1)],axis=1) #(batch, 2*hidden)
test_LR_input = T.concatenate([test_premise_hypo_emb, test_fine_max],
axis=1)
test_LR_input_size = joint_LR_input_size
test_layer_LR = LogisticRegression(
rng,
input=test_LR_input,
n_in=test_LR_input_size,
n_out=3,
W=joint_U_a,
b=joint_LR_b
) #basically it is a multiplication between weight matrix and input feature vector
params = [init_embeddings] + NN_para + LR_para + joint_LR_para
cost = loss + joint_loss
"Use AdaGrad to update parameters"
updates = Gradient_Cost_Para(cost, params, learning_rate)
train_model = theano.function([
sents_ids, sents_mask, sents_labels, claim_ids, claim_mask,
joint_sents_ids, joint_sents_mask, joint_sents_labels, joint_claim_ids,
joint_claim_mask, joint_labels
],
cost,
updates=updates,
allow_input_downcast=True,
on_unused_input='ignore')
# dev_model = theano.function([sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels], layer_LR.errors(labels), allow_input_downcast=True, on_unused_input='ignore')
test_model = theano.function([
sents_ids, sents_mask, sents_labels, claim_ids, claim_mask,
joint_labels
], [
inter_matrix,
test_layer_LR.errors(joint_labels), test_layer_LR.y_pred
],
allow_input_downcast=True,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
joint_n_train_batches = joint_train_size / batch_size
joint_train_batch_start = list(
np.arange(joint_n_train_batches) *
batch_size) + [joint_train_size - batch_size]
n_train_batches = train_size / batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
n_test_batches = test_size / batch_size
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
n_test_3th_batches = test_3th_size / batch_size
test_3th_batch_start = list(np.arange(n_test_3th_batches) *
batch_size) + [test_3th_size - batch_size]
max_acc = 0.0
max_test_f1 = 0.0
max_acc_full_evi = 0.0
cost_i = 0.0
joint_train_indices = range(joint_train_size)
train_indices = range(train_size)
while epoch < n_epochs:
epoch = epoch + 1
random.Random(100).shuffle(
joint_train_indices
) #shuffle training set for each new epoch, is supposed to promote performance, but not garrenteed
random.Random(100).shuffle(train_indices)
iter_accu = 0
for joint_batch_id in joint_train_batch_start: #for each batch
# iter means how many batches have been run, taking into loop
iter = (epoch - 1) * joint_n_train_batches + iter_accu + 1
iter_accu += 1
joint_train_id_batch = joint_train_indices[
joint_batch_id:joint_batch_id + batch_size]
for i in range(3):
batch_id = random.choice(train_batch_start)
train_id_batch = train_indices[batch_id:batch_id + batch_size]
cost_i += train_model(
train_sents[train_id_batch],
train_sent_masks[train_id_batch],
train_sent_labels[train_id_batch],
train_claims[train_id_batch],
train_claim_mask[train_id_batch],
#joint_sents_ids,joint_sents_mask,joint_sents_labels, joint_claim_ids, joint_claim_mask, joint_labels
joint_train_sents[joint_train_id_batch],
joint_train_sent_masks[joint_train_id_batch],
joint_train_sent_labels[joint_train_id_batch],
joint_train_claims[joint_train_id_batch],
joint_train_claim_mask[joint_train_id_batch],
joint_train_labels[joint_train_id_batch])
#after each 1000 batches, we test the performance of the model on all test data
# if (epoch==1 and iter%1000==0) or (epoch>=2 and iter%5==0):
if iter % 100 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
past_time = time.time()
f1_sum = 0.0
error_sum = 0.0
full_evi = 0
predictions = []
for test_batch_id in test_batch_start: # for each test batch
batch_prob, error_i, pred_i = test_model(
test_sents[test_batch_id:test_batch_id + batch_size],
test_sent_masks[test_batch_id:test_batch_id +
batch_size],
test_sent_labels[test_batch_id:test_batch_id +
batch_size],
test_claims[test_batch_id:test_batch_id + batch_size],
test_claim_mask[test_batch_id:test_batch_id +
batch_size],
test_labels[test_batch_id:test_batch_id + batch_size])
error_sum += error_i
batch_sent_labels = test_sent_labels[
test_batch_id:test_batch_id + batch_size]
batch_sent_names = test_sent_names[
test_batch_id:test_batch_id + batch_size]
batch_ground_names = test_ground_names[
test_batch_id:test_batch_id + batch_size]
batch_ground_labels = test_labels[
test_batch_id:test_batch_id + batch_size]
for i in range(batch_size):
instance_i = {}
instance_i['label'] = pred_id2label.get(
batch_ground_labels[i])
instance_i['predicted_label'] = pred_id2label.get(
pred_i[i])
pred_sent_names = []
gold_sent_names = batch_ground_names[i]
zipped = [(batch_prob[i, k], batch_sent_labels[i][k],
batch_sent_names[i][k])
for k in range(cand_size)]
sorted_zip = sorted(zipped,
key=lambda x: x[0],
reverse=True)
for j in range(cand_size):
triple = sorted_zip[j]
if triple[1] == 1.0:
'''
we should consider a rank, instead of binary
if triple[0] >0.5: can control the recall, influence the strict_acc
'''
if triple[0] > 0.5:
# pred_sent_names.append(batch_sent_names[i][j])
pred_sent_names.append(triple[2])
# if len(pred_sent_names) == max_pred_pick:
# break
instance_i['predicted_evidence'] = pred_sent_names
# print 'pred_sent_names:',pred_sent_names
# print 'gold_sent_names:',gold_sent_names
new_gold_names = []
for gold_name in gold_sent_names:
new_gold_names.append([None, None] + gold_name)
instance_i['evidence'] = [new_gold_names]
predictions.append(instance_i)
strict_score, label_accuracy, precision, recall, f1 = fever_score(
predictions)
print 'strict_score, label_accuracy, precision, recall, f1: ', strict_score, label_accuracy, precision, recall, f1
# test_f1=f1_sum/(len(test_batch_start)*batch_size)
for test_batch_id in test_3th_batch_start: # for each test batch
_, error_i, pred_i = test_model(
test_3th_sents[test_batch_id:test_batch_id +
batch_size],
test_3th_sent_masks[test_batch_id:test_batch_id +
batch_size],
test_3th_sent_labels[test_batch_id:test_batch_id +
batch_size],
test_3th_claims[test_batch_id:test_batch_id +
batch_size],
test_3th_claim_mask[test_batch_id:test_batch_id +
batch_size],
test_3th_labels[test_batch_id:test_batch_id +
batch_size])
for i in range(batch_size):
instance_i = {}
instance_i['label'] = pred_id2label.get(2)
instance_i['predicted_label'] = pred_id2label.get(
pred_i[i])
instance_i['predicted_evidence'] = []
instance_i['evidence'] = []
predictions.append(instance_i)
strict_score, label_accuracy, precision, recall, f1 = fever_score(
predictions)
print 'strict_score, label_accuracy, precision, recall, f1: ', strict_score, label_accuracy, precision, recall, f1
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
return max_acc_test
def evaluate_lenet5(learning_rate=0.01,
n_epochs=4,
emb_size=300,
batch_size=50,
describ_max_len=20,
type_size=12,
filter_size=[3, 5],
maxSentLen=200,
hidden_size=[300, 300]):
model_options = locals().copy()
print "model options", model_options
emb_root = '/save/wenpeng/datasets/LORELEI/multi-lingual-emb/2018-il9-il10/multi-emb/'
test_file_path = '/save/wenpeng/datasets/LORELEI/il9/il9-setE-as-test-input_ner_filtered_w2.txt'
output_file_path = '/save/wenpeng/datasets/LORELEI/il9/il9_system_output_concMT_BBN_NI_epoch4.json'
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
srng = T.shared_randomstreams.RandomStreams(rng.randint(seed))
word2id = {}
# all_sentences, all_masks, all_labels, all_other_labels, word2id=load_BBN_il5Trans_il5_dataset(maxlen=maxSentLen) #minlen, include one label, at least one word in the sentence
train_p1_sents, train_p1_masks, train_p1_labels, word2id = load_trainingData_types(
word2id, maxSentLen)
train_p2_sents, train_p2_masks, train_p2_labels, train_p2_other_labels, word2id = load_trainingData_types_plus_others(
word2id, maxSentLen)
test_sents, test_masks, test_lines, word2id = load_official_testData_il_and_MT(
word2id, maxSentLen, test_file_path)
label_sent, label_mask = load_SF_type_descriptions(word2id, type_size,
describ_max_len)
label_sent = np.asarray(label_sent, dtype='int32')
label_mask = np.asarray(label_mask, dtype=theano.config.floatX)
train_p1_sents = np.asarray(train_p1_sents, dtype='int32')
train_p1_masks = np.asarray(train_p1_masks, dtype=theano.config.floatX)
train_p1_labels = np.asarray(train_p1_labels, dtype='int32')
train_p1_size = len(train_p1_labels)
train_p2_sents = np.asarray(train_p2_sents, dtype='int32')
train_p2_masks = np.asarray(train_p2_masks, dtype=theano.config.floatX)
train_p2_labels = np.asarray(train_p2_labels, dtype='int32')
train_p2_other_labels = np.asarray(train_p2_other_labels, dtype='int32')
train_p2_size = len(train_p2_labels)
'''
combine train_p1 and train_p2
'''
train_sents = np.concatenate([train_p1_sents, train_p2_sents], axis=0)
train_masks = np.concatenate([train_p1_masks, train_p2_masks], axis=0)
train_labels = np.concatenate([train_p1_labels, train_p2_labels], axis=0)
train_size = train_p1_size + train_p2_size
test_sents = np.asarray(test_sents, dtype='int32')
test_masks = np.asarray(test_masks, dtype=theano.config.floatX)
# test_labels=np.asarray(all_labels[2], dtype='int32')
test_size = len(test_sents)
vocab_size = len(word2id) + 1 # add one zero pad index
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
rand_values[0] = np.array(np.zeros(emb_size), dtype=theano.config.floatX)
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_fasttext_multiple_word2vec_given_file([
emb_root + '100k-ENG-multicca.300.ENG.txt',
emb_root + '100k-IL9-multicca.d300.IL9.txt'
], 300)
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
#now, start to build the input form of the model
sents_id_matrix = T.imatrix('sents_id_matrix')
sents_mask = T.fmatrix('sents_mask')
labels = T.imatrix('labels') #batch*12
other_labels = T.imatrix() #batch*4
des_id_matrix = T.imatrix()
des_mask = T.fmatrix()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
common_input = embeddings[sents_id_matrix.flatten()].reshape(
(batch_size, maxSentLen, emb_size)).dimshuffle(
0, 2, 1) #the input format can be adapted into CNN or GRU or LSTM
bow_emb = T.sum(common_input * sents_mask.dimshuffle(0, 'x', 1), axis=2)
repeat_common_input = T.repeat(
normalize_tensor3_colwise(common_input), type_size,
axis=0) #(batch_size*type_size, emb_size, maxsentlen)
des_input = embeddings[des_id_matrix.flatten()].reshape(
(type_size, describ_max_len, emb_size)).dimshuffle(0, 2, 1)
bow_des = T.sum(des_input * des_mask.dimshuffle(0, 'x', 1),
axis=2) #(tyope_size, emb_size)
repeat_des_input = T.tile(
normalize_tensor3_colwise(des_input),
(batch_size, 1, 1)) #(batch_size*type_size, emb_size, maxsentlen)
conv_W, conv_b = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size, filter_size[0]))
conv_W2, conv_b2 = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size,
filter_size[1]))
multiCNN_para = [conv_W, conv_b, conv_W2, conv_b2]
conv_att_W, conv_att_b = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size,
filter_size[0]))
conv_W_context, conv_b_context = create_conv_para(
rng, filter_shape=(hidden_size[0], 1, emb_size, 1))
conv_att_W2, conv_att_b2 = create_conv_para(rng,
filter_shape=(hidden_size[0],
1, emb_size,
filter_size[1]))
conv_W_context2, conv_b_context2 = create_conv_para(
rng, filter_shape=(hidden_size[0], 1, emb_size, 1))
ACNN_para = [
conv_att_W, conv_att_b, conv_W_context, conv_att_W2, conv_att_b2,
conv_W_context2
]
'''
multi-CNN
'''
conv_model = Conv_with_Mask(
rng,
input_tensor3=common_input,
mask_matrix=sents_mask,
image_shape=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
W=conv_W,
b=conv_b
) #mutiple mask with the conv_out to set the features by UNK to zero
sent_embeddings = conv_model.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
conv_model2 = Conv_with_Mask(
rng,
input_tensor3=common_input,
mask_matrix=sents_mask,
image_shape=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[1]),
W=conv_W2,
b=conv_b2
) #mutiple mask with the conv_out to set the features by UNK to zero
sent_embeddings2 = conv_model2.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
'''
GRU
'''
U1, W1, b1 = create_GRU_para(rng, emb_size, hidden_size[0])
GRU_NN_para = [
U1, W1, b1
] #U1 includes 3 matrices, W1 also includes 3 matrices b1 is bias
# gru_input = common_input.dimshuffle((0,2,1)) #gru requires input (batch_size, emb_size, maxSentLen)
gru_layer = GRU_Batch_Tensor_Input_with_Mask(common_input, sents_mask,
hidden_size[0], U1, W1, b1)
gru_sent_embeddings = gru_layer.output_sent_rep # (batch_size, hidden_size)
'''
ACNN
'''
attentive_conv_layer = Attentive_Conv_for_Pair(
rng,
origin_input_tensor3=common_input,
origin_input_tensor3_r=common_input,
input_tensor3=common_input,
input_tensor3_r=common_input,
mask_matrix=sents_mask,
mask_matrix_r=sents_mask,
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[0], 1, emb_size, 1),
W=conv_att_W,
b=conv_att_b,
W_context=conv_W_context,
b_context=conv_b_context)
sent_att_embeddings = attentive_conv_layer.attentive_maxpool_vec_l
attentive_conv_layer2 = Attentive_Conv_for_Pair(
rng,
origin_input_tensor3=common_input,
origin_input_tensor3_r=common_input,
input_tensor3=common_input,
input_tensor3_r=common_input,
mask_matrix=sents_mask,
mask_matrix_r=sents_mask,
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[1]),
filter_shape_context=(hidden_size[0], 1, emb_size, 1),
W=conv_att_W2,
b=conv_att_b2,
W_context=conv_W_context2,
b_context=conv_b_context2)
sent_att_embeddings2 = attentive_conv_layer2.attentive_maxpool_vec_l
'''
cross-DNN-dataless
'''
#first map label emb into hidden space
HL_layer_1_W, HL_layer_1_b = create_HiddenLayer_para(
rng, emb_size, hidden_size[0])
HL_layer_1_params = [HL_layer_1_W, HL_layer_1_b]
HL_layer_1 = HiddenLayer(rng,
input=bow_des,
n_in=emb_size,
n_out=hidden_size[0],
W=HL_layer_1_W,
b=HL_layer_1_b,
activation=T.tanh)
des_rep_hidden = HL_layer_1.output #(type_size, hidden_size)
dot_dnn_dataless_1 = T.tanh(sent_embeddings.dot(
des_rep_hidden.T)) #(batch_size, type_size)
dot_dnn_dataless_2 = T.tanh(sent_embeddings2.dot(des_rep_hidden.T))
'''
dataless cosine
'''
cosine_scores = normalize_matrix_rowwise(bow_emb).dot(
normalize_matrix_rowwise(bow_des).T)
cosine_score_matrix = T.nnet.sigmoid(
cosine_scores) #(batch_size, type_size)
'''
dataless top-30 fine grained cosine
'''
fine_grained_cosine = T.batched_dot(
repeat_common_input.dimshuffle(0, 2, 1),
repeat_des_input) #(batch_size*type_size,maxsentlen,describ_max_len)
fine_grained_cosine_to_matrix = fine_grained_cosine.reshape(
(batch_size * type_size, maxSentLen * describ_max_len))
sort_fine_grained_cosine_to_matrix = T.sort(fine_grained_cosine_to_matrix,
axis=1)
top_k_simi = sort_fine_grained_cosine_to_matrix[:,
-30:] # (batch_size*type_size, 5)
max_fine_grained_cosine = T.mean(top_k_simi, axis=1)
top_k_cosine_scores = max_fine_grained_cosine.reshape(
(batch_size, type_size))
top_k_score_matrix = T.nnet.sigmoid(top_k_cosine_scores)
acnn_LR_input = T.concatenate([
dot_dnn_dataless_1, dot_dnn_dataless_2, cosine_score_matrix,
top_k_score_matrix, sent_embeddings, sent_embeddings2,
gru_sent_embeddings, sent_att_embeddings, sent_att_embeddings2, bow_emb
],
axis=1)
acnn_LR_input_size = hidden_size[0] * 5 + emb_size + 4 * type_size
#classification layer, it is just mapping from a feature vector of size "hidden_size" to a vector of only two values: positive, negative
acnn_U_a, acnn_LR_b = create_LR_para(rng, acnn_LR_input_size, 12)
acnn_LR_para = [acnn_U_a, acnn_LR_b]
acnn_layer_LR = LogisticRegression(
rng,
input=acnn_LR_input,
n_in=acnn_LR_input_size,
n_out=12,
W=acnn_U_a,
b=acnn_LR_b
) #basically it is a multiplication between weight matrix and input feature vector
acnn_score_matrix = T.nnet.sigmoid(
acnn_layer_LR.before_softmax) #batch * 12
acnn_prob_pos = T.where(labels < 1, 1.0 - acnn_score_matrix,
acnn_score_matrix)
acnn_loss = -T.mean(T.log(acnn_prob_pos))
acnn_other_U_a, acnn_other_LR_b = create_LR_para(rng, acnn_LR_input_size,
16)
acnn_other_LR_para = [acnn_other_U_a, acnn_other_LR_b]
acnn_other_layer_LR = LogisticRegression(rng,
input=acnn_LR_input,
n_in=acnn_LR_input_size,
n_out=16,
W=acnn_other_U_a,
b=acnn_other_LR_b)
acnn_other_prob_matrix = T.nnet.softmax(
acnn_other_layer_LR.before_softmax.reshape((batch_size * 4, 4)))
acnn_other_prob_tensor3 = acnn_other_prob_matrix.reshape(
(batch_size, 4, 4))
acnn_other_prob = acnn_other_prob_tensor3[
T.repeat(T.arange(batch_size), 4),
T.tile(T.arange(4), (batch_size)),
other_labels.flatten()]
acnn_other_field_loss = -T.mean(T.log(acnn_other_prob))
params = multiCNN_para + GRU_NN_para + ACNN_para + acnn_LR_para + HL_layer_1_params # put all model parameters together
cost = acnn_loss + 1e-4 * ((conv_W**2).sum() + (conv_W2**2).sum() +
(conv_att_W**2).sum() + (conv_att_W2**2).sum())
updates = Gradient_Cost_Para(cost, params, learning_rate)
other_paras = params + acnn_other_LR_para
cost_other = cost + acnn_other_field_loss
other_updates = Gradient_Cost_Para(cost_other, other_paras, learning_rate)
'''
testing
'''
ensemble_NN_scores = acnn_score_matrix #T.max(T.concatenate([att_score_matrix.dimshuffle('x',0,1), score_matrix.dimshuffle('x',0,1), acnn_score_matrix.dimshuffle('x',0,1)],axis=0),axis=0)
# '''
# majority voting, does not work
# '''
# binarize_NN = T.where(ensemble_NN_scores > 0.5, 1, 0)
# binarize_dataless = T.where(cosine_score_matrix > 0.5, 1, 0)
# binarize_dataless_finegrained = T.where(top_k_score_matrix > 0.5, 1, 0)
# binarize_conc = T.concatenate([binarize_NN.dimshuffle('x',0,1), binarize_dataless.dimshuffle('x',0,1),binarize_dataless_finegrained.dimshuffle('x',0,1)],axis=0)
# sum_binarize_conc = T.sum(binarize_conc,axis=0)
# binarize_prob = T.where(sum_binarize_conc > 0.0, 1, 0)
# '''
# sum up prob, works
# '''
# ensemble_scores_1 = 0.6*ensemble_NN_scores+0.4*top_k_score_matrix
# binarize_prob = T.where(ensemble_scores_1 > 0.3, 1, 0)
'''
sum up prob, works
'''
ensemble_scores = ensemble_NN_scores #0.6*ensemble_NN_scores+0.4*0.5*(cosine_score_matrix+top_k_score_matrix)
binarize_prob = T.where(ensemble_scores > 0.3, 1, 0)
'''
test for other fields
'''
sum_tensor3 = acnn_other_prob_tensor3 #(batch, 4, 3)
#train_model = theano.function([sents_id_matrix, sents_mask, labels], cost, updates=updates, on_unused_input='ignore')
train_p1_model = theano.function(
[sents_id_matrix, sents_mask, labels, des_id_matrix, des_mask],
cost,
updates=updates,
allow_input_downcast=True,
on_unused_input='ignore')
train_p2_model = theano.function([
sents_id_matrix, sents_mask, labels, des_id_matrix, des_mask,
other_labels
],
cost_other,
updates=other_updates,
allow_input_downcast=True,
on_unused_input='ignore')
test_model = theano.function(
[sents_id_matrix, sents_mask, des_id_matrix, des_mask],
[binarize_prob, ensemble_scores, sum_tensor3],
allow_input_downcast=True,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
n_train_batches = train_size / batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
n_train_p2_batches = train_p2_size / batch_size
train_p2_batch_start = list(np.arange(n_train_p2_batches) *
batch_size) + [train_p2_size - batch_size]
n_test_batches = test_size / batch_size
n_test_remain = test_size % batch_size
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
train_p2_batch_start_set = set(train_p2_batch_start)
# max_acc_dev=0.0
# max_meanf1_test=0.0
# max_weightf1_test=0.0
train_indices = range(train_size)
train_p2_indices = range(train_p2_size)
cost_i = 0.0
other_cost_i = 0.0
min_mean_frame = 100.0
while epoch < n_epochs:
epoch = epoch + 1
random.Random(100).shuffle(train_indices)
random.Random(100).shuffle(train_p2_indices)
iter_accu = 0
for batch_id in train_batch_start: #for each batch
# iter means how many batches have been run, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
train_id_batch = train_indices[batch_id:batch_id + batch_size]
cost_i += train_p1_model(train_sents[train_id_batch],
train_masks[train_id_batch],
train_labels[train_id_batch], label_sent,
label_mask)
if batch_id in train_p2_batch_start_set:
train_p2_id_batch = train_p2_indices[batch_id:batch_id +
batch_size]
other_cost_i += train_p2_model(
train_p2_sents[train_p2_id_batch],
train_p2_masks[train_p2_id_batch],
train_p2_labels[train_p2_id_batch], label_sent, label_mask,
train_p2_other_labels[train_p2_id_batch])
# else:
# random_batch_id = random.choice(train_p2_batch_start)
# train_p2_id_batch = train_p2_indices[random_batch_id:random_batch_id+batch_size]
# other_cost_i+=train_p2_model(
# train_p2_sents[train_p2_id_batch],
# train_p2_masks[train_p2_id_batch],
# train_p2_labels[train_p2_id_batch],
# label_sent,
# label_mask,
# train_p2_other_labels[train_p2_id_batch]
# )
#after each 1000 batches, we test the performance of the model on all test data
if iter % 20 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), str(
other_cost_i /
iter), 'uses ', (time.time() - past_time) / 60.0, 'min'
past_time = time.time()
pred_types = []
pred_confs = []
pred_others = []
for i, test_batch_id in enumerate(
test_batch_start): # for each test batch
pred_types_i, pred_conf_i, pred_fields_i = test_model(
test_sents[test_batch_id:test_batch_id + batch_size],
test_masks[test_batch_id:test_batch_id + batch_size],
label_sent, label_mask)
if i < len(test_batch_start) - 1:
pred_types.append(pred_types_i)
pred_confs.append(pred_conf_i)
pred_others.append(pred_fields_i)
else:
pred_types.append(pred_types_i[-n_test_remain:])
pred_confs.append(pred_conf_i[-n_test_remain:])
pred_others.append(pred_fields_i[-n_test_remain:])
pred_types = np.concatenate(pred_types, axis=0)
pred_confs = np.concatenate(pred_confs, axis=0)
pred_others = np.concatenate(pred_others, axis=0)
mean_frame = generate_2018_official_output(
test_lines, output_file_path, pred_types, pred_confs,
pred_others, min_mean_frame)
if mean_frame < min_mean_frame:
min_mean_frame = mean_frame
print '\t\t\t test over, min_mean_frame:', min_mean_frame
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(claim, title2sentlist, title2wordlist, word2id):
learning_rate = 0.02
n_epochs = 100
emb_size = 300
batch_size = 1 #50
filter_size = [3]
sent_len = 40
claim_len = 40
cand_size = 10
hidden_size = [300, 300]
max_pred_pick = 5
# model_options = locals().copy()
# print("model options", model_options)
# print('title2sentlist len', len(title2sentlist))
# print('title2wordlist len', len(title2wordlist))
pred_id2label = {1: 'SUPPORTS', 0: 'REFUTES', 2: 'NOT ENOUGH INFO'}
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
srng = T.shared_randomstreams.RandomStreams(rng.randint(seed))
claim_idlist, claim_masklist, sent_ins_ids, sent_ins_mask, sent_cand_list = claim_input_2_theano_input(
claim, word2id, claim_len, sent_len, cand_size, title2sentlist,
title2wordlist)
test_claims = np.asarray([claim_idlist], dtype='int32')
test_claim_mask = np.asarray([claim_masklist], dtype=theano.config.floatX)
test_sents = np.asarray([sent_ins_ids], dtype='int32')
test_sent_masks = np.asarray([sent_ins_mask], dtype=theano.config.floatX)
vocab_size = len(word2id) + 1
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
# id2word = {y:x for x,y in word2id.items()}
# word2vec=load_word2vec()
# rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
init_embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
'''
the first block for evidence identification in two classes (support & reject)
the second block for textual entailment: given evidence labels, predict the claim labels
'''
sents_ids = T.itensor3() #(batch, cand_size, sent_len)
sents_mask = T.ftensor3()
# sents_labels=T.imatrix() #(batch, cand_size)
claim_ids = T.imatrix() #(batch, claim_len)
claim_mask = T.fmatrix()
# joint_sents_ids=T.itensor3() #(batch, cand_size, sent_len)
# joint_sents_mask=T.ftensor3()
# # joint_sents_labels=T.imatrix() #(batch, cand_size)
# joint_claim_ids = T.imatrix() #(batch, claim_len)
# joint_claim_mask = T.fmatrix()
# joint_labels=T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print('... building the model')
embed_input_sents = init_embeddings[sents_ids.flatten(
)].reshape((batch_size * cand_size, sent_len, emb_size)).dimshuffle(
0, 2, 1
) #embed_input(init_embeddings, sents_ids_l)#embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
embed_input_claim = init_embeddings[claim_ids.flatten()].reshape(
(batch_size, claim_len, emb_size)).dimshuffle(0, 2, 1)
"shared parameters"
conv_W, conv_b = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size, filter_size[0]))
"tasl 1 parameters"
task1_att_conv_W, task1_att_conv_b = create_conv_para(
rng, filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]))
task1_conv_W_context, task1_conv_b_context = create_conv_para(
rng, filter_shape=(hidden_size[0], 1, emb_size, 1))
"task 2 parameters"
att_conv_W, att_conv_b = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size,
filter_size[0]))
conv_W_context, conv_b_context = create_conv_para(
rng, filter_shape=(hidden_size[0], 1, emb_size, 1))
NN_para = [
conv_W, conv_b, task1_att_conv_W, task1_att_conv_b, att_conv_W,
att_conv_b, task1_conv_W_context, conv_W_context
]
conv_model_sents = Conv_with_Mask(
rng,
input_tensor3=embed_input_sents,
mask_matrix=sents_mask.reshape(
(sents_mask.shape[0] * sents_mask.shape[1], sents_mask.shape[2])),
image_shape=(batch_size * cand_size, 1, emb_size, sent_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
W=conv_W,
b=conv_b
) #mutiple mask with the conv_out to set the features by UNK to zero
sent_embeddings = conv_model_sents.maxpool_vec #(batch_size*cand_size, hidden_size) # each sentence then have an embedding of length hidden_size
batch_sent_emb = sent_embeddings.reshape(
(batch_size, cand_size, hidden_size[0]))
conv_model_claims = Conv_with_Mask(
rng,
input_tensor3=embed_input_claim,
mask_matrix=claim_mask,
image_shape=(batch_size, 1, emb_size, claim_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
W=conv_W,
b=conv_b
) #mutiple mask with the conv_out to set the features by UNK to zero
claim_embeddings = conv_model_claims.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
batch_claim_emb = T.repeat(claim_embeddings.dimshuffle(0, 'x', 1),
cand_size,
axis=1)
'''
attentive conv for task1
'''
task1_attentive_conv_layer = Attentive_Conv_for_Pair_easy_version(
rng,
input_tensor3=
embed_input_sents, #batch_size*cand_size, emb_size, sent_len
input_tensor3_r=T.repeat(embed_input_claim, cand_size, axis=0),
mask_matrix=sents_mask.reshape(
(sents_mask.shape[0] * sents_mask.shape[1], sents_mask.shape[2])),
mask_matrix_r=T.repeat(claim_mask, cand_size, axis=0),
image_shape=(batch_size * cand_size, 1, emb_size, sent_len),
image_shape_r=(batch_size * cand_size, 1, emb_size, claim_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[0], 1, emb_size, 1),
W=task1_att_conv_W,
b=task1_att_conv_b,
W_context=task1_conv_W_context,
b_context=task1_conv_b_context)
task1_attentive_sent_embeddings_l = task1_attentive_conv_layer.attentive_maxpool_vec_l #(batch_size*cand_size, hidden_size)
task1_attentive_sent_embeddings_r = task1_attentive_conv_layer.attentive_maxpool_vec_r
concate_claim_sent = T.concatenate([
batch_claim_emb, batch_sent_emb,
T.sum(batch_claim_emb * batch_sent_emb, axis=2).dimshuffle(0, 1, 'x')
],
axis=2)
concate_2_matrix = concate_claim_sent.reshape(
(batch_size * cand_size, hidden_size[0] * 2 + 1))
"to score each evidence sentence, we use the output of attentiveConv, as well as the output of standard CNN"
LR_input = T.concatenate([
concate_2_matrix, task1_attentive_sent_embeddings_l,
task1_attentive_sent_embeddings_r
],
axis=1)
LR_input_size = hidden_size[0] * 2 + 1 + hidden_size[0] * 2
# LR_input = concate_2_matrix
# LR_input_size = hidden_size[0]*2+1
#classification layer, it is just mapping from a feature vector of size "hidden_size" to a vector of only two values: positive, negative
U_a = create_ensemble_para(
rng, 1, LR_input_size) # the weight matrix hidden_size*2
# LR_b = theano.shared(value=np.zeros((8,),dtype=theano.config.floatX),name='LR_b', borrow=True) #bias for each target class
LR_para = [U_a]
# layer_LR=LogisticRegression(rng, input=LR_input, n_in=LR_input_size, n_out=8, W=U_a, b=LR_b) #basically it is a multiplication between weight matrix and input feature vector
score_matrix = T.nnet.sigmoid(LR_input.dot(U_a)) #batch * 12
inter_matrix = score_matrix.reshape((batch_size, cand_size))
# inter_sent_claim = T.batched_dot(batch_sent_emb, batch_claim_emb) #(batch_size, cand_size, 1)
# inter_matrix = T.nnet.sigmoid(inter_sent_claim.reshape((batch_size, cand_size)))
'''
maybe 1.0-inter_matrix can be rewritten into 1/e^(inter_matrix)
'''
binarize_prob = T.where(inter_matrix > 0.5, 1, 0) #(batch_size, cand_size)
sents_labels = inter_matrix * binarize_prob
'''
training task2, predict 3 labels
'''
# joint_embed_input_sents=init_embeddings[joint_sents_ids.flatten()].reshape((batch_size*cand_size, sent_len, emb_size)).dimshuffle(0,2,1)#embed_input(init_embeddings, sents_ids_l)#embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
# joint_embed_input_claim=init_embeddings[joint_claim_ids.flatten()].reshape((batch_size,claim_len, emb_size)).dimshuffle(0,2,1)
# joint_conv_model_sents = Conv_with_Mask(rng, input_tensor3=joint_embed_input_sents,
# mask_matrix = joint_sents_mask.reshape((joint_sents_mask.shape[0]*joint_sents_mask.shape[1],joint_sents_mask.shape[2])),
# image_shape=(batch_size*cand_size, 1, emb_size, sent_len),
# filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]), W=conv_W, b=conv_b) #mutiple mask with the conv_out to set the features by UNK to zero
# joint_sent_embeddings=joint_conv_model_sents.maxpool_vec #(batch_size*cand_size, hidden_size) # each sentence then have an embedding of length hidden_size
# joint_batch_sent_emb = joint_sent_embeddings.reshape((batch_size, cand_size, hidden_size[0]))
# "??? use joint_sents_labels means the evidence labels are not provided by task 1?"
# joint_premise_emb = T.sum(joint_batch_sent_emb*joint_sents_labels.dimshuffle(0,1,'x'), axis=1) #(batch, hidden_size)
premise_emb = T.sum(batch_sent_emb * sents_labels.dimshuffle(0, 1, 'x'),
axis=1)
# joint_conv_model_claims = Conv_with_Mask(rng, input_tensor3=joint_embed_input_claim,
# mask_matrix = joint_claim_mask,
# image_shape=(batch_size, 1, emb_size, claim_len),
# filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]), W=conv_W, b=conv_b) #mutiple mask with the conv_out to set the features by UNK to zero
# joint_claim_embeddings=joint_conv_model_claims.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
premise_hypo_emb = T.concatenate([premise_emb, claim_embeddings],
axis=1) #(batch, 2*hidden_size)
'''
attentive conv in task2
'''
sents_tensor3 = embed_input_sents.dimshuffle(0, 2, 1).reshape(
(batch_size, cand_size * sent_len, emb_size))
sents_dot = T.batched_dot(sents_tensor3, sents_tensor3.dimshuffle(
0, 2, 1)) #(batch_size, cand_size*sent_len, cand_size*sent_len)
sents_dot_2_matrix = T.nnet.softmax(
sents_dot.reshape(
(batch_size * cand_size * sent_len, cand_size * sent_len)))
sents_context = T.batched_dot(
sents_dot_2_matrix.reshape(
(batch_size, cand_size * sent_len, cand_size * sent_len)),
sents_tensor3) #(batch_size, cand_size*sent_len, emb_size)
add_sents_context = embed_input_sents + sents_context.reshape(
(batch_size * cand_size, sent_len, emb_size)
).dimshuffle(
0, 2, 1
) #T.concatenate([joint_embed_input_sents, joint_sents_context.reshape((batch_size*cand_size, sent_len, emb_size)).dimshuffle(0,2,1)], axis=1) #(batch_size*cand_size, 2*emb_size, sent_len)
attentive_conv_layer = Attentive_Conv_for_Pair_easy_version(
rng,
input_tensor3=
add_sents_context, #batch_size*cand_size, 2*emb_size, sent_len
input_tensor3_r=T.repeat(embed_input_claim, cand_size, axis=0),
mask_matrix=sents_mask.reshape(
(sents_mask.shape[0] * sents_mask.shape[1], sents_mask.shape[2])),
mask_matrix_r=T.repeat(claim_mask, cand_size, axis=0),
image_shape=(batch_size * cand_size, 1, emb_size, sent_len),
image_shape_r=(batch_size * cand_size, 1, emb_size, claim_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[0], 1, emb_size, 1),
W=att_conv_W,
b=att_conv_b,
W_context=conv_W_context,
b_context=conv_b_context)
attentive_sent_embeddings_l = attentive_conv_layer.attentive_maxpool_vec_l.reshape(
(batch_size, cand_size,
hidden_size[0])) #(batch_size*cand_size, hidden_size)
attentive_sent_embeddings_r = attentive_conv_layer.attentive_maxpool_vec_r.reshape(
(batch_size, cand_size, hidden_size[0]))
masked_sents_attconv = attentive_sent_embeddings_l * sents_labels.dimshuffle(
0, 1, 'x')
masked_claim_attconv = attentive_sent_embeddings_r * sents_labels.dimshuffle(
0, 1, 'x')
fine_max = T.concatenate([
T.max(masked_sents_attconv, axis=1),
T.max(masked_claim_attconv, axis=1)
],
axis=1) #(batch, 2*hidden)
# fine_sum = T.concatenate([T.sum(masked_sents_attconv, axis=1),T.sum(masked_claim_attconv, axis=1)],axis=1) #(batch, 2*hidden)
"Logistic Regression layer"
joint_LR_input = T.concatenate([premise_hypo_emb, fine_max], axis=1)
joint_LR_input_size = 2 * hidden_size[0] + 2 * hidden_size[0]
joint_U_a = create_ensemble_para(rng, 3,
joint_LR_input_size) # (input_size, 3)
joint_LR_b = theano.shared(value=np.zeros((3, ),
dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
joint_LR_para = [joint_U_a, joint_LR_b]
joint_layer_LR = LogisticRegression(
rng,
input=joint_LR_input,
n_in=joint_LR_input_size,
n_out=3,
W=joint_U_a,
b=joint_LR_b
) #basically it is a multiplication between weight matrix and input feature vector
# joint_loss=joint_layer_LR.negative_log_likelihood(joint_labels) #for classification task, we usually used negative log likelihood as loss, the lower the better.
params = [init_embeddings] + NN_para + LR_para + joint_LR_para
print('initialze model parameters...')
load_model_from_file(
'/home1/w/wenpeng/dataset/FEVER/model_para_0.9936287838053803', params)
# train_model = theano.function([sents_ids,sents_mask,sents_labels,claim_ids,claim_mask,joint_sents_ids,joint_sents_mask,joint_sents_labels, joint_claim_ids, joint_claim_mask, joint_labels], cost, updates=updates, allow_input_downcast=True, on_unused_input='ignore')
test_model = theano.function(
[sents_ids, sents_mask, claim_ids, claim_mask],
[inter_matrix, binarize_prob, joint_layer_LR.y_pred],
allow_input_downcast=True,
on_unused_input='ignore')
# dev_model = theano.function([sents_ids,sents_mask, claim_ids,claim_mask], [binarize_prob,joint_layer_LR.y_pred], allow_input_downcast=True, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print('... testing')
# early-stopping parameters
batch_score_vec, batch_binary_vec, pred_i = test_model(
test_sents, test_sent_masks, test_claims, test_claim_mask)
sorted_indices = np.argsort(batch_score_vec[0])[::-1] #descending order
selected_sents = []
for index in sorted_indices:
if batch_binary_vec[0][index] == 1:
selected_sents.append(sent_cand_list[index])
if len(selected_sents) == 5:
break
# for i, indicator in enumerate(list(batch_binary_vec[0])):
# if indicator == 1:
# selected_sents.append(sent_cand_list[i])
return pred_id2label.get(
pred_i[0]) + '"<p>"' + '"<br />"'.join(selected_sents) + '"<p/>"'
def evaluate_lenet5(learning_rate=0.01,
n_epochs=100,
emb_size=40,
batch_size=50,
describ_max_len=20,
type_size=12,
filter_size=[3, 5],
maxSentLen=100,
hidden_size=[300, 300]):
model_options = locals().copy()
print "model options", model_options
emb_root = '/save/wenpeng/datasets/LORELEI/multi-lingual-emb/'
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
srng = T.shared_randomstreams.RandomStreams(rng.randint(seed))
all_sentences, all_masks, all_labels, word2id = load_BBN_multi_labels_dataset(
maxlen=maxSentLen
) #minlen, include one label, at least one word in the sentence
label_sent, label_mask = load_SF_type_descriptions(word2id, type_size,
describ_max_len)
label_sent = np.asarray(label_sent, dtype='int32')
label_mask = np.asarray(label_mask, dtype=theano.config.floatX)
train_sents = np.asarray(all_sentences[0], dtype='int32')
train_masks = np.asarray(all_masks[0], dtype=theano.config.floatX)
train_labels = np.asarray(all_labels[0], dtype='int32')
train_size = len(train_labels)
dev_sents = np.asarray(all_sentences[1], dtype='int32')
dev_masks = np.asarray(all_masks[1], dtype=theano.config.floatX)
dev_labels = np.asarray(all_labels[1], dtype='int32')
dev_size = len(dev_labels)
test_sents = np.asarray(all_sentences[2], dtype='int32')
test_masks = np.asarray(all_masks[2], dtype=theano.config.floatX)
test_labels = np.asarray(all_labels[2], dtype='int32')
test_size = len(test_labels)
vocab_size = len(word2id) + 1 # add one zero pad index
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
rand_values[0] = np.array(np.zeros(emb_size), dtype=theano.config.floatX)
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_fasttext_multiple_word2vec_given_file([
emb_root + 'IL5-cca-wiki-lorelei-d40.eng.vec',
emb_root + 'IL5-cca-wiki-lorelei-d40.IL5.vec'
], 40)
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
#now, start to build the input form of the model
sents_id_matrix = T.imatrix('sents_id_matrix')
sents_mask = T.fmatrix('sents_mask')
labels = T.imatrix('labels') #batch*12
des_id_matrix = T.imatrix()
des_mask = T.fmatrix()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
common_input = embeddings[sents_id_matrix.flatten()].reshape(
(batch_size, maxSentLen, emb_size)).dimshuffle(
0, 2, 1) #the input format can be adapted into CNN or GRU or LSTM
bow_emb = T.sum(common_input * sents_mask.dimshuffle(0, 'x', 1), axis=2)
# bow_mean_emb = bow_emb/T.sum(sents_mask,axis=1).dimshuffle(0,'x')
des_input = embeddings[des_id_matrix.flatten()].reshape(
(type_size, describ_max_len, emb_size)).dimshuffle(0, 2, 1)
bow_des = T.sum(des_input * des_mask.dimshuffle(0, 'x', 1),
axis=2) #(tyope_size, emb_size)
conv_W, conv_b = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size, filter_size[0]))
conv_W2, conv_b2 = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size,
filter_size[1]))
multiCNN_para = [conv_W, conv_b, conv_W2, conv_b2]
conv_att_W, conv_att_b = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size,
filter_size[0]))
conv_W_context, conv_b_context = create_conv_para(
rng, filter_shape=(hidden_size[0], 1, emb_size, 1))
conv_att_W2, conv_att_b2 = create_conv_para(rng,
filter_shape=(hidden_size[0],
1, emb_size,
filter_size[1]))
conv_W_context2, conv_b_context2 = create_conv_para(
rng, filter_shape=(hidden_size[0], 1, emb_size, 1))
ACNN_para = [
conv_att_W, conv_att_b, conv_W_context, conv_att_W2, conv_att_b2,
conv_W_context2
]
# NN_para = multiCNN_para+ACNN_para
conv_model = Conv_with_Mask(
rng,
input_tensor3=common_input,
mask_matrix=sents_mask,
image_shape=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
W=conv_W,
b=conv_b
) #mutiple mask with the conv_out to set the features by UNK to zero
sent_embeddings = conv_model.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
conv_model2 = Conv_with_Mask(
rng,
input_tensor3=common_input,
mask_matrix=sents_mask,
image_shape=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[1]),
W=conv_W2,
b=conv_b2
) #mutiple mask with the conv_out to set the features by UNK to zero
sent_embeddings2 = conv_model2.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
LR_input = T.concatenate([sent_embeddings, sent_embeddings2, bow_emb],
axis=1)
LR_input_size = hidden_size[0] * 2 + emb_size
#classification layer, it is just mapping from a feature vector of size "hidden_size" to a vector of only two values: positive, negative
U_a = create_ensemble_para(
rng, 12, LR_input_size) # the weight matrix hidden_size*2
LR_b = theano.shared(value=np.zeros((12, ), dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
LR_para = [U_a, LR_b]
layer_LR = LogisticRegression(
rng, input=LR_input, n_in=LR_input_size, n_out=12, W=U_a, b=LR_b
) #basically it is a multiplication between weight matrix and input feature vector
score_matrix = T.nnet.sigmoid(layer_LR.before_softmax) #batch * 12
prob_pos = T.where(labels < 1, 1.0 - score_matrix, score_matrix)
loss = -T.mean(T.log(prob_pos))
'''
GRU
'''
U1, W1, b1 = create_GRU_para(rng, emb_size, hidden_size[0])
GRU_NN_para = [
U1, W1, b1
] #U1 includes 3 matrices, W1 also includes 3 matrices b1 is bias
# gru_input = common_input.dimshuffle((0,2,1)) #gru requires input (batch_size, emb_size, maxSentLen)
gru_layer = GRU_Batch_Tensor_Input_with_Mask(common_input, sents_mask,
hidden_size[0], U1, W1, b1)
gru_sent_embeddings = gru_layer.output_sent_rep # (batch_size, hidden_size)
LR_att_input = T.concatenate([gru_sent_embeddings, bow_emb], axis=1)
LR_att_input_size = hidden_size[0] + emb_size
#classification layer, it is just mapping from a feature vector of size "hidden_size" to a vector of only two values: positive, negative
U_att_a = create_ensemble_para(
rng, 12, LR_att_input_size) # the weight matrix hidden_size*2
LR_att_b = theano.shared(value=np.zeros((12, ),
dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
LR_att_para = [U_att_a, LR_att_b]
layer_att_LR = LogisticRegression(
rng,
input=LR_att_input,
n_in=LR_att_input_size,
n_out=12,
W=U_att_a,
b=LR_att_b
) #basically it is a multiplication between weight matrix and input feature vector
att_score_matrix = T.nnet.sigmoid(layer_att_LR.before_softmax) #batch * 12
att_prob_pos = T.where(labels < 1, 1.0 - att_score_matrix,
att_score_matrix)
att_loss = -T.mean(T.log(att_prob_pos))
'''
ACNN
'''
attentive_conv_layer = Attentive_Conv_for_Pair(
rng,
origin_input_tensor3=common_input,
origin_input_tensor3_r=common_input,
input_tensor3=common_input,
input_tensor3_r=common_input,
mask_matrix=sents_mask,
mask_matrix_r=sents_mask,
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[0], 1, emb_size, 1),
W=conv_att_W,
b=conv_att_b,
W_context=conv_W_context,
b_context=conv_b_context)
sent_att_embeddings = attentive_conv_layer.attentive_maxpool_vec_l
attentive_conv_layer2 = Attentive_Conv_for_Pair(
rng,
origin_input_tensor3=common_input,
origin_input_tensor3_r=common_input,
input_tensor3=common_input,
input_tensor3_r=common_input,
mask_matrix=sents_mask,
mask_matrix_r=sents_mask,
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[1]),
filter_shape_context=(hidden_size[0], 1, emb_size, 1),
W=conv_att_W2,
b=conv_att_b2,
W_context=conv_W_context2,
b_context=conv_b_context2)
sent_att_embeddings2 = attentive_conv_layer2.attentive_maxpool_vec_l
acnn_LR_input = T.concatenate(
[sent_att_embeddings, sent_att_embeddings2, bow_emb], axis=1)
acnn_LR_input_size = hidden_size[0] * 2 + emb_size
#classification layer, it is just mapping from a feature vector of size "hidden_size" to a vector of only two values: positive, negative
acnn_U_a = create_ensemble_para(
rng, 12, acnn_LR_input_size) # the weight matrix hidden_size*2
acnn_LR_b = theano.shared(value=np.zeros((12, ),
dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
acnn_LR_para = [acnn_U_a, acnn_LR_b]
acnn_layer_LR = LogisticRegression(
rng,
input=acnn_LR_input,
n_in=acnn_LR_input_size,
n_out=12,
W=acnn_U_a,
b=acnn_LR_b
) #basically it is a multiplication between weight matrix and input feature vector
acnn_score_matrix = T.nnet.sigmoid(
acnn_layer_LR.before_softmax) #batch * 12
acnn_prob_pos = T.where(labels < 1, 1.0 - acnn_score_matrix,
acnn_score_matrix)
acnn_loss = -T.mean(T.log(acnn_prob_pos))
'''
dataless cosine
'''
cosine_scores = normalize_matrix_rowwise(bow_emb).dot(
normalize_matrix_rowwise(bow_des).T)
cosine_score_matrix = T.nnet.sigmoid(
cosine_scores) #(batch_size, type_size)
params = multiCNN_para + LR_para + GRU_NN_para + LR_att_para + ACNN_para + acnn_LR_para # put all model parameters together
cost = loss + att_loss + acnn_loss + 1e-4 * ((conv_W**2).sum() +
(conv_W2**2).sum())
updates = Gradient_Cost_Para(cost, params, learning_rate)
'''
testing
'''
ensemble_NN_scores = T.max(T.concatenate([
att_score_matrix.dimshuffle('x', 0, 1),
score_matrix.dimshuffle('x', 0, 1),
acnn_score_matrix.dimshuffle('x', 0, 1)
],
axis=0),
axis=0)
ensemble_scores = 0.5 * ensemble_NN_scores + 0.5 * cosine_score_matrix
binarize_prob = T.where(ensemble_scores > 0.3, 1, 0)
#train_model = theano.function([sents_id_matrix, sents_mask, labels], cost, updates=updates, on_unused_input='ignore')
train_model = theano.function(
[sents_id_matrix, sents_mask, labels, des_id_matrix, des_mask],
cost,
updates=updates,
allow_input_downcast=True,
on_unused_input='ignore')
# dev_model = theano.function([sents_id_matrix, sents_mask, labels], layer_LR.errors(labels), allow_input_downcast=True, on_unused_input='ignore')
test_model = theano.function(
[sents_id_matrix, sents_mask, des_id_matrix, des_mask],
binarize_prob,
allow_input_downcast=True,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
n_train_batches = train_size / batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
# n_dev_batches=dev_size/batch_size
# dev_batch_start=list(np.arange(n_dev_batches)*batch_size)+[dev_size-batch_size]
n_test_batches = test_size / batch_size
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
# max_acc_dev=0.0
max_meanf1_test = 0.0
max_weightf1_test = 0.0
train_indices = range(train_size)
cost_i = 0.0
while epoch < n_epochs:
epoch = epoch + 1
random.Random(100).shuffle(train_indices)
iter_accu = 0
for batch_id in train_batch_start: #for each batch
# iter means how many batches have been run, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
train_id_batch = train_indices[batch_id:batch_id + batch_size]
cost_i += train_model(train_sents[train_id_batch],
train_masks[train_id_batch],
train_labels[train_id_batch], label_sent,
label_mask)
#after each 1000 batches, we test the performance of the model on all test data
if iter % 20 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
past_time = time.time()
error_sum = 0.0
all_pred_labels = []
all_gold_labels = []
for test_batch_id in test_batch_start: # for each test batch
pred_labels = test_model(
test_sents[test_batch_id:test_batch_id + batch_size],
test_masks[test_batch_id:test_batch_id + batch_size],
label_sent, label_mask)
gold_labels = test_labels[test_batch_id:test_batch_id +
batch_size]
# print 'pred_labels:', pred_labels
# print 'gold_labels;', gold_labels
all_pred_labels.append(pred_labels)
all_gold_labels.append(gold_labels)
all_pred_labels = np.concatenate(all_pred_labels)
all_gold_labels = np.concatenate(all_gold_labels)
test_mean_f1, test_weight_f1 = average_f1_two_array_by_col(
all_pred_labels, all_gold_labels)
if test_weight_f1 > max_weightf1_test:
max_weightf1_test = test_weight_f1
if test_mean_f1 > max_meanf1_test:
max_meanf1_test = test_mean_f1
print '\t\t\t\t\t\t\t\tcurrent f1s:', test_mean_f1, test_weight_f1, '\t\tmax_f1:', max_meanf1_test, max_weightf1_test
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.02,
n_epochs=100,
emb_size=300,
batch_size=50,
filter_size=[3],
sent_len=40,
claim_len=40,
cand_size=10,
hidden_size=[300, 300],
max_pred_pick=5):
model_options = locals().copy()
print "model options", model_options
pred_id2label = {1: 'SUPPORTS', 0: 'REFUTES', 2: 'NOT ENOUGH INFO'}
root = '/save/wenpeng/datasets/FEVER/'
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
srng = T.shared_randomstreams.RandomStreams(rng.randint(seed))
"load raw data"
vocabfile = codecs.open(root + 'word2id.txt', 'r', 'utf-8')
word2id = json.loads(vocabfile.read())
# co=0
# for line in vocabfile:
# word2id = json.loads(line)
# co+=1
# print 'co: ', co
# word2id = json.load(open(root+'word2id.json')) #json.loads(vocabfile)
vocabfile.close()
print 'load word2id over'
# train_sents, train_sent_masks, train_sent_labels, train_claims, train_claim_mask, train_labels, word2id = load_fever_train(sent_len, claim_len, cand_size)
# train_3th_sents, train_3th_sent_masks, train_3th_sent_labels, train_3th_claims, train_3th_claim_mask, train_3th_labels, word2id = load_fever_train_NoEnoughInfo(sent_len, claim_len, cand_size, word2id)
all_sentences_l, all_masks_l, all_sentences_r, all_masks_r, all_labels, _ = load_SciTailV1_dataset(
sent_len, word2id)
# all_sentences_l, all_masks_l, all_sentences_r, all_masks_r, all_labels, _ = load_RTE_dataset_as_test(sent_len, word2id)
# dev_sents_l=np.asarray(all_sentences_l[1], dtype='int32')
test_sents_l = np.asarray(all_sentences_l[2], dtype='int32')
# dev_masks_l=np.asarray(all_masks_l[1], dtype=theano.config.floatX)
test_masks_l = np.asarray(all_masks_l[2], dtype=theano.config.floatX)
# dev_sents_r=np.asarray(all_sentences_r[1] , dtype='int32')
test_sents_r = np.asarray(all_sentences_r[2], dtype='int32')
# dev_masks_r=np.asarray(all_masks_r[1], dtype=theano.config.floatX)
test_masks_r = np.asarray(all_masks_r[2], dtype=theano.config.floatX)
# dev_labels_store=np.asarray(all_labels[1], dtype='int32')
test_labels_store = np.asarray(all_labels[2], dtype='int32')
# dev_size=len(dev_labels_store)
test_size = len(test_labels_store)
vocab_size = len(word2id) + 1
print 'vocab size: ', vocab_size
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
# id2word = {y:x for x,y in word2id.iteritems()}
# word2vec=load_word2vec()
# rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
init_embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
"now, start to build the input form of the model"
sents_ids = T.imatrix() #(batch, cand_size, sent_len)
sents_mask = T.fmatrix()
# sents_labels=T.imatrix() #(batch, cand_size)
# claim_ids = T.imatrix() #(batch, claim_len)
# claim_mask = T.fmatrix()
# joint_sents_ids=T.itensor3() #(batch, cand_size, sent_len)
# joint_sents_mask=T.ftensor3()
# joint_sents_labels=T.imatrix() #(batch, cand_size)
claim_ids = T.imatrix() #(batch, claim_len)
claim_mask = T.fmatrix()
labels = T.ivector()
# test_premise_ids = T.imatrix()
# test_premise_matrix = T.fmatrix()
# test_hypo_ids = T.imatrix()
# test_hypo_matrix = T.fmatrix()
# test_scitail_minibatch_labels = T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
embed_input_sents = init_embeddings[sents_ids.flatten(
)].reshape((batch_size, sent_len, emb_size)).dimshuffle(
0, 2, 1
) #embed_input(init_embeddings, sents_ids_l)#embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
embed_input_claim = init_embeddings[claim_ids.flatten()].reshape(
(batch_size, sent_len, emb_size)).dimshuffle(0, 2, 1)
conv_W, conv_b = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size, filter_size[0]))
# task1_att_conv_W, task1_att_conv_b=create_conv_para(rng, filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]))
# task1_conv_W_context, task1_conv_b_context=create_conv_para(rng, filter_shape=(hidden_size[0], 1, emb_size, 1))
att_conv_W, att_conv_b = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size,
filter_size[0]))
conv_W_context, conv_b_context = create_conv_para(
rng, filter_shape=(hidden_size[0], 1, emb_size, 1))
NN_para = [
conv_W, conv_b, att_conv_W, att_conv_b, conv_W_context, conv_b_context
]
'''
training task2, predict 3 labels
'''
joint_embed_input_sents = init_embeddings[sents_ids.flatten(
)].reshape((batch_size, sent_len, emb_size)).dimshuffle(
0, 2, 1
) #embed_input(init_embeddings, sents_ids_l)#embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
joint_embed_input_claim = init_embeddings[claim_ids.flatten()].reshape(
(batch_size, sent_len, emb_size)).dimshuffle(0, 2, 1)
joint_conv_model_sents = Conv_with_Mask(
rng,
input_tensor3=joint_embed_input_sents,
mask_matrix=sents_mask,
image_shape=(batch_size, 1, emb_size, sent_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
W=conv_W,
b=conv_b
) #mutiple mask with the conv_out to set the features by UNK to zero
joint_premise_emb = joint_conv_model_sents.maxpool_vec #(batch_size*cand_size, hidden_size) # each sentence then have an embedding of length hidden_size
# joint_batch_sent_emb = joint_sent_embeddings.reshape((batch_size, cand_size, hidden_size[0]))
# joint_premise_emb = T.sum(joint_batch_sent_emb*joint_sents_labels.dimshuffle(0,1,'x'), axis=1) #(batch, hidden_size)
joint_conv_model_claims = Conv_with_Mask(
rng,
input_tensor3=joint_embed_input_claim,
mask_matrix=claim_mask,
image_shape=(batch_size, 1, emb_size, claim_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
W=conv_W,
b=conv_b
) #mutiple mask with the conv_out to set the features by UNK to zero
joint_claim_embeddings = joint_conv_model_claims.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
joint_premise_hypo_emb = T.concatenate(
[joint_premise_emb, joint_claim_embeddings],
axis=1) #(batch, 2*hidden_size)
'''
attentive conv in task2
'''
# joint_sents_tensor3 = joint_embed_input_sents.dimshuffle(0,2,1).reshape((batch_size, cand_size*sent_len, emb_size))
# joint_sents_dot = T.batched_dot(joint_sents_tensor3, joint_sents_tensor3.dimshuffle(0,2,1)) #(batch_size, cand_size*sent_len, cand_size*sent_len)
# joint_sents_dot_2_matrix = T.nnet.softmax(joint_sents_dot.reshape((batch_size*cand_size*sent_len, cand_size*sent_len)))
# joint_sents_context = T.batched_dot(joint_sents_dot_2_matrix.reshape((batch_size, cand_size*sent_len, cand_size*sent_len)), joint_sents_tensor3) #(batch_size, cand_size*sent_len, emb_size)
# joint_add_sents_context = joint_embed_input_sents+joint_sents_context.reshape((batch_size*cand_size, sent_len, emb_size)).dimshuffle(0,2,1)#T.concatenate([joint_embed_input_sents, joint_sents_context.reshape((batch_size*cand_size, sent_len, emb_size)).dimshuffle(0,2,1)], axis=1) #(batch_size*cand_size, 2*emb_size, sent_len)
attentive_conv_layer = Attentive_Conv_for_Pair_easy_version(
rng,
input_tensor3=
joint_embed_input_sents, #batch_size*cand_size, 2*emb_size, sent_len
input_tensor3_r=joint_embed_input_claim,
mask_matrix=sents_mask,
mask_matrix_r=claim_mask,
image_shape=(batch_size, 1, emb_size, sent_len),
image_shape_r=(batch_size, 1, emb_size, sent_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[0], 1, emb_size, 1),
W=att_conv_W,
b=att_conv_b,
W_context=conv_W_context,
b_context=conv_b_context)
attentive_sent_embeddings_l = attentive_conv_layer.attentive_maxpool_vec_l #(batch_size*cand_size, hidden_size)
attentive_sent_embeddings_r = attentive_conv_layer.attentive_maxpool_vec_r
"Logistic Regression layer"
joint_LR_input = T.concatenate([
joint_premise_hypo_emb, attentive_sent_embeddings_l,
attentive_sent_embeddings_r
],
axis=1)
joint_LR_input_size = 2 * hidden_size[0] + 2 * hidden_size[0]
joint_U_a = create_ensemble_para(rng, 3,
joint_LR_input_size) # (input_size, 3)
joint_LR_b = theano.shared(value=np.zeros((3, ),
dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
joint_LR_para = [joint_U_a, joint_LR_b]
joint_layer_LR = LogisticRegression(
rng,
input=joint_LR_input,
n_in=joint_LR_input_size,
n_out=3,
W=joint_U_a,
b=joint_LR_b
) #basically it is a multiplication between weight matrix and input feature vector
# joint_loss=joint_layer_LR.negative_log_likelihood(joint_labels) #for classification task, we usually used negative log likelihood as loss, the lower the better.
'''
testing
joint_sents_ids=T.itensor3() #(batch, cand_size, sent_len)
joint_sents_mask=T.ftensor3()
joint_sents_labels=T.imatrix() #(batch, cand_size)
joint_claim_ids = T.imatrix() #(batch, claim_len)
joint_claim_mask = T.fmatrix()
joint_labels=T.ivector()
'''
pred_minibatch_labels = joint_layer_LR.y_pred
pred_minibatch_labels_2_2classes = T.where(pred_minibatch_labels > 1, 0,
pred_minibatch_labels)
pred_minibatch_error = T.mean(
T.neq(pred_minibatch_labels_2_2classes, labels))
params = [init_embeddings] + NN_para + joint_LR_para
load_model_from_file(root + 'para_for_test_scitail', params)
# train_model = theano.function([sents_ids,sents_mask,sents_labels,claim_ids,claim_mask,joint_sents_ids,joint_sents_mask,joint_sents_labels, joint_claim_ids, joint_claim_mask, joint_labels], cost, updates=updates, allow_input_downcast=True, on_unused_input='ignore')
test_model = theano.function(
[sents_ids, sents_mask, claim_ids, claim_mask, labels],
pred_minibatch_error,
allow_input_downcast=True,
on_unused_input='ignore')
# dev_model = theano.function([joint_sents_ids,joint_sents_mask,joint_sents_labels, joint_claim_ids, joint_claim_mask, joint_labels], pred_minibatch_error, allow_input_downcast=True, on_unused_input='ignore')
# test_model = theano.function([sents_ids,sents_mask,sents_labels, claim_ids,claim_mask, joint_labels], [inter_matrix,test_layer_LR.errors(joint_labels), test_layer_LR.y_pred], allow_input_downcast=True, on_unused_input='ignore')
# dev_model = theano.function([sents_ids,sents_mask,sents_labels, claim_ids,claim_mask, joint_labels], [inter_matrix,test_layer_LR.errors(joint_labels), test_layer_LR.y_pred], allow_input_downcast=True, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... testing'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
# joint_n_train_batches=joint_train_size/batch_size
# joint_train_batch_start=list(np.arange(joint_n_train_batches)*batch_size)+[joint_train_size-batch_size]
# n_train_batches=train_size/batch_size
# train_batch_start=list(np.arange(n_train_batches)*batch_size)+[train_size-batch_size]
# n_dev_batches=dev_size/batch_size
# dev_batch_start=list(np.arange(n_dev_batches)*batch_size)+[dev_size-batch_size]
n_test_batches = test_size / batch_size
remain_test_batches = test_size % batch_size
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
max_acc_dev = 0.0
max_acc_test = 0.0
cost_i = 0.0
error_sum = 0.0
for idd, test_batch_id in enumerate(
test_batch_start): # for each test batch
error_i = test_model(
test_sents_l[test_batch_id:test_batch_id + batch_size],
test_masks_l[test_batch_id:test_batch_id + batch_size],
test_sents_r[test_batch_id:test_batch_id + batch_size],
test_masks_r[test_batch_id:test_batch_id + batch_size],
test_labels_store[test_batch_id:test_batch_id + batch_size])
error_sum += error_i
test_acc = 1.0 - error_sum / (len(test_batch_start))
print '\tcurrent test_acc:', test_acc
def evaluate_lenet5(learning_rate=0.02, n_epochs=100, emb_size=300, batch_size=50, filter_size=[3], sent_len=40, claim_len=20, cand_size=10,hidden_size=[300,300], max_pred_pick=5):
model_options = locals().copy()
print "model options", model_options
seed=1234
np.random.seed(seed)
rng = np.random.RandomState(seed) #random seed, control the model generates the same results
srng = T.shared_randomstreams.RandomStreams(rng.randint(seed))
"load raw data"
train_sents, train_sent_masks, train_sent_labels, train_claims, train_claim_mask, _, word2id = load_fever_train(sent_len, claim_len, cand_size)
test_sents, test_sent_masks, test_sent_labels, test_claims, test_claim_mask, test_sent_names,test_ground_names,_, word2id = load_fever_dev(sent_len, claim_len, cand_size, word2id)
train_sents=np.asarray(train_sents, dtype='int32')
# dev_sents_l=np.asarray(all_sentences_l[1], dtype='int32')
test_sents=np.asarray(test_sents, dtype='int32')
train_sent_masks=np.asarray(train_sent_masks, dtype=theano.config.floatX)
# dev_masks_l=np.asarray(all_masks_l[1], dtype=theano.config.floatX)
test_sent_masks=np.asarray(test_sent_masks, dtype=theano.config.floatX)
train_sent_labels=np.asarray(train_sent_labels, dtype='int32')
# dev_sents_r=np.asarray(all_sentences_r[1] , dtype='int32')
# test_sent_labels=np.asarray(test_sent_labels, dtype='int32')
train_claims=np.asarray(train_claims, dtype='int32')
# dev_sents_r=np.asarray(all_sentences_r[1] , dtype='int32')
test_claims=np.asarray(test_claims, dtype='int32')
train_claim_mask=np.asarray(train_claim_mask, dtype=theano.config.floatX)
# dev_masks_r=np.asarray(all_masks_r[1], dtype=theano.config.floatX)
test_claim_mask=np.asarray(test_claim_mask, dtype=theano.config.floatX)
# train_labels_store=np.asarray(all_labels[0], dtype='int32')
# dev_labels_store=np.asarray(all_labels[1], dtype='int32')
# test_labels_store=np.asarray(all_labels[2], dtype='int32')
train_size=len(train_claims)
# dev_size=len(dev_labels_store)
test_size=len(test_claims)
print 'train size: ', train_size, ' test size: ', test_size
vocab_size=len(word2id)+1
rand_values=rng.normal(0.0, 0.01, (vocab_size, emb_size)) #generate a matrix by Gaussian distribution
id2word = {y:x for x,y in word2id.iteritems()}
word2vec=load_word2vec()
rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
init_embeddings=theano.shared(value=np.array(rand_values,dtype=theano.config.floatX), borrow=True) #wrap up the python variable "rand_values" into theano variable
"now, start to build the input form of the model"
sents_ids=T.itensor3() #(batch, cand_size, sent_len)
sents_mask=T.ftensor3()
sents_labels=T.imatrix() #(batch, cand_size)
claim_ids = T.imatrix() #(batch, claim_len)
claim_mask = T.imatrix()
# labels=T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
embed_input_sents=init_embeddings[sents_ids.flatten()].reshape((batch_size*cand_size, sent_len, emb_size)).dimshuffle(0,2,1)#embed_input(init_embeddings, sents_ids_l)#embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
embed_input_claim=init_embeddings[claim_ids.flatten()].reshape((batch_size,claim_len, emb_size)).dimshuffle(0,2,1)
conv_W, conv_b=create_conv_para(rng, filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]))
# conv_W2, conv_b2=create_conv_para(rng, filter_shape=(hidden_size[0], 1, emb_size, filter_size[1]))
NN_para = [conv_W, conv_b]
conv_model_sents = Conv_with_Mask(rng, input_tensor3=embed_input_sents,
mask_matrix = sents_mask.reshape((sents_mask.shape[0]*sents_mask.shape[1],sents_mask.shape[2])),
image_shape=(batch_size*cand_size, 1, emb_size, sent_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]), W=conv_W, b=conv_b) #mutiple mask with the conv_out to set the features by UNK to zero
sent_embeddings=conv_model_sents.maxpool_vec #(batch_size*cand_size, hidden_size) # each sentence then have an embedding of length hidden_size
batch_sent_emb = sent_embeddings.reshape((batch_size, cand_size, hidden_size[0]))
conv_model_claims = Conv_with_Mask(rng, input_tensor3=embed_input_claim,
mask_matrix = claim_mask,
image_shape=(batch_size, 1, emb_size, claim_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]), W=conv_W, b=conv_b) #mutiple mask with the conv_out to set the features by UNK to zero
claim_embeddings=conv_model_claims.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
batch_claim_emb = T.repeat(claim_embeddings.dimshuffle(0,'x', 1), cand_size, axis=1)
concate_claim_sent = T.concatenate([batch_claim_emb,batch_sent_emb ], axis=2)
concate_2_matrix = concate_claim_sent.reshape((batch_size*cand_size, hidden_size[0]*2))
LR_input = concate_2_matrix#T.concatenate([sent_embeddings,sent_embeddings2], axis=1)
LR_input_size = hidden_size[0]*2
#classification layer, it is just mapping from a feature vector of size "hidden_size" to a vector of only two values: positive, negative
U_a = create_ensemble_para(rng, 1, LR_input_size) # the weight matrix hidden_size*2
# LR_b = theano.shared(value=np.zeros((8,),dtype=theano.config.floatX),name='LR_b', borrow=True) #bias for each target class
LR_para=[U_a]
# layer_LR=LogisticRegression(rng, input=LR_input, n_in=LR_input_size, n_out=8, W=U_a, b=LR_b) #basically it is a multiplication between weight matrix and input feature vector
score_matrix = T.nnet.sigmoid(concate_2_matrix.dot(U_a)) #batch * 12
inter_matrix = score_matrix.reshape((batch_size, cand_size))
# inter_sent_claim = T.batched_dot(batch_sent_emb, batch_claim_emb) #(batch_size, cand_size, 1)
# inter_matrix = T.nnet.sigmoid(inter_sent_claim.reshape((batch_size, cand_size)))
'''
maybe 1.0-inter_matrix can be rewritten into 1/e^(inter_matrix)
'''
prob_pos = T.where( sents_labels < 1, 1.0-inter_matrix, inter_matrix)
loss = -T.mean(T.log(prob_pos))
#
# "Logistic Regression layer"
# LR_input = T.concatenate([attentive_sent_embeddings_l,attentive_sent_embeddings_r,attentive_sent_embeddings_l+attentive_sent_embeddings_r,attentive_sent_embeddings_l*attentive_sent_embeddings_r],axis=1)
# LR_input_size=4*hidden_size[0]
#
# U_a = create_ensemble_para(rng, 3, LR_input_size) # (input_size, 3)
# LR_b = theano.shared(value=np.zeros((3,),dtype=theano.config.floatX),name='LR_b', borrow=True) #bias for each target class
# LR_para=[U_a, LR_b]
#
# layer_LR=LogisticRegression(rng, input=normalize_matrix_col_wise(LR_input), n_in=LR_input_size, n_out=3, W=U_a, b=LR_b) #basically it is a multiplication between weight matrix and input feature vector
# loss=layer_LR.negative_log_likelihood(labels) #for classification task, we usually used negative log likelihood as loss, the lower the better.
'''
testing
'''
binarize_prob = T.where( inter_matrix > 0.5, 1, 0) #(batch_size, cand_size
params = [init_embeddings]+NN_para+LR_para
cost=loss
"Use AdaGrad to update parameters"
updates = Gradient_Cost_Para(cost,params, learning_rate)
train_model = theano.function([sents_ids,sents_mask,sents_labels,claim_ids,claim_mask], cost, updates=updates, allow_input_downcast=True, on_unused_input='ignore')
# dev_model = theano.function([sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels], layer_LR.errors(labels), allow_input_downcast=True, on_unused_input='ignore')
test_model = theano.function([sents_ids,sents_mask,claim_ids,claim_mask], inter_matrix, allow_input_downcast=True, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
n_train_batches=train_size/batch_size
train_batch_start=list(np.arange(n_train_batches)*batch_size)+[train_size-batch_size]
# n_dev_batches=dev_size/batch_size
# dev_batch_start=list(np.arange(n_dev_batches)*batch_size)+[dev_size-batch_size]
n_test_batches=test_size/batch_size
test_batch_start=list(np.arange(n_test_batches)*batch_size)+[test_size-batch_size]
max_acc_dev=0.0
max_test_f1=0.0
cost_i=0.0
train_indices = range(train_size)
while epoch < n_epochs:
epoch = epoch + 1
random.Random(100).shuffle(train_indices) #shuffle training set for each new epoch, is supposed to promote performance, but not garrenteed
iter_accu=0
for batch_id in train_batch_start: #for each batch
# iter means how many batches have been run, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
iter_accu+=1
train_id_batch = train_indices[batch_id:batch_id+batch_size]
'''
train_sents, train_sent_masks, train_sent_labels, train_claims, train_claim_mask
sents_ids,sents_mask,sents_labels,claim_ids,claim_mask
'''
cost_i+= train_model(
train_sents[train_id_batch],
train_sent_masks[train_id_batch],
train_sent_labels[train_id_batch],
train_claims[train_id_batch],
train_claim_mask[train_id_batch])
#after each 1000 batches, we test the performance of the model on all test data
# if (epoch==1 and iter%1000==0) or (epoch>=2 and iter%5==0):
if iter%10==0:
print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
past_time = time.time()
'''
test
test_sents, test_sent_masks, test_sent_labels, test_claims, test_claim_mask,
sents_ids,sents_mask,claim_ids,claim_mask
'''
f1_sum=0.0
for test_batch_id in test_batch_start: # for each test batch
batch_prob=test_model(
test_sents[test_batch_id:test_batch_id+batch_size],
test_sent_masks[test_batch_id:test_batch_id+batch_size],
test_claims[test_batch_id:test_batch_id+batch_size],
test_claim_mask[test_batch_id:test_batch_id+batch_size])
batch_sent_labels = test_sent_labels[test_batch_id:test_batch_id+batch_size]
batch_sent_names = test_sent_names[test_batch_id:test_batch_id+batch_size]
batch_ground_names = test_ground_names[test_batch_id:test_batch_id+batch_size]
for i in range(batch_size):
pred_sent_names = []
gold_sent_names = batch_ground_names[i]
zipped=[(batch_prob[i,k],batch_sent_labels[i][k],batch_sent_names[i][k]) for k in range(cand_size)]
sorted_zip = sorted(zipped, key=lambda x: x[0], reverse=True)
# print 'sorted_zip:', sorted_zip
# exit(0)
for j in range(cand_size):
triple = sorted_zip[j]
if triple[1] == 1.0:
'''
we should consider a rank, instead of binary
'''
if triple[0] >0.5:
pred_sent_names.append(batch_sent_names[i][j])
if len(pred_sent_names) == max_pred_pick:
break
f1_i = compute_f1_two_list_names(pred_sent_names, gold_sent_names)
f1_sum+=f1_i
test_f1=f1_sum/(len(test_batch_start)*batch_size)
if test_f1 > max_test_f1:
max_test_f1=test_f1
print '\t\tcurrent test_f1:', test_f1,' ; ','\t\t\t\t\tmax_test_f1:', max_test_f1
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
return max_acc_test
def __init__(self, rng,
origin_input_tensor3, origin_input_tensor3_r,
input_tensor3, input_tensor3_r,
#our method
cm_origin_input_tensor3, cm_origin_input_tensor3_r,
cm_input_tensor3, cm_input_tensor3_r,
###########
mask_matrix, mask_matrix_r,
filter_shape, filter_shape_context,
image_shape, image_shape_r,
W, b, W_posi, b_posi, W_context, b_context, posi_emb_matrix, posi_emb_size, K_ratio):
batch_size = origin_input_tensor3.shape[0]
hidden_size = origin_input_tensor3.shape[1]
l_len = origin_input_tensor3.shape[2]
r_len = origin_input_tensor3_r.shape[2]
#construct interaction matrix
input_tensor3 = input_tensor3*mask_matrix.dimshuffle(0,'x',1)
input_tensor3_r = input_tensor3_r*mask_matrix_r.dimshuffle(0,'x',1) #(batch, hidden, r_len)
dot_tensor3 = T.batched_dot(input_tensor3.dimshuffle(0,2,1),input_tensor3_r) #(batch, l_len, r_len)
# our method
cm_input_tensor3 = cm_input_tensor3 * mask_matrix.dimshuffle(0,'x',1)
cm_input_tensor3_r = cm_input_tensor3_r * mask_matrix_r.dimshuffle(0,'x',1) #(batch, hidden, r_len)
cm_dot_tensor3 = T.batched_dot(cm_input_tensor3.dimshuffle(0,2,1), cm_input_tensor3_r) #(batch, l_len, r_len)
new_dot_tensor3 = dot_tensor3 + 0.01 * cm_dot_tensor3
'''
try to get position shift of best match
'''
aligned_posi_l = T.argmax(dot_tensor3, axis=2).flatten()
posi_emb_tensor3_l = posi_emb_matrix[aligned_posi_l].reshape((batch_size,dot_tensor3.shape[1],posi_emb_size)).dimshuffle(0,2,1) #(batch, emb_size, l_len)
aligned_posi_r = T.argmax(dot_tensor3, axis=1).flatten()
posi_emb_tensor3_r = posi_emb_matrix[aligned_posi_r].reshape((batch_size,dot_tensor3.shape[2],posi_emb_size)).dimshuffle(0,2,1) #(batch, emb_size, r_len)
l_max_cos = (1.0 - T.min(selu(cm_dot_tensor3), axis=2))/(1.0 + 0.5 * T.max(selu(dot_tensor3), axis=2))#1.0/T.exp(T.max(T.nnet.sigmoid(dot_tensor3), axis=2)) #(batch, l_len)
r_max_cos = (1.0 - T.min(selu(cm_dot_tensor3), axis=1))/(1.0 + 0.5 * T.max(selu(dot_tensor3), axis=1))#1.0/T.exp(T.max(T.nnet.sigmoid(dot_tensor3), axis=1)) #(batch, r_len)
'''
another interaction matrix
'''
dot_matrix_for_right = T.nnet.softmax(new_dot_tensor3.reshape((batch_size*l_len, r_len))) #(batch*l_len, r_len)
dot_tensor3_for_right = dot_matrix_for_right.reshape((batch_size, l_len, r_len))#(batch, l_len, r_len)
weighted_sum_r = T.batched_dot(dot_tensor3_for_right, input_tensor3_r.dimshuffle(0,2,1)).dimshuffle(0,2,1)*mask_matrix.dimshuffle(0,'x',1) #(batch,hidden, l_len)
dot_matrix_for_left = T.nnet.softmax(new_dot_tensor3.dimshuffle(0,2,1).reshape((batch_size*r_len, l_len))) #(batch*r_len, l_len)
dot_tensor3_for_left = dot_matrix_for_left.reshape((batch_size, r_len, l_len))#(batch, r_len, l_len)
weighted_sum_l = T.batched_dot(dot_tensor3_for_left, input_tensor3.dimshuffle(0,2,1)).dimshuffle(0,2,1)*mask_matrix_r.dimshuffle(0,'x',1) #(batch,hidden, r_len)
#convolve left, weighted sum r
biased_conv_model_l = Conv_with_Mask(rng, input_tensor3=origin_input_tensor3*l_max_cos.dimshuffle(0,'x',1),
mask_matrix = mask_matrix,
image_shape=image_shape,
filter_shape=filter_shape, W=W, b=b)
biased_temp_conv_output_l = biased_conv_model_l.naked_conv_out
self.conv_out_l = biased_conv_model_l.masked_conv_out
self.maxpool_vec_l = biased_conv_model_l.maxpool_vec
conv_model_l = Conv_with_Mask(rng, input_tensor3=T.concatenate([origin_input_tensor3,posi_emb_tensor3_l],axis=1),
mask_matrix = mask_matrix,
image_shape=(image_shape[0], image_shape[1], image_shape[2]+posi_emb_size, image_shape[3]),
filter_shape=(filter_shape[0],filter_shape[1],filter_shape[2]+posi_emb_size,filter_shape[3]), W=W_posi, b=b_posi)
temp_conv_output_l = conv_model_l.naked_conv_out
conv_model_weighted_r = Conv_with_Mask(rng, input_tensor3=weighted_sum_r,
mask_matrix = mask_matrix,
image_shape=image_shape,
filter_shape=filter_shape_context, W=W_context, b=b_context) # note that b_context is not used
temp_conv_output_weighted_r = conv_model_weighted_r.naked_conv_out
'''
combine
'''
mask_for_conv_output_l=T.repeat(mask_matrix.dimshuffle(0,'x',1), filter_shape[0], axis=1) #(batch_size, emb_size, maxSentLen-filter_size+1)
mask_for_conv_output_l=(1.0-mask_for_conv_output_l)*(mask_for_conv_output_l-10)
self.biased_conv_attend_out_l = T.tanh(biased_temp_conv_output_l+ temp_conv_output_weighted_r+ b.dimshuffle('x', 0, 'x'))*mask_matrix.dimshuffle(0,'x',1)
self.biased_attentive_sumpool_vec_l=T.sum(self.biased_conv_attend_out_l, axis=2)
self.biased_attentive_meanpool_vec_l=self.biased_attentive_sumpool_vec_l/T.sum(mask_matrix,axis=1).dimshuffle(0,'x')
masked_biased_conv_output_l=self.biased_conv_attend_out_l+mask_for_conv_output_l #mutiple mask with the conv_out to set the features by UNK to zero
self.biased_attentive_maxpool_vec_l=T.max(masked_biased_conv_output_l, axis=2) #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
self.conv_attend_out_l = T.tanh(temp_conv_output_l+ temp_conv_output_weighted_r+ b_posi.dimshuffle('x', 0, 'x'))*mask_matrix.dimshuffle(0,'x',1)
masked_conv_output_l=self.conv_attend_out_l+mask_for_conv_output_l #mutiple mask with the conv_out to set the features by UNK to zero
self.attentive_maxpool_vec_l=T.max(masked_conv_output_l, axis=2) #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
#convolve right, weighted sum l
biased_conv_model_r = Conv_with_Mask(rng, input_tensor3=origin_input_tensor3_r*r_max_cos.dimshuffle(0,'x',1),
mask_matrix = mask_matrix_r,
image_shape=image_shape_r,
filter_shape=filter_shape, W=W, b=b)
biased_temp_conv_output_r = biased_conv_model_r.naked_conv_out
self.conv_out_r = biased_conv_model_r.masked_conv_out
self.maxpool_vec_r = biased_conv_model_r.maxpool_vec
conv_model_r = Conv_with_Mask(rng, input_tensor3=T.concatenate([origin_input_tensor3_r,posi_emb_tensor3_r],axis=1),
mask_matrix = mask_matrix_r,
image_shape=(image_shape_r[0],image_shape_r[1],image_shape_r[2]+posi_emb_size,image_shape_r[3]),
filter_shape=(filter_shape[0],filter_shape[1],filter_shape[2]+posi_emb_size,filter_shape[3]), W=W_posi, b=b_posi)
temp_conv_output_r = conv_model_r.naked_conv_out
conv_model_weighted_l = Conv_with_Mask(rng, input_tensor3=weighted_sum_l,
mask_matrix = mask_matrix_r,
image_shape=image_shape_r,
filter_shape=filter_shape_context, W=W_context, b=b_context) # note that b_context is not used
temp_conv_output_weighted_l = conv_model_weighted_l.naked_conv_out
'''
combine
'''
mask_for_conv_output_r=T.repeat(mask_matrix_r.dimshuffle(0,'x',1), filter_shape[0], axis=1) #(batch_size, emb_size, maxSentLen-filter_size+1)
mask_for_conv_output_r=(1.0-mask_for_conv_output_r)*(mask_for_conv_output_r-10)
self.biased_conv_attend_out_r = T.tanh(biased_temp_conv_output_r+ temp_conv_output_weighted_l+ b.dimshuffle('x', 0, 'x'))*mask_matrix_r.dimshuffle(0,'x',1)
self.biased_attentive_sumpool_vec_r=T.sum(self.biased_conv_attend_out_r, axis=2)
self.biased_attentive_meanpool_vec_r=self.biased_attentive_sumpool_vec_r/T.sum(mask_matrix_r,axis=1).dimshuffle(0,'x')
self.conv_attend_out_r = T.tanh(temp_conv_output_r+ temp_conv_output_weighted_l+ b_posi.dimshuffle('x', 0, 'x'))*mask_matrix_r.dimshuffle(0,'x',1)
masked_biased_conv_output_r=self.biased_conv_attend_out_r+mask_for_conv_output_r #mutiple mask with the conv_out to set the features by UNK to zero
self.biased_attentive_maxpool_vec_r=T.max(masked_biased_conv_output_r, axis=2) #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
masked_conv_output_r=self.conv_attend_out_r+mask_for_conv_output_r #mutiple mask with the conv_out to set the features by UNK to zero
self.attentive_maxpool_vec_r=T.max(masked_conv_output_r, axis=2) #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
