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(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=4,
L2_weight=0.0000001,
extra_size=4,
drop_p=0.0,
div_weight=0.00001,
emb_size=300,
batch_size=50,
filter_size=[3, 3],
maxSentLen=40,
hidden_size=[300, 300],
comment='only one HL layer, drop0.0'):
model_options = locals().copy()
print "model options", model_options
first_seeds = [1234, 1235, 1236, 1237] #first copy starts by 1
first_rngs = [
np.random.RandomState(first_seeds[0]),
np.random.RandomState(first_seeds[1]),
np.random.RandomState(first_seeds[2]),
np.random.RandomState(first_seeds[3])
] #random seed, control the model generates the same results
first_srng = RandomStreams(first_rngs[0].randint(999999))
all_sentences_l, all_masks_l, all_sentences_r, all_masks_r, all_extra, all_labels, word2id, test_rows = load_SNLI_dataset_with_extra_with_test(
maxlen=maxSentLen
) #minlen, include one label, at least one word in the sentence
train_sents_l = np.asarray(all_sentences_l[0], dtype='int32')
dev_sents_l = np.asarray(all_sentences_l[1], dtype='int32')
# train_sents_l = np.concatenate((train_sents_l, dev_sents_l), axis=0)
test_sents_l = np.asarray(all_sentences_l[2], dtype='int32')
train_masks_l = np.asarray(all_masks_l[0], dtype=theano.config.floatX)
dev_masks_l = np.asarray(all_masks_l[1], dtype=theano.config.floatX)
# train_masks_l = np.concatenate((train_masks_l, dev_masks_l), axis=0)
test_masks_l = np.asarray(all_masks_l[2], dtype=theano.config.floatX)
train_sents_r = np.asarray(all_sentences_r[0], dtype='int32')
dev_sents_r = np.asarray(all_sentences_r[1], dtype='int32')
# train_sents_r = np.concatenate((train_sents_r, dev_sents_r), axis=0)
test_sents_r = np.asarray(all_sentences_r[2], dtype='int32')
train_masks_r = np.asarray(all_masks_r[0], dtype=theano.config.floatX)
dev_masks_r = np.asarray(all_masks_r[1], dtype=theano.config.floatX)
# train_masks_r = np.concatenate((train_masks_r, dev_masks_r), axis=0)
test_masks_r = np.asarray(all_masks_r[2], dtype=theano.config.floatX)
train_extra = np.asarray(all_extra[0], dtype=theano.config.floatX)
dev_extra = np.asarray(all_extra[1], dtype=theano.config.floatX)
test_extra = np.asarray(all_extra[2], dtype=theano.config.floatX)
train_labels_store = np.asarray(all_labels[0], dtype='int32')
dev_labels_store = np.asarray(all_labels[1], dtype='int32')
# train_labels_store = np.concatenate((train_labels_store, dev_labels_store), axis=0)
test_labels_store = np.asarray(all_labels[2], dtype='int32')
train_size = len(train_labels_store)
dev_size = len(dev_labels_store)
test_size = len(test_labels_store)
print 'train size: ', train_size, ' dev size: ', dev_size, ' test size: ', test_size
vocab_size = len(word2id) + 1
rand_values = first_rngs[0].normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
#here, we leave code for loading word2vec to initialize words
rand_values[0] = np.array(np.zeros(emb_size), dtype=theano.config.floatX)
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_word2vec()
# word2vec =extend_word2vec_lowercase(word2vec)
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
first_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
train_flag = T.iscalar()
first_sents_ids_l = T.imatrix()
first_sents_mask_l = T.fmatrix()
first_sents_ids_r = T.imatrix()
first_sents_mask_r = T.fmatrix()
first_labels = T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
def common_input(emb_matrix, sent_ids):
return emb_matrix[sent_ids.flatten()].reshape(
(batch_size, maxSentLen, emb_size)).dimshuffle(0, 2, 1)
first_common_input_l = dropout_layer(
first_srng, common_input(first_embeddings,
first_sents_ids_l), drop_p, train_flag
) #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
first_common_input_r = dropout_layer(
first_srng, common_input(first_embeddings,
first_sents_ids_r), drop_p, train_flag
) #embeddings[sents_ids_r.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1)
gate_filter_shape = (hidden_size[0], 1, emb_size, 1)
def create_CNN_params(rng):
conv_W_2_pre, conv_b_2_pre = create_conv_para(
rng, filter_shape=gate_filter_shape)
conv_W_2_gate, conv_b_2_gate = create_conv_para(
rng, filter_shape=gate_filter_shape)
conv_W_2, conv_b_2 = create_conv_para(rng,
filter_shape=(hidden_size[1], 1,
hidden_size[0],
filter_size[0]))
conv_W_2_context, conv_b_2_context = create_conv_para(
rng, filter_shape=(hidden_size[1], 1, hidden_size[0], 1))
return conv_W_2_pre, conv_b_2_pre, conv_W_2_gate, conv_b_2_gate, conv_W_2, conv_b_2, conv_W_2_context, conv_b_2_context
first_conv_W_pre, first_conv_b_pre, first_conv_W_gate, first_conv_b_gate, first_conv_W, first_conv_b, first_conv_W_context, first_conv_b_context = create_CNN_params(
first_rngs[0])
first_2_conv_W_pre, first_2_conv_b_pre, first_2_conv_W_gate, first_2_conv_b_gate, first_2_conv_W, first_2_conv_b, first_2_conv_W_context, first_2_conv_b_context = create_CNN_params(
first_rngs[1])
first_3_conv_W_pre, first_3_conv_b_pre, first_3_conv_W_gate, first_3_conv_b_gate, first_3_conv_W, first_3_conv_b, first_3_conv_W_context, first_3_conv_b_context = create_CNN_params(
first_rngs[2])
first_4_conv_W_pre, first_4_conv_b_pre, first_4_conv_W_gate, first_4_conv_b_gate, first_4_conv_W, first_4_conv_b, first_4_conv_W_context, first_4_conv_b_context = create_CNN_params(
first_rngs[3])
# first_5_conv_W_pre, first_5_conv_b_pre,first_5_conv_W_gate, first_5_conv_b_gate,first_5_conv_W, first_5_conv_b,first_5_conv_W_context, first_5_conv_b_context = create_CNN_params(first_rng)
# first_6_conv_W_pre, first_6_conv_b_pre,first_6_conv_W_gate, first_6_conv_b_gate,first_6_conv_W, first_6_conv_b,first_6_conv_W_context, first_6_conv_b_context = create_CNN_params(first_rng)
first_CNN_1_para = [
first_conv_W_pre, first_conv_b_pre, first_conv_W_gate,
first_conv_b_gate, first_conv_W, first_conv_b, first_conv_W_context
]
first_CNN_2_para = [
first_2_conv_W_pre, first_2_conv_b_pre, first_2_conv_W_gate,
first_2_conv_b_gate, first_2_conv_W, first_2_conv_b,
first_2_conv_W_context
]
first_CNN_3_para = [
first_3_conv_W_pre, first_3_conv_b_pre, first_3_conv_W_gate,
first_3_conv_b_gate, first_3_conv_W, first_3_conv_b,
first_3_conv_W_context
]
first_CNN_4_para = [
first_4_conv_W_pre, first_4_conv_b_pre, first_4_conv_W_gate,
first_4_conv_b_gate, first_4_conv_W, first_4_conv_b,
first_4_conv_W_context
]
'''
first copy
'''
def copy(
rngs,
common_input_l,
common_input_r,
sents_mask_l,
sents_mask_r,
drop_conv_W_1_pre,
conv_b_1_pre,
drop_conv_W_1_gate,
conv_b_1_gate,
drop_conv_W_1,
conv_b_1,
drop_conv_W_1_context,
conv_b_1_context,
drop_conv_W_1_pre_2,
conv_b_1_pre_2,
drop_conv_W_1_gate_2,
conv_b_1_gate_2,
drop_conv_W_1_2,
conv_b_1_2,
drop_conv_W_1_context_2,
conv_b_1_context_2,
drop_conv_W_1_pre_3,
conv_b_1_pre_3,
drop_conv_W_1_gate_3,
conv_b_1_gate_3,
drop_conv_W_1_3,
conv_b_1_3,
drop_conv_W_1_context_3,
conv_b_1_context_3,
drop_conv_W_1_pre_4,
conv_b_1_pre_4,
drop_conv_W_1_gate_4,
conv_b_1_gate_4,
drop_conv_W_1_4,
conv_b_1_4,
drop_conv_W_1_context_4,
conv_b_1_context_4,
# drop_conv_W_1_pre_5,conv_b_1_pre_5,drop_conv_W_1_gate_5,conv_b_1_gate_5,
# drop_conv_W_1_5,conv_b_1_5,drop_conv_W_1_context_5, conv_b_1_context_5,
#
# drop_conv_W_1_pre_6,conv_b_1_pre_6,drop_conv_W_1_gate_6,conv_b_1_gate_6,
# drop_conv_W_1_6,conv_b_1_6,drop_conv_W_1_context_6, conv_b_1_context_6,
labels):
loss_0_0, distr_0_0, params_0_0 = one_classifier_in_one_copy(
rngs[0], common_input_l, common_input_r, sents_mask_l,
sents_mask_r, batch_size, emb_size, maxSentLen, gate_filter_shape,
hidden_size, filter_size, first_srng, drop_p, train_flag, labels,
drop_conv_W_1_pre, conv_b_1_pre, drop_conv_W_1_gate, conv_b_1_gate,
drop_conv_W_1, conv_b_1, drop_conv_W_1_context, conv_b_1_context,
True)
loss_0_1, distr_0_1, params_0_1 = one_classifier_in_one_copy(
rngs[1], common_input_l, common_input_r, sents_mask_l,
sents_mask_r, batch_size, emb_size, maxSentLen, gate_filter_shape,
hidden_size, filter_size, first_srng, drop_p, train_flag, labels,
drop_conv_W_1_pre_2, conv_b_1_pre_2, drop_conv_W_1_gate_2,
conv_b_1_gate_2, drop_conv_W_1_2, conv_b_1_2,
drop_conv_W_1_context_2, conv_b_1_context_2, False)
loss_0_2, distr_0_2, params_0_2 = one_classifier_in_one_copy(
rngs[2], common_input_l, common_input_r, sents_mask_l,
sents_mask_r, batch_size, emb_size, maxSentLen, gate_filter_shape,
hidden_size, filter_size, first_srng, drop_p, train_flag, labels,
drop_conv_W_1_pre_3, conv_b_1_pre_3, drop_conv_W_1_gate_3,
conv_b_1_gate_3, drop_conv_W_1_3, conv_b_1_3,
drop_conv_W_1_context_3, conv_b_1_context_3, True)
loss_0_3, distr_0_3, params_0_3 = one_classifier_in_one_copy(
rngs[3], common_input_l, common_input_r, sents_mask_l,
sents_mask_r, batch_size, emb_size, maxSentLen, gate_filter_shape,
hidden_size, filter_size, first_srng, drop_p, train_flag, labels,
drop_conv_W_1_pre_4, conv_b_1_pre_4, drop_conv_W_1_gate_4,
conv_b_1_gate_4, drop_conv_W_1_4, conv_b_1_4,
drop_conv_W_1_context_4, conv_b_1_context_4, False)
# loss_0_4, distr_0_4, params_0_4 = one_classifier_in_one_copy(rng, common_input_l,common_input_r,sents_mask_l,sents_mask_r,batch_size, emb_size,
# maxSentLen,gate_filter_shape,hidden_size,filter_size,
# first_srng, drop_p,train_flag,labels,
# drop_conv_W_1_pre_5,conv_b_1_pre_5,drop_conv_W_1_gate_5,conv_b_1_gate_5,
# drop_conv_W_1_5,conv_b_1_5,drop_conv_W_1_context_5,conv_b_1_context_5,
# True)
# loss_0_5, distr_0_5, params_0_5 = one_classifier_in_one_copy(rng, common_input_l,common_input_r,sents_mask_l,sents_mask_r,batch_size, emb_size,
# maxSentLen,gate_filter_shape,hidden_size,filter_size,
# first_srng, drop_p,train_flag,labels,
# drop_conv_W_1_pre_6,conv_b_1_pre_6,drop_conv_W_1_gate_6,conv_b_1_gate_6,
# drop_conv_W_1_6,conv_b_1_6,drop_conv_W_1_context_6,conv_b_1_context_6,
# False)
# psp_label = T.repeat(labels, multi_psp_size)
loss_0 = [(loss_0_0 + loss_0_1 + loss_0_2 + loss_0_3) / 4.0, loss_0_0,
loss_0_1, loss_0_2, loss_0_3] #5
para_0 = [params_0_0, params_0_1, params_0_2, params_0_3]
# loss = loss_0+loss_1+loss_2
batch_distr = distr_0_0 + distr_0_1 + distr_0_2 + distr_0_3 #T.sum((layer_LR.p_y_given_x).reshape((batch_size, multi_psp_size,3)), axis=1) #(batch, 3)
return loss_0, para_0, batch_distr
first_loss, first_classifier_params, first_test_distr = copy(
first_rngs,
first_common_input_l,
first_common_input_r,
first_sents_mask_l,
first_sents_mask_r,
first_conv_W_pre,
first_conv_b_pre,
first_conv_W_gate,
first_conv_b_gate,
first_conv_W,
first_conv_b,
first_conv_W_context,
first_conv_b_context,
first_2_conv_W_pre,
first_2_conv_b_pre,
first_2_conv_W_gate,
first_2_conv_b_gate,
first_2_conv_W,
first_2_conv_b,
first_2_conv_W_context,
first_2_conv_b_context,
first_3_conv_W_pre,
first_3_conv_b_pre,
first_3_conv_W_gate,
first_3_conv_b_gate,
first_3_conv_W,
first_3_conv_b,
first_3_conv_W_context,
first_3_conv_b_context,
first_4_conv_W_pre,
first_4_conv_b_pre,
first_4_conv_W_gate,
first_4_conv_b_gate,
first_4_conv_W,
first_4_conv_b,
first_4_conv_W_context,
first_4_conv_b_context,
# drop_first_5_conv_W_pre,first_5_conv_b_pre,drop_first_5_conv_W_gate,first_5_conv_b_gate,
# drop_first_5_conv_W,first_5_conv_b,drop_first_5_conv_W_context,first_5_conv_b_context,
#
# drop_first_6_conv_W_pre,first_6_conv_b_pre,drop_first_6_conv_W_gate,first_6_conv_b_gate,
# drop_first_6_conv_W,first_6_conv_b,drop_first_6_conv_W_context,first_6_conv_b_context,
first_labels)
first_preds = T.argmax(first_test_distr, axis=1) #batch
all_error = T.mean(T.neq(first_preds, first_labels))
# neg_labels = T.where( labels < 2, 2, labels-1)
# loss2=-T.mean(T.log(1.0/(1.0+layer_LR.p_y_given_x))[T.arange(neg_labels.shape[0]), neg_labels])
# rank loss
# entail_prob_batch = T.nnet.softmax(layer_LR.before_softmax.T)[2] #batch
# entail_ids = elementwise_is_two(labels)
# entail_probs = entail_prob_batch[entail_ids.nonzero()]
# non_entail_probs = entail_prob_batch[(1-entail_ids).nonzero()]
#
# repeat_entail = T.extra_ops.repeat(entail_probs, non_entail_probs.shape[0], axis=0)
# repeat_non_entail = T.extra_ops.repeat(non_entail_probs.dimshuffle('x',0), entail_probs.shape[0], axis=0).flatten()
# loss2 = -T.mean(T.log(entail_probs))#T.mean(T.maximum(0.0, margin-repeat_entail+repeat_non_entail))
# zero_matrix = T.zeros((batch_size, 3))
# filled_zero_matrix = T.set_subtensor(zero_matrix[T.arange(batch_size), labels], 1.0)
# prob_batch_posi = layer_LR.p_y_given_x[filled_zero_matrix.nonzero()]
# prob_batch_nega = layer_LR.p_y_given_x[(1-filled_zero_matrix).nonzero()]
#
# repeat_posi = T.extra_ops.repeat(prob_batch_posi, prob_batch_nega.shape[0], axis=0)
# repeat_nega = T.extra_ops.repeat(prob_batch_nega.dimshuffle('x',0), prob_batch_posi.shape[0], axis=0).flatten()
# loss2 = T.mean(T.maximum(0.0, margin-repeat_posi+repeat_nega))
first_common_para = [first_embeddings]
first_classifier_1_para = first_CNN_1_para + first_classifier_params[0]
first_classifier_2_para = first_CNN_2_para + first_classifier_params[1]
first_classifier_3_para = first_CNN_3_para + first_classifier_params[2]
first_classifier_4_para = first_CNN_4_para + first_classifier_params[3]
first_common_updates = Gradient_Cost_Para(first_loss[0], first_common_para,
learning_rate)
first_classifier_1_updates = Gradient_Cost_Para(first_loss[1],
first_classifier_1_para,
learning_rate)
first_classifier_2_updates = Gradient_Cost_Para(first_loss[2],
first_classifier_2_para,
learning_rate)
first_classifier_3_updates = Gradient_Cost_Para(first_loss[3],
first_classifier_3_para,
learning_rate)
first_classifier_4_updates = Gradient_Cost_Para(first_loss[4],
first_classifier_4_para,
learning_rate)
cost = first_loss[0]
first_updates = first_common_updates + first_classifier_1_updates + first_classifier_2_updates + first_classifier_3_updates + first_classifier_4_updates
updates = first_updates
#train_model = theano.function([sents_id_matrix, sents_mask, labels], cost, updates=updates, on_unused_input='ignore')
train_model = theano.function([
train_flag,
first_sents_ids_l,
first_sents_mask_l,
first_sents_ids_r,
first_sents_mask_r,
first_labels,
],
cost,
updates=updates,
allow_input_downcast=True,
on_unused_input='ignore')
# train_model_pred = theano.function([sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, train_flag,extra,labels,
# second_sents_ids_l,second_sents_mask_l,second_sents_ids_r,second_sents_mask_r,second_labels], [LR_input, labels], allow_input_downcast=True, on_unused_input='ignore')
#
# dev_model = theano.function([sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, train_flag,extra, labels,
# second_sents_ids_l,second_sents_mask_l,second_sents_ids_r,second_sents_mask_r,second_labels], layer_LR.errors(labels), allow_input_downcast=True, on_unused_input='ignore')
test_model = theano.function([
train_flag,
first_sents_ids_l,
first_sents_mask_l,
first_sents_ids_r,
first_sents_mask_r,
first_labels,
], [all_error, first_preds],
allow_input_downcast=True,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
n_train_batches = train_size / batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
n_dev_batches = dev_size / batch_size
dev_batch_start = list(
np.arange(n_dev_batches) * batch_size) + [dev_size - batch_size]
n_test_batches = test_size / batch_size
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
gold_test_rows = test_rows[:(n_test_batches *
batch_size)] + test_rows[-batch_size:]
max_acc_test = 0.0
cost_i = 0.0
first_train_indices = range(train_size)
while epoch < n_epochs:
epoch = epoch + 1
random.Random(200).shuffle(
first_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
first_train_id_batch = first_train_indices[batch_id:batch_id +
batch_size]
cost_i += train_model(1, train_sents_l[first_train_id_batch],
train_masks_l[first_train_id_batch],
train_sents_r[first_train_id_batch],
train_masks_r[first_train_id_batch],
train_labels_store[first_train_id_batch])
#after each 1000 batches, we test the performance of the model on all test data
if iter % int(2000 * (50.0 / batch_size)) == 0:
# if iter%int(200*(50.0 / batch_size))==0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
past_time = time.time()
# if epoch >=3 and iter >= len(train_batch_start)*2.0/3 and iter%500==0:
# print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
# past_time = time.time()
# error_sum=0.0
# for dev_batch_id in dev_batch_start: # for each test batch
# error_i=dev_model(
# dev_sents_l[dev_batch_id:dev_batch_id+batch_size],
# dev_masks_l[dev_batch_id:dev_batch_id+batch_size],
# dev_sents_r[dev_batch_id:dev_batch_id+batch_size],
# dev_masks_r[dev_batch_id:dev_batch_id+batch_size],
# dev_labels_store[dev_batch_id:dev_batch_id+batch_size]
# )
#
# error_sum+=error_i
# dev_accuracy=1.0-error_sum/(len(dev_batch_start))
# if dev_accuracy > max_acc_dev:
# max_acc_dev=dev_accuracy
# print 'current dev_accuracy:', dev_accuracy, '\t\t\t\t\tmax max_acc_dev:', max_acc_dev
#best dev model, do test
# error_sum_1=0.0
# error_sum_2=0.0
error_sum_comb = 0.0
pred_ys = []
for test_batch_id in test_batch_start: # for each test batch
error_comb, pred_ys_batch = test_model(
0,
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_1+=error_1
# error_sum_2+=error_2
error_sum_comb += error_comb
pred_ys += list(pred_ys_batch)
# test_acc_1=1.0-error_sum_1/(len(test_batch_start))
# test_acc_2=1.0-error_sum_2/(len(test_batch_start))
test_acc_comb = 1.0 - error_sum_comb / (len(test_batch_start))
# if test_acc_1 > max_acc_test:
# max_acc_test=test_acc_1
# if test_acc_2 > max_acc_test:
# max_acc_test=test_acc_2
if test_acc_comb > max_acc_test:
max_acc_test = test_acc_comb
# store_model_to_file('/mounts/data/proj/wenpeng/Dataset/StanfordEntailment/model_para_single_model_'+str(max_acc_test), params)
if len(pred_ys) != len(gold_test_rows):
print 'len(pred_ys)!=len(gold_test_rows):', len(
pred_ys), len(gold_test_rows)
else:
test_write = open(
'/mounts/data/proj/wenpeng/Dataset/StanfordEntailment/error_analysis_'
+ str(max_acc_test) + '.txt', 'w')
for i in range(len(pred_ys)):
test_write.write(
str(pred_ys[i]) + '\t' + gold_test_rows[i] +
'\n')
print 'error analysis file written over.'
test_write.close()
print '\t\tcurrent acc:', test_acc_comb, '\t\t\t\t\tmax_acc:', max_acc_test
# else:
# print 'current dev_accuracy:', dev_accuracy, '\t\t\t\t\tmax max_acc_dev:', max_acc_dev
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
return max_acc_test
def evaluate_lenet5(learning_rate=0.05,
n_epochs=700,
il5_emb_size=300,
train_ratio=0.8,
emb_size=300,
batch_size=50,
filter_size=[3, 5],
max_il5_phrase_len=5,
hidden_size=300):
model_options = locals().copy()
print "model options", model_options
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
# srng = T.shared_randomstreams.RandomStreams(rng.randint(seed))
root = '/save/wenpeng/datasets/LORELEI/multi-lingual-emb/'
# en_word2vec=load_fasttext_word2vec_given_file(root+'wiki.en.vec') #
en_word2vec = load_fasttext_word2vec_given_file(
'/save/wenpeng/datasets/word2vec_words_300d.txt', 300)
il5_word2vec = load_fasttext_word2vec_given_file(
root + 'il5_300d_word2vec.txt', 300)
english_vocab = set(en_word2vec.keys())
il5_vocab = set(il5_word2vec.keys())
source_ids, source_masks, target_ids, il5_word2id, english_word2id = load_trainingdata_il5(
root, english_vocab, il5_vocab, max_il5_phrase_len)
# print len(english_vocab)
# print len(english_word2id)
# print set(english_word2id.keys()) - english_vocab
assert set(english_word2id.keys()).issubset(english_vocab)
assert set(il5_word2id.keys()).issubset(il5_vocab)
data_size = len(target_ids)
train_size = int(data_size * train_ratio)
dev_size = data_size - train_size
print 'trainin size: ', train_size, ' dev_size: ', dev_size
# 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(source_ids[:train_size], dtype='int32')
train_masks = np.asarray(source_masks[:train_size],
dtype=theano.config.floatX)
train_target = np.asarray(target_ids[:train_size], dtype='int32')
dev_sents = np.asarray(source_ids[-dev_size:], dtype='int32')
dev_masks = np.asarray(source_masks[-dev_size:],
dtype=theano.config.floatX)
dev_target = np.asarray(target_ids[-dev_size:], dtype='int32')
en_vocab_size = len(english_word2id)
en_rand_values = rng.normal(
0.0, 0.01,
(en_vocab_size, emb_size)) #generate a matrix by Gaussian distribution
en_id2word = {y: x for x, y in english_word2id.iteritems()}
en_rand_values = load_word2vec_to_init(en_rand_values, en_id2word,
en_word2vec)
en_embeddings = theano.shared(
value=np.array(en_rand_values, dtype=theano.config.floatX),
borrow=True
) #wrap up the python variable "rand_values" into theano variable
il5_vocab_size = len(il5_word2id) + 1 # add one zero pad index
il5_rand_values = rng.normal(
0.0, 0.01, (il5_vocab_size,
il5_emb_size)) #generate a matrix by Gaussian distribution
il5_id2word = {y: x for x, y in il5_word2id.iteritems()}
il5_rand_values = load_word2vec_to_init(il5_rand_values, il5_id2word,
il5_word2vec)
il5_embeddings = theano.shared(
value=np.array(il5_rand_values, dtype=theano.config.floatX),
borrow=True
) #wrap up the python variable "rand_values" into theano variable
# source_embeddings=theano.shared(value=np.array(rand_values,dtype=theano.config.floatX), borrow=True) #wrap up the python variable "rand_values" into theano variable
# target_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
batch_ids = T.imatrix() #(batch, maxlen)
batch_masks = T.fmatrix() #(batch, maxlen)
batch_targets = T.ivector()
batch_test_source = T.fmatrix() #(batch, emb_size)
input_batch = il5_embeddings[batch_ids.flatten()].reshape(
(batch_size, max_il5_phrase_len, il5_emb_size)) #(batch, emb_size)
masked_input_batch = T.sum(input_batch * batch_masks.dimshuffle(0, 1, 'x'),
axis=1) #(batch, emb_size)
# masked_input_batch = masked_input_batch / T.sum(batch_masks,axis=1).dimshuffle(0,'x')
target_embs = en_embeddings[batch_targets]
HL_layer_W1, HL_layer_b1 = create_HiddenLayer_para(rng, hidden_size,
hidden_size)
HL_layer_W2, HL_layer_b2 = create_HiddenLayer_para(rng, hidden_size,
hidden_size)
HL_layer_W3, HL_layer_b3 = create_HiddenLayer_para(rng, hidden_size,
hidden_size)
HL_layer_params = [
HL_layer_W3, HL_layer_b3
] #][HL_layer_W1, HL_layer_b1, HL_layer_W2, HL_layer_b2, HL_layer_W3, HL_layer_b3]
#doc, by pos
# HL_layer_1=HiddenLayer(rng, input=masked_input_batch, n_in=il5_emb_size, n_out=emb_size, W=HL_layer_W1, b=HL_layer_b1, activation=T.nnet.relu)
# HL_layer_2=HiddenLayer(rng, input=HL_layer_1.output, n_in=emb_size, n_out=emb_size, W=HL_layer_W2, b=HL_layer_b2, activation=T.nnet.relu)
HL_layer_3 = HiddenLayer(rng,
input=masked_input_batch,
n_in=emb_size,
n_out=emb_size,
W=HL_layer_W3,
b=HL_layer_b3,
activation=T.tanh)
batch_raw_pred = HL_layer_3.output
# batch_pred = batch_raw_pred/T.sqrt(1e-8+T.sum(batch_raw_pred**2, axis=1)).dimshuffle(0,'x')
batch_cosine = T.mean(
cosine_row_wise_twoMatrix(
batch_raw_pred,
target_embs)) #T.mean(T.sum(batch_pred *target_embs, axis=1))
batch_distance = T.mean(
T.sqrt(1e-8 + T.sum((batch_raw_pred - target_embs)**2, axis=1)))
cos_loss = -T.log(1.0 + batch_cosine) #1.0-batch_cosine
loss = -T.log(1.0 / (1.0 + batch_distance))
params = HL_layer_params # put all model parameters together
cost = cos_loss + loss #+1e-4*((HL_layer_W3**2).sum())
updates = Gradient_Cost_Para(cost, params, learning_rate)
'''
testing
'''
#train_model = theano.function([sents_id_matrix, sents_mask, labels], cost, updates=updates, on_unused_input='ignore')
train_model = theano.function([batch_ids, batch_masks, batch_targets],
loss,
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([batch_ids, batch_masks, batch_targets],
batch_cosine,
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 = dev_size / batch_size
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [dev_size - batch_size]
train_indices = range(train_size)
cost_i = 0.0
max_cosine = 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_target[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()
test_loss = 0.0
for test_batch_id in test_batch_start: # for each test batch
test_loss_i = test_model(
dev_sents[test_batch_id:test_batch_id + batch_size],
dev_masks[test_batch_id:test_batch_id + batch_size],
dev_target[test_batch_id:test_batch_id + batch_size])
test_loss += test_loss_i
test_loss /= len(test_batch_start)
if test_loss > max_cosine:
max_cosine = test_loss
print '\t\t\t\t\t\t\t\tcurrent mean_cosin:', test_loss, ' max cosine: ', max_cosine
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=0.01,
n_epochs=10,
L2_weight=0.000001,
extra_size=4,
emb_size=300,
posi_emb_size=50,
batch_size=50,
filter_size=[3, 3],
maxSentLen=50,
hidden_size=300):
model_options = locals().copy()
print "model options", model_options
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
all_sentences_l, all_masks_l, all_sentences_r, all_masks_r, all_labels, word2id = load_SciTailV1_dataset(
maxlen=maxSentLen
) #minlen, include one label, at least one word in the sentence
# test_sents_l, test_masks_l, test_sents_r, test_masks_r, test_labels, word2id =load_ACE05_dataset(maxSentLen, word2id)
train_sents_l = np.asarray(all_sentences_l[0], dtype='int32')
dev_sents_l = np.asarray(all_sentences_l[1], dtype='int32')
test_sents_l = np.asarray(all_sentences_l[2], dtype='int32')
train_masks_l = np.asarray(all_masks_l[0], dtype=theano.config.floatX)
dev_masks_l = np.asarray(all_masks_l[1], dtype=theano.config.floatX)
test_masks_l = np.asarray(all_masks_l[2], dtype=theano.config.floatX)
train_sents_r = np.asarray(all_sentences_r[0], dtype='int32')
dev_sents_r = np.asarray(all_sentences_r[1], dtype='int32')
test_sents_r = np.asarray(all_sentences_r[2], dtype='int32')
train_masks_r = np.asarray(all_masks_r[0], dtype=theano.config.floatX)
dev_masks_r = np.asarray(all_masks_r[1], dtype=theano.config.floatX)
test_masks_r = np.asarray(all_masks_r[2], dtype=theano.config.floatX)
train_labels_store = np.asarray(all_labels[0], dtype='int32')
dev_labels_store = np.asarray(all_labels[1], dtype='int32')
test_labels_store = np.asarray(all_labels[2], dtype='int32')
train_size = len(train_labels_store)
dev_size = len(dev_labels_store)
test_size = len(test_labels_store)
print 'train size: ', train_size, ' dev size: ', dev_size, ' test size: ', test_size
vocab_size = len(word2id) + 1
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
#here, we leave code for loading word2vec to initialize words
rand_values[0] = np.array(np.zeros(emb_size), dtype=theano.config.floatX)
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_word2vec()
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
init_embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
posi_rand_values = rng.normal(
0.0, 0.01,
(maxSentLen,
posi_emb_size)) #generate a matrix by Gaussian distribution
posi_embeddings = theano.shared(
value=np.array(posi_rand_values, dtype=theano.config.floatX),
borrow=True
) #wrap up the python variable "rand_values" into theano variable
#now, start to build the input form of the model
sents_ids_l = T.imatrix()
sents_mask_l = T.fmatrix()
sents_ids_r = T.imatrix()
sents_mask_r = T.fmatrix()
labels = T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
def embed_input(emb_matrix, sent_ids):
return emb_matrix[sent_ids.flatten()].reshape(
(batch_size, maxSentLen, emb_size)).dimshuffle(0, 2, 1)
embed_input_l = embed_input(
init_embeddings, sents_ids_l
) #embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
embed_input_r = embed_input(
init_embeddings, sents_ids_r
) #embeddings[sents_ids_r.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1)
'''create_AttentiveConv_params '''
conv_W, conv_b = create_conv_para(rng,
filter_shape=(hidden_size, 1, emb_size,
filter_size[0]))
conv_W_posi, conv_b_posi = create_conv_para(
rng,
filter_shape=(hidden_size, 1, emb_size + posi_emb_size,
filter_size[0]))
conv_W_context, conv_b_context = create_conv_para(
rng, filter_shape=(hidden_size, 1, emb_size, 1))
NN_para = [conv_W, conv_b, conv_W_posi, conv_b_posi, conv_W_context]
'''
attentive convolution function
'''
attentive_conv_layer = Conv_for_Pair(
rng,
origin_input_tensor3=embed_input_l,
origin_input_tensor3_r=embed_input_r,
input_tensor3=embed_input_l,
input_tensor3_r=embed_input_r,
mask_matrix=sents_mask_l,
mask_matrix_r=sents_mask_r,
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size, 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size, 1, emb_size, 1),
W=conv_W,
b=conv_b,
W_posi=conv_W_posi,
b_posi=conv_b_posi,
W_context=conv_W_context,
b_context=conv_b_context,
posi_emb_matrix=posi_embeddings,
posi_emb_size=posi_emb_size)
attentive_sent_embeddings_l = attentive_conv_layer.attentive_maxpool_vec_l
attentive_sent_embeddings_r = attentive_conv_layer.attentive_maxpool_vec_r
sent_embeddings_l = attentive_conv_layer.maxpool_vec_l
sent_embeddings_r = attentive_conv_layer.maxpool_vec_r
"form input to LR classifier"
LR_input = T.concatenate([
sent_embeddings_l, sent_embeddings_r,
sent_embeddings_l * sent_embeddings_r, attentive_sent_embeddings_l,
attentive_sent_embeddings_r,
attentive_sent_embeddings_l * attentive_sent_embeddings_r
],
axis=1)
LR_input_size = 6 * hidden_size
U_a = create_ensemble_para(
rng, 2, LR_input_size) # the weight matrix hidden_size*2
LR_b = theano.shared(value=np.zeros((2, ), dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
LR_para = [U_a, LR_b]
layer_LR = LogisticRegression(
rng, input=LR_input, n_in=LR_input_size, n_out=2, W=U_a, b=LR_b
) #basically it is a multiplication between weight matrix and input feature vector
loss = layer_LR.negative_log_likelihood(
labels
) #for classification task, we usually used negative log likelihood as loss, the lower the better.
params = [init_embeddings, posi_embeddings] + NN_para + LR_para
# L2_reg = (init_embeddings**2).sum()+(conv_W**2).sum()+(conv_W_context**2).sum()+(U_a**2).sum()
cost = loss #+L2_weight*L2_reg
updates = Gradient_Cost_Para(cost, params, learning_rate)
train_model = theano.function(
[sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels],
cost,
updates=updates,
allow_input_downcast=True,
on_unused_input='ignore')
dev_model = theano.function(
[sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels],
layer_LR.errors(labels),
allow_input_downcast=True,
on_unused_input='ignore')
test_model = theano.function(
[sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels],
layer_LR.errors(labels),
allow_input_downcast=True,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
n_train_batches = train_size / batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
n_dev_batches = dev_size / batch_size
dev_batch_start = list(
np.arange(n_dev_batches) * batch_size) + [dev_size - batch_size]
n_test_batches = test_size / batch_size
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
max_acc_dev = 0.0
max_acc_test = 0.0
max_f1 = 0.0
cost_i = 0.0
train_indices = range(train_size)
while epoch < n_epochs:
epoch = epoch + 1
random.Random(100).shuffle(
train_indices
) #shuffle training set for each new epoch, is supposed to promote performance, but not garrenteed
iter_accu = 0
for batch_id in train_batch_start: #for each batch
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
train_id_batch = train_indices[batch_id:batch_id + batch_size]
cost_i += train_model(train_sents_l[train_id_batch],
train_masks_l[train_id_batch],
train_sents_r[train_id_batch],
train_masks_r[train_id_batch],
train_labels_store[train_id_batch])
#after each 1000 batches, we test the performance of the model on all test data
if iter % 100 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
past_time = time.time()
dev_error_sum = 0.0
for dev_batch_id in dev_batch_start: # for each test batch
dev_error_i = dev_model(
dev_sents_l[dev_batch_id:dev_batch_id + batch_size],
dev_masks_l[dev_batch_id:dev_batch_id + batch_size],
dev_sents_r[dev_batch_id:dev_batch_id + batch_size],
dev_masks_r[dev_batch_id:dev_batch_id + batch_size],
dev_labels_store[dev_batch_id:dev_batch_id +
batch_size])
dev_error_sum += dev_error_i
dev_acc = 1.0 - dev_error_sum / (len(dev_batch_start))
if dev_acc > max_acc_dev:
max_acc_dev = dev_acc
print '\tcurrent dev_acc:', dev_acc, ' ; ', '\tmax_dev_acc:', max_acc_dev
error_sum = 0.0
for idd, test_batch_id in enumerate(
test_batch_start): # for each test batch
error_i = test_model(
test_sents_l[test_batch_id:test_batch_id +
batch_size],
test_masks_l[test_batch_id:test_batch_id +
batch_size],
test_sents_r[test_batch_id:test_batch_id +
batch_size],
test_masks_r[test_batch_id:test_batch_id +
batch_size],
test_labels_store[test_batch_id:test_batch_id +
batch_size])
error_sum += error_i
test_acc = 1.0 - error_sum / (len(test_batch_start))
if test_acc > max_acc_test:
max_acc_test = test_acc
store_model_to_file(
'/home/wenpeng/workspace/SciTail/src/model_para_' +
str(max_acc_test), params)
print '\t\tcurrent acc:', test_acc, ' ; ', '\t\tmax_acc:', max_acc_test
else:
print '\tcurrent dev_acc:', dev_acc, ' ; ', '\tmax_dev_acc:', max_acc_dev
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
return max_acc_test
def evaluate_lenet5(learning_rate=0.02,
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=0.02,
n_epochs=4,
L2_weight=0.0000001,
extra_size=4,
emb_size=300,
batch_size=50,
filter_size=[3, 5],
maxSentLen=60,
hidden_size=[300, 300]):
model_options = locals().copy()
print "model options", model_options
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
all_sentences_l, all_masks_l, all_sentences_r, all_masks_r, all_labels, word2id = load_SNLI_dataset(
maxlen=maxSentLen
) #minlen, include one label, at least one word in the sentence
test_sents_l, test_masks_l, test_sents_r, test_masks_r, test_labels, word2id = load_NYT_dataset(
maxSentLen, word2id)
train_sents_l = np.asarray(all_sentences_l[0], dtype='int32')
dev_sents_l = np.asarray(all_sentences_l[1], dtype='int32')
test_sents_l = np.asarray(test_sents_l, dtype='int32')
train_masks_l = np.asarray(all_masks_l[0], dtype=theano.config.floatX)
dev_masks_l = np.asarray(all_masks_l[1], dtype=theano.config.floatX)
test_masks_l = np.asarray(test_masks_l, dtype=theano.config.floatX)
train_sents_r = np.asarray(all_sentences_r[0], dtype='int32')
dev_sents_r = np.asarray(all_sentences_r[1], dtype='int32')
test_sents_r = np.asarray(test_sents_r, dtype='int32')
train_masks_r = np.asarray(all_masks_r[0], dtype=theano.config.floatX)
dev_masks_r = np.asarray(all_masks_r[1], dtype=theano.config.floatX)
test_masks_r = np.asarray(test_masks_r, dtype=theano.config.floatX)
train_labels_store = np.asarray(all_labels[0], dtype='int32')
dev_labels_store = np.asarray(all_labels[1], dtype='int32')
test_labels_store = np.asarray(test_labels, dtype='int32')
train_size = len(train_labels_store)
dev_size = len(dev_labels_store)
test_size = len(test_labels_store)
print 'train size: ', train_size, ' dev size: ', dev_size, ' test size: ', test_size
vocab_size = len(word2id) + 1
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
#here, we leave code for loading word2vec to initialize words
rand_values[0] = np.array(np.zeros(emb_size), dtype=theano.config.floatX)
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_word2vec()
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
init_embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
#now, start to build the input form of the model
sents_ids_l = T.imatrix()
sents_mask_l = T.fmatrix()
sents_ids_r = T.imatrix()
sents_mask_r = T.fmatrix()
labels = T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
def embed_input(emb_matrix, sent_ids):
return emb_matrix[sent_ids.flatten()].reshape(
(batch_size, maxSentLen, emb_size)).dimshuffle(0, 2, 1)
embed_input_l = embed_input(
init_embeddings, sents_ids_l
) #embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
embed_input_r = embed_input(
init_embeddings, sents_ids_r
) #embeddings[sents_ids_r.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1)
'''create_AttentiveConv_params '''
conv_W, conv_b = create_conv_para(rng,
filter_shape=(hidden_size[1], 1,
hidden_size[0],
filter_size[0]))
conv_W_context, conv_b_context = create_conv_para(
rng, filter_shape=(hidden_size[1], 1, hidden_size[0], 1))
conv_W_2, conv_b_2 = create_conv_para(rng,
filter_shape=(hidden_size[1], 1,
hidden_size[0],
filter_size[1]))
conv_W_context_2, conv_b_context_2 = create_conv_para(
rng, filter_shape=(hidden_size[1], 1, hidden_size[0], 1))
NN_para = [
conv_W, conv_b, conv_W_context, conv_W_2, conv_b_2, conv_W_context_2
]
'''
attentive convolution function
'''
attentive_conv_layer = Conv_for_Pair(
rng,
origin_input_tensor3=embed_input_l,
origin_input_tensor3_r=embed_input_r,
input_tensor3=embed_input_l,
input_tensor3_r=embed_input_r,
mask_matrix=sents_mask_l,
mask_matrix_r=sents_mask_r,
image_shape=(batch_size, 1, hidden_size[0], maxSentLen),
image_shape_r=(batch_size, 1, hidden_size[0], maxSentLen),
filter_shape=(hidden_size[1], 1, hidden_size[0], filter_size[0]),
filter_shape_context=(hidden_size[1], 1, hidden_size[0], 1),
W=conv_W,
b=conv_b,
W_context=conv_W_context,
b_context=conv_b_context)
attentive_sent_embeddings_l = attentive_conv_layer.attentive_maxpool_vec_l
attentive_sent_embeddings_r = attentive_conv_layer.attentive_maxpool_vec_r
attentive_conv_layer_2 = Conv_for_Pair(
rng,
origin_input_tensor3=embed_input_l,
origin_input_tensor3_r=embed_input_r,
input_tensor3=embed_input_l,
input_tensor3_r=embed_input_r,
mask_matrix=sents_mask_l,
mask_matrix_r=sents_mask_r,
image_shape=(batch_size, 1, hidden_size[0], maxSentLen),
image_shape_r=(batch_size, 1, hidden_size[0], maxSentLen),
filter_shape=(hidden_size[1], 1, hidden_size[0], filter_size[1]),
filter_shape_context=(hidden_size[1], 1, hidden_size[0], 1),
W=conv_W_2,
b=conv_b_2,
W_context=conv_W_context_2,
b_context=conv_b_context_2)
attentive_sent_embeddings_l_2 = attentive_conv_layer_2.attentive_maxpool_vec_l
attentive_sent_embeddings_r_2 = attentive_conv_layer_2.attentive_maxpool_vec_r
#form input to HL layers
HL_layer_1_input = T.concatenate([
attentive_sent_embeddings_l, attentive_sent_embeddings_r,
attentive_sent_embeddings_l * attentive_sent_embeddings_r,
attentive_sent_embeddings_l_2, attentive_sent_embeddings_r_2,
attentive_sent_embeddings_l_2 * attentive_sent_embeddings_r_2
],
axis=1)
HL_layer_1_input_size = 6 * hidden_size[1]
HL_layer_1 = HiddenLayer(rng,
input=HL_layer_1_input,
n_in=HL_layer_1_input_size,
n_out=hidden_size[1],
activation=T.nnet.relu)
HL_layer_2 = HiddenLayer(rng,
input=HL_layer_1.output,
n_in=hidden_size[1],
n_out=hidden_size[1],
activation=T.nnet.relu)
# LR_input_size=HL_layer_1_input_size+2*hidden_size[0]
"form input to LR classifier"
LR_input = T.tanh(
T.concatenate([HL_layer_1_input, HL_layer_1.output, HL_layer_2.output],
axis=1))
LR_input_size = HL_layer_1_input_size + 2 * hidden_size[1]
U_a = create_ensemble_para(
rng, 3, LR_input_size) # the weight matrix hidden_size*2
LR_b = theano.shared(value=np.zeros((3, ), dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
LR_para = [U_a, LR_b]
layer_LR = LogisticRegression(
rng, input=LR_input, n_in=LR_input_size, n_out=3, W=U_a, b=LR_b
) #basically it is a multiplication between weight matrix and input feature vector
loss = layer_LR.negative_log_likelihood(
labels
) #for classification task, we usually used negative log likelihood as loss, the lower the better.
'''
testing
'''
test_preds = T.argmax(layer_LR.p_y_given_x, axis=1)
transfered_preds = T.eq(test_preds, 2)
test_error = T.mean(T.neq(transfered_preds, labels))
params = [init_embeddings
] + NN_para + HL_layer_1.params + HL_layer_2.params + LR_para
cost = loss
updates = Gradient_Cost_Para(cost, params, learning_rate)
train_model = theano.function(
[sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels],
cost,
updates=updates,
allow_input_downcast=True,
on_unused_input='ignore')
test_model = theano.function(
[sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels],
[test_error, transfered_preds],
allow_input_downcast=True,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
n_train_batches = train_size / batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
n_dev_batches = dev_size / batch_size
dev_batch_start = list(
np.arange(n_dev_batches) * batch_size) + [dev_size - batch_size]
n_test_batches = test_size / batch_size
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
max_acc_dev = 0.0
max_acc_test = 0.0
max_f1 = 0.0
cost_i = 0.0
train_indices = range(train_size)
while epoch < n_epochs:
epoch = epoch + 1
random.Random(100).shuffle(
train_indices
) #shuffle training set for each new epoch, is supposed to promote performance, but not garrenteed
iter_accu = 0
for batch_id in train_batch_start: #for each batch
# iter means how many batches have been run, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
train_id_batch = train_indices[batch_id:batch_id + batch_size]
cost_i += train_model(train_sents_l[train_id_batch],
train_masks_l[train_id_batch],
train_sents_r[train_id_batch],
train_masks_r[train_id_batch],
train_labels_store[train_id_batch])
#after each 1000 batches, we test the performance of the model on all test data
if iter % 100 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
past_time = time.time()
pred_labels = []
gold_labels = []
error_sum = 0.0
for idd, test_batch_id in enumerate(
test_batch_start): # for each test batch
error_i, pred_labels_i = test_model(
test_sents_l[test_batch_id:test_batch_id + batch_size],
test_masks_l[test_batch_id:test_batch_id + batch_size],
test_sents_r[test_batch_id:test_batch_id + batch_size],
test_masks_r[test_batch_id:test_batch_id + batch_size],
test_labels_store[test_batch_id:test_batch_id +
batch_size])
error_sum += error_i
pred_labels += list(pred_labels_i)
gold_labels += list(
test_labels_store[test_batch_id:test_batch_id +
batch_size])
test_acc = 1.0 - error_sum / (len(test_batch_start))
test_f1 = f1_score_2_binary_list(
gold_labels, pred_labels) #, average='binary')
if test_acc > max_acc_test:
max_acc_test = test_acc
if test_f1 > max_f1:
max_f1 = test_f1
# store_model_to_file('/mounts/data/proj/wenpeng/Dataset/StanfordEntailment/model_para_five_copies_'+str(max_acc_test), params)
print '\t\tcurrent acc:', test_acc, ' ; ', '\t\tmax_acc:', max_acc_test, '\t\t test_f1:', test_f1, '\t\tmax F1:', max_f1
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
return max_acc_test
def evaluate_lenet5(learning_rate=0.02,
n_epochs=4,
L2_weight=1e-5,
extra_size=4,
emb_size=300,
batch_size=100,
filter_size=[3, 3],
maxSentLen=40,
hidden_size=[300, 300],
max_term_len=4,
p_mode='conc'):
model_options = locals().copy()
print "model options", model_options
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
all_sentences_l, all_masks_l, all_sentences_r, all_masks_r, all_word1, all_word2, all_word1_mask, all_word2_mask, all_labels, all_extra, word2id = load_wordnet_hyper_vs_all_with_words(
maxlen=maxSentLen, wordlen=max_term_len
) #minlen, include one label, at least one word in the sentence
# test_sents_l, test_masks_l, test_sents_r, test_masks_r, test_labels, word2id =load_ACE05_dataset(maxSentLen, word2id)
test_sents_l, test_masks_l, test_sents_r, test_masks_r, test_word1, test_word2, test_word1_mask, test_word2_mask, test_labels, test_extra, word2id = load_task_hyper_vs_all_with_words(
LenciBenotto_file, maxSentLen, word2id, wordlen=max_term_len)
store_word2id(word2id, root_dic + 'LenciBenotto_word2id.pkl')
# exit(0)
total_size = len(all_sentences_l)
hold_test_size = 10000
train_size = total_size - hold_test_size
train_sents_l = np.asarray(all_sentences_l[:train_size], dtype='int32')
# dev_sents_l=np.asarray(all_sentences_l[1], dtype='int32')
# test_sents_l=np.asarray(all_sentences_l[-test_size:], dtype='int32')
test_sents_l = np.asarray(test_sents_l, dtype='int32')
train_masks_l = np.asarray(all_masks_l[:train_size],
dtype=theano.config.floatX)
# dev_masks_l=np.asarray(all_masks_l[1], dtype=theano.config.floatX)
# test_masks_l=np.asarray(all_masks_l[-test_size:], dtype=theano.config.floatX)
test_masks_l = np.asarray(test_masks_l, dtype=theano.config.floatX)
train_sents_r = np.asarray(all_sentences_r[:train_size], dtype='int32')
# dev_sents_r=np.asarray(all_sentences_r[1] , dtype='int32')
# test_sents_r=np.asarray(all_sentences_r[-test_size:], dtype='int32')
test_sents_r = np.asarray(test_sents_r, dtype='int32')
train_masks_r = np.asarray(all_masks_r[:train_size],
dtype=theano.config.floatX)
# dev_masks_r=np.asarray(all_masks_r[1], dtype=theano.config.floatX)
# test_masks_r=np.asarray(all_masks_r[-test_size:], dtype=theano.config.floatX)
test_masks_r = np.asarray(test_masks_r, dtype=theano.config.floatX)
train_word1 = np.asarray(all_word1[:train_size], dtype='int32')
train_word2 = np.asarray(all_word2[:train_size], dtype='int32')
test_word1 = np.asarray(test_word1, dtype='int32')
test_word2 = np.asarray(test_word2, dtype='int32')
train_word1_mask = np.asarray(all_word1_mask[:train_size],
dtype=theano.config.floatX)
train_word2_mask = np.asarray(all_word2_mask[:train_size],
dtype=theano.config.floatX)
test_word1_mask = np.asarray(test_word1_mask, dtype=theano.config.floatX)
test_word2_mask = np.asarray(test_word2_mask, dtype=theano.config.floatX)
train_labels_store = np.asarray(all_labels[:train_size], dtype='int32')
# dev_labels_store=np.asarray(all_labels[1], dtype='int32')
# test_labels_store=np.asarray(all_labels[-test_size:], dtype='int32')
test_labels_store = np.asarray(test_labels, dtype='int32')
train_extra = np.asarray(all_extra[:train_size],
dtype=theano.config.floatX)
test_extra = np.asarray(test_extra, dtype=theano.config.floatX)
# train_size=len(train_labels_store)
# dev_size=len(dev_labels_store)
test_size = len(test_labels_store)
print 'train size: ', train_size, ' test size: ', test_size
vocab_size = len(word2id) + 1
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
#here, we leave code for loading word2vec to initialize words
rand_values[0] = np.array(np.zeros(emb_size), dtype=theano.config.floatX)
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_word2vec()
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
init_embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
store_model_to_file(root_dic + 'LenciBenotto_best_para_init_embeddings',
[init_embeddings])
#now, start to build the input form of the model
sents_ids_l = T.imatrix()
sents_mask_l = T.fmatrix()
sents_ids_r = T.imatrix()
sents_mask_r = T.fmatrix()
word1_ids = T.imatrix()
word2_ids = T.imatrix()
word1_mask = T.fmatrix()
word2_mask = T.fmatrix()
extra = T.fvector()
labels = T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
def embed_input(emb_matrix, sent_ids):
return emb_matrix[sent_ids.flatten()].reshape(
(batch_size, maxSentLen, emb_size)).dimshuffle(0, 2, 1)
embed_input_l = embed_input(
init_embeddings, sents_ids_l
) #embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
embed_input_r = embed_input(
init_embeddings, sents_ids_r
) #embeddings[sents_ids_r.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1)
embed_word1 = init_embeddings[word1_ids.flatten()].reshape(
(batch_size, word1_ids.shape[1], emb_size))
embed_word2 = init_embeddings[word2_ids.flatten()].reshape(
(batch_size, word2_ids.shape[1], emb_size))
word1_embedding = T.sum(embed_word1 * word1_mask.dimshuffle(0, 1, 'x'),
axis=1)
word2_embedding = T.sum(embed_word2 * word2_mask.dimshuffle(0, 1, 'x'),
axis=1)
'''create_AttentiveConv_params '''
conv_W, conv_b = create_conv_para(rng,
filter_shape=(hidden_size[1], 1,
emb_size, filter_size[0]))
conv_W_context, conv_b_context = create_conv_para(
rng, filter_shape=(hidden_size[1], 1, emb_size, 1))
NN_para = [conv_W, conv_b, conv_W_context]
'''
attentive convolution function
'''
term_vs_term_layer = Conv_for_Pair(
rng,
origin_input_tensor3=embed_word1.dimshuffle(0, 2, 1),
origin_input_tensor3_r=embed_word2.dimshuffle(0, 2, 1),
input_tensor3=embed_word1.dimshuffle(0, 2, 1),
input_tensor3_r=embed_word2.dimshuffle(0, 2, 1),
mask_matrix=word1_mask,
mask_matrix_r=word2_mask,
image_shape=(batch_size, 1, emb_size, max_term_len),
image_shape_r=(batch_size, 1, emb_size, max_term_len),
filter_shape=(hidden_size[1], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[1], 1, emb_size, 1),
W=conv_W,
b=conv_b,
W_context=conv_W_context,
b_context=conv_b_context)
tt_embeddings_l = term_vs_term_layer.attentive_maxpool_vec_l
tt_embeddings_r = term_vs_term_layer.attentive_maxpool_vec_r
p_ww = T.concatenate([
tt_embeddings_l, tt_embeddings_r, tt_embeddings_l * tt_embeddings_r,
tt_embeddings_l - tt_embeddings_r
],
axis=1)
term_vs_def_layer = Conv_for_Pair(
rng,
origin_input_tensor3=embed_word1.dimshuffle(0, 2, 1),
origin_input_tensor3_r=embed_input_r,
input_tensor3=embed_word1.dimshuffle(0, 2, 1),
input_tensor3_r=embed_input_r,
mask_matrix=word1_mask,
mask_matrix_r=sents_mask_r,
image_shape=(batch_size, 1, emb_size, max_term_len),
image_shape_r=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[1], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[1], 1, emb_size, 1),
W=conv_W,
b=conv_b,
W_context=conv_W_context,
b_context=conv_b_context)
td_embeddings_l = term_vs_def_layer.attentive_maxpool_vec_l
td_embeddings_r = term_vs_def_layer.attentive_maxpool_vec_r
p_wd = T.concatenate([
td_embeddings_l, td_embeddings_r, td_embeddings_l * td_embeddings_r,
td_embeddings_l - td_embeddings_r
],
axis=1)
def_vs_term_layer = Conv_for_Pair(
rng,
origin_input_tensor3=embed_input_l,
origin_input_tensor3_r=embed_word2.dimshuffle(0, 2, 1),
input_tensor3=embed_input_l,
input_tensor3_r=embed_word2.dimshuffle(0, 2, 1),
mask_matrix=sents_mask_l,
mask_matrix_r=word2_mask,
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r=(batch_size, 1, emb_size, max_term_len),
filter_shape=(hidden_size[1], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[1], 1, emb_size, 1),
W=conv_W,
b=conv_b,
W_context=conv_W_context,
b_context=conv_b_context)
dt_embeddings_l = def_vs_term_layer.attentive_maxpool_vec_l
dt_embeddings_r = def_vs_term_layer.attentive_maxpool_vec_r
p_dw = T.concatenate([
dt_embeddings_l, dt_embeddings_r, dt_embeddings_l * dt_embeddings_r,
dt_embeddings_l - dt_embeddings_r
],
axis=1)
def_vs_def_layer = Conv_for_Pair(
rng,
origin_input_tensor3=embed_input_l,
origin_input_tensor3_r=embed_input_r,
input_tensor3=embed_input_l,
input_tensor3_r=embed_input_r,
mask_matrix=sents_mask_l,
mask_matrix_r=sents_mask_r,
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[1], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[1], 1, emb_size, 1),
W=conv_W,
b=conv_b,
W_context=conv_W_context,
b_context=conv_b_context)
dd_embeddings_l = def_vs_def_layer.attentive_maxpool_vec_l
dd_embeddings_r = def_vs_def_layer.attentive_maxpool_vec_r
p_dd = T.concatenate([
dd_embeddings_l, dd_embeddings_r, dd_embeddings_l * dd_embeddings_r,
dd_embeddings_l - dd_embeddings_r
],
axis=1)
if p_mode == 'conc':
p = T.concatenate([p_ww, p_wd, p_dw, p_dd], axis=1)
p_len = 4 * 4 * hidden_size[1]
else:
p = T.max(T.concatenate([
p_ww.dimshuffle('x', 0, 1),
p_wd.dimshuffle('x', 0, 1),
p_dw.dimshuffle('x', 0, 1),
p_dd.dimshuffle('x', 0, 1)
],
axis=0),
axis=0)
p_len = 4 * hidden_size[1]
# HL_input = T.concatenate([p,cosine_matrix1_matrix2_rowwise(word1_embedding,word2_embedding).dimshuffle(0,'x'),extra.dimshuffle(0,'x')],axis=1)
# HL_input_size=p_len+1+1
#
# HL_layer_1=HiddenLayer(rng, input=HL_input, n_in=HL_input_size, n_out=hidden_size[1], activation=T.tanh)
"form input to LR classifier"
LR_input = T.concatenate([
p,
cosine_matrix1_matrix2_rowwise(word1_embedding,
word2_embedding).dimshuffle(0, 'x'),
extra.dimshuffle(0, 'x')
],
axis=1)
LR_input_size = p_len + 1 + 1
# LR_input = HL_layer_1.output
# LR_input_size = hidden_size[1]
U_a = create_ensemble_para(
rng, 2, LR_input_size) # the weight matrix hidden_size*2
LR_b = theano.shared(value=np.zeros((2, ), dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
LR_para = [U_a, LR_b]
layer_LR = LogisticRegression(
rng,
input=LR_input,
n_in=LR_input_size,
n_out=2,
W=U_a,
b=LR_b,
bias=0.25
) #basically it is a multiplication between weight matrix and input feature vector
loss = layer_LR.negative_log_likelihood(
labels
) #for classification task, we usually used negative log likelihood as loss, the lower the better.
# L2_reg = (conv_W**2).sum()+(conv_W_context**2).sum()+(U_a**2).sum()
params = NN_para + LR_para #[init_embeddings]
cost = loss #+L2_weight*L2_reg
updates = Gradient_Cost_Para(cost, params, learning_rate)
train_model = theano.function([
sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, word1_ids,
word2_ids, word1_mask, word2_mask, extra, labels
],
cost,
updates=updates,
allow_input_downcast=True,
on_unused_input='ignore')
test_model = theano.function([
sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, word1_ids,
word2_ids, word1_mask, word2_mask, extra, labels
], [layer_LR.errors(labels), layer_LR.y_pred, layer_LR.prop_for_posi],
allow_input_downcast=True,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
n_train_batches = train_size / batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
# n_dev_batches=dev_size/batch_size
# dev_batch_start=list(np.arange(n_dev_batches)*batch_size)+[dev_size-batch_size]
n_test_batches = test_size / batch_size
n_test_remain = test_size % batch_size
if n_test_remain != 0:
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
else:
test_batch_start = list(np.arange(n_test_batches) * batch_size)
# max_acc_dev=0.0
max_ap_test = 0.0
max_ap_topk_test = 0.0
max_f1 = 0.0
cost_i = 0.0
train_indices = range(train_size)
while epoch < n_epochs:
epoch = epoch + 1
random.Random(100).shuffle(
train_indices
) #shuffle training set for each new epoch, is supposed to promote performance, but not garrenteed
iter_accu = 0
for batch_id in train_batch_start: #for each batch
# iter means how many batches have been run, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
train_id_batch = train_indices[batch_id:batch_id + batch_size]
cost_i += train_model(
train_sents_l[train_id_batch], train_masks_l[train_id_batch],
train_sents_r[train_id_batch], train_masks_r[train_id_batch],
train_word1[train_id_batch], train_word2[train_id_batch],
train_word1_mask[train_id_batch],
train_word2_mask[train_id_batch], train_extra[train_id_batch],
train_labels_store[train_id_batch])
#after each 1000 batches, we test the performance of the model on all test data
if iter % 100 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
past_time = time.time()
pred_labels = []
probs = []
gold_labels = []
error_sum = 0.0
for idd, test_batch_id in enumerate(
test_batch_start): # for each test batch
error_i, pred_i, prob_i = test_model(
test_sents_l[test_batch_id:test_batch_id + batch_size],
test_masks_l[test_batch_id:test_batch_id + batch_size],
test_sents_r[test_batch_id:test_batch_id + batch_size],
test_masks_r[test_batch_id:test_batch_id + batch_size],
test_word1[test_batch_id:test_batch_id + batch_size],
test_word2[test_batch_id:test_batch_id + batch_size],
test_word1_mask[test_batch_id:test_batch_id +
batch_size],
test_word2_mask[test_batch_id:test_batch_id +
batch_size],
test_extra[test_batch_id:test_batch_id + batch_size],
test_labels_store[test_batch_id:test_batch_id +
batch_size])
error_sum += error_i
pred_labels += list(pred_i)
probs += list(prob_i)
if n_test_remain != 0:
probs = probs[:(len(test_batch_start) - 1) *
batch_size] + probs[-n_test_remain:]
assert len(test_labels) == len(probs)
# test_acc=1.0-error_sum/(len(test_batch_start))
test_ap = apk(test_labels, probs, k=len(test_labels))
test_ap_top100 = apk(test_labels, probs, k=100)
if test_ap > max_ap_test:
max_ap_test = test_ap
store_model_to_file(
root_dic + 'LenciBenotto_best_para_' +
str(max_ap_test), params)
if test_ap_top100 > max_ap_topk_test:
max_ap_topk_test = test_ap_top100
print '\t\tcurrent ap:', test_ap, ' ; ', '\t\tmax_ap: ', max_ap_test, 'ap@100: ', test_ap_top100, '\tmax_ap@100:', max_ap_topk_test
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.01,
n_epochs=100,
emb_size=40,
batch_size=50,
filter_size=[3, 5],
maxSentLen=100,
hidden_size=[300, 300]):
model_options = locals().copy()
print "model options", model_options
emb_root = '/save/wenpeng/datasets/LORELEI/multi-lingual-emb/'
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
srng = T.shared_randomstreams.RandomStreams(rng.randint(seed))
all_sentences, all_masks, all_labels, word2id = load_reliefweb_il5_12_multilabel(
maxlen=maxSentLen
) #minlen, include one label, at least one word in the sentence
train_sents = np.asarray(all_sentences[0], dtype='int32')
train_masks = np.asarray(all_masks[0], dtype=theano.config.floatX)
train_labels = np.asarray(all_labels[0], dtype='int32')
train_size = len(train_labels)
# dev_sents=all_sentences[1]
# dev_masks=all_masks[1]
# dev_labels=all_labels[1]
# dev_size=len(dev_labels)
test_sents = np.asarray(all_sentences[2], dtype='int32')
test_masks = np.asarray(all_masks[2], dtype=theano.config.floatX)
test_labels = np.asarray(all_labels[2], dtype='int32')
test_size = len(test_labels)
vocab_size = len(word2id) + 1 # add one zero pad index
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
#here, we leave code for loading word2vec to initialize words
rand_values[0] = np.array(np.zeros(emb_size), dtype=theano.config.floatX)
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_fasttext_multiple_word2vec_given_file([
emb_root + 'IL5-cca-wiki-lorelei-d40.eng.vec',
emb_root + 'IL5-cca-wiki-lorelei-d40.IL5.vec'
], 40)
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
#now, start to build the input form of the model
sents_id_matrix = T.imatrix('sents_id_matrix')
sents_mask = T.fmatrix('sents_mask')
labels = T.imatrix('labels')
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
common_input = embeddings[sents_id_matrix.flatten()].reshape(
(batch_size, maxSentLen, emb_size)).dimshuffle(
0, 2, 1) #the input format can be adapted into CNN or GRU or LSTM
bow_emb = T.sum(common_input * sents_mask.dimshuffle(0, 'x', 1), axis=2)
# conv_W, conv_b=create_conv_para(rng, filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]))
# conv_W2, conv_b2=create_conv_para(rng, filter_shape=(hidden_size[0], 1, emb_size, filter_size[1]))
# NN_para = [conv_W, conv_b, conv_W2, conv_b2]
# conv_model = Conv_with_Mask(rng, input_tensor3=common_input,
# mask_matrix = sents_mask,
# image_shape=(batch_size, 1, emb_size, maxSentLen),
# filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]), W=conv_W, b=conv_b) #mutiple mask with the conv_out to set the features by UNK to zero
# sent_embeddings=conv_model.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
#
# conv_model2 = Conv_with_Mask(rng, input_tensor3=common_input,
# mask_matrix = sents_mask,
# image_shape=(batch_size, 1, emb_size, maxSentLen),
# filter_shape=(hidden_size[0], 1, emb_size, filter_size[1]), W=conv_W2, b=conv_b2) #mutiple mask with the conv_out to set the features by UNK to zero
# sent_embeddings2=conv_model2.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
#
# LR_input = T.concatenate([sent_embeddings,sent_embeddings2, bow_emb], axis=1)
# LR_input_size = hidden_size[0]*2+emb_size
# #classification layer, it is just mapping from a feature vector of size "hidden_size" to a vector of only two values: positive, negative
# U_a = create_ensemble_para(rng, 8, LR_input_size) # the weight matrix hidden_size*2
# LR_b = theano.shared(value=np.zeros((8,),dtype=theano.config.floatX),name='LR_b', borrow=True) #bias for each target class
# LR_para=[U_a, LR_b]
# layer_LR=LogisticRegression(rng, input=LR_input, n_in=LR_input_size, n_out=8, W=U_a, b=LR_b) #basically it is a multiplication between weight matrix and input feature vector
# score_matrix = T.nnet.sigmoid(layer_LR.before_softmax) #batch * 8
# sub_labels = labels[:,:8]
# prob_pos = T.where( sub_labels < 1, 1.0-score_matrix, score_matrix)
# loss = -T.mean(T.log(prob_pos))
'''
GRU
'''
U1, W1, b1 = create_GRU_para(rng, emb_size, hidden_size[0])
GRU_NN_para = [
U1, W1, b1
] #U1 includes 3 matrices, W1 also includes 3 matrices b1 is bias
# gru_input = common_input.dimshuffle((0,2,1)) #gru requires input (batch_size, emb_size, maxSentLen)
gru_layer = GRU_Batch_Tensor_Input_with_Mask(common_input, sents_mask,
hidden_size[0], U1, W1, b1)
gru_sent_embeddings = gru_layer.maxpooling_vec #gru_layer.output_sent_rep # (batch_size, hidden_size)
LR_att_input = gru_sent_embeddings #T.concatenate([gru_sent_embeddings, bow_emb], axis=1)
LR_att_input_size = hidden_size[0]
#classification layer, it is just mapping from a feature vector of size "hidden_size" to a vector of only two values: positive, negative
U_att_a = create_ensemble_para(
rng, 8, LR_att_input_size) # the weight matrix hidden_size*2
LR_att_b = theano.shared(value=np.zeros((8, ), dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
LR_att_para = [U_att_a, LR_att_b]
layer_att_LR = LogisticRegression(
rng,
input=LR_att_input,
n_in=LR_att_input_size,
n_out=8,
W=U_att_a,
b=LR_att_b
) #basically it is a multiplication between weight matrix and input feature vector
att_score_matrix = T.nnet.sigmoid(layer_att_LR.before_softmax) #batch * 12
sub_labels = labels[:, :8]
att_prob_pos = T.where(sub_labels < 1, 1.0 - att_score_matrix,
att_score_matrix)
att_loss = -T.mean(T.log(att_prob_pos))
params = GRU_NN_para + LR_att_para # put all model parameters together
cost = att_loss #+Div_reg*diversify_reg#+L2_weight*L2_reg
updates = Gradient_Cost_Para(cost, params, learning_rate)
'''
testing
'''
binarize_prob = T.where(att_score_matrix > 0.3, 1, 0)
#train_model = theano.function([sents_id_matrix, sents_mask, labels], cost, updates=updates, on_unused_input='ignore')
train_model = theano.function([sents_id_matrix, sents_mask, labels],
cost,
updates=updates,
allow_input_downcast=True,
on_unused_input='ignore')
# dev_model = theano.function([sents_id_matrix, sents_mask, labels], layer_LR.errors(labels), allow_input_downcast=True, on_unused_input='ignore')
test_model = theano.function([sents_id_matrix, sents_mask],
binarize_prob,
allow_input_downcast=True,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
n_train_batches = train_size / batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
# n_dev_batches=dev_size/batch_size
# dev_batch_start=list(np.arange(n_dev_batches)*batch_size)+[dev_size-batch_size]
n_test_batches = test_size / batch_size
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
max_meanf1_test = 0.0
max_weightf1_test = 0.0
train_indices = range(train_size)
while epoch < n_epochs:
epoch = epoch + 1
random.Random(100).shuffle(train_indices)
iter_accu = 0
cost_i = 0.0
for batch_id in train_batch_start: #for each batch
# iter means how many batches have been run, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
train_id_batch = train_indices[batch_id:batch_id + batch_size]
# labels_matrix = []
# for lab in train_labels[train_id_batch]:
# vec = [0]*8
# vec[lab]=1
# labels_matrix.append(vec)
# labels_matrix = np.asarray(labels_matrix, dtype='int32')
cost_i += train_model(train_sents[train_id_batch],
train_masks[train_id_batch],
train_labels[train_id_batch])
#after each 1000 batches, we test the performance of the model on all test data
if iter % 20 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
past_time = time.time()
# error_sum=0.0
# for test_batch_id in test_batch_start: # for each test batch
# error_i, pred_labels=test_model(
# test_sents[test_batch_id:test_batch_id+batch_size],
# test_masks[test_batch_id:test_batch_id+batch_size])
# pred_labels=list(pred_labels)
# error_sum+=error_i
#
# test_accuracy=1.0-error_sum/(len(test_batch_start))
# if test_accuracy > max_acc_test:
# max_acc_test=test_accuracy
# print '\t\t\t\t\t\t\t\tcurrent testbacc:', test_accuracy, '\t\tmax_acc_test:', max_acc_test
error_sum = 0.0
all_pred_labels = []
all_gold_labels = []
for test_batch_id in test_batch_start: # for each test batch
pred_labels = test_model(
test_sents[test_batch_id:test_batch_id + batch_size],
test_masks[test_batch_id:test_batch_id + batch_size])
gold_labels = test_labels[test_batch_id:test_batch_id +
batch_size]
# gold_labels_matrix = []
# for lab in gold_labels:
# vec = [0]*8
# vec[lab]=1
# gold_labels_matrix.append(vec)
# gold_labels_matrix = np.asarray(gold_labels_matrix, dtype='int32')
all_pred_labels.append(pred_labels)
all_gold_labels.append(gold_labels)
all_pred_labels = np.concatenate(all_pred_labels)
all_gold_labels = np.concatenate(all_gold_labels)
test_mean_f1, test_weight_f1 = average_f1_two_array_by_col_firstK(
all_pred_labels, all_gold_labels, 8)
if test_weight_f1 > max_weightf1_test:
max_weightf1_test = test_weight_f1
if test_mean_f1 > max_meanf1_test:
max_meanf1_test = test_mean_f1
print '\t\t\t\t\t\t\t\tcurrent f1s:', test_mean_f1, test_weight_f1, '\t\tmax_f1:', max_meanf1_test, max_weightf1_test
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.01, 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
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)
repeat_des_input = T.tile(normalize_tensor3_colwise(des_input), (batch_size,1,1))#(batch_size*type_size, emb_size, maxsentlen)
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_5_simi = sort_fine_grained_cosine_to_matrix[:,-30:] # (batch_size*type_size, 5)
max_fine_grained_cosine = T.mean(top_5_simi, axis=1)
cosine_scores = max_fine_grained_cosine.reshape((batch_size, type_size))
# score_matrix = T.nnet.sigmoid(cosine_scores) #(batch_size, type_size)
score_matrix = T.nnet.sigmoid(cosine_scores)
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.
'''
do not update emb, so that can generalize from english to ils
'''
params = []#+NN_para+LR_para # put all model parameters together
cost=loss#+1e-4*((conv_W**2).sum()+(conv_W2**2).sum())
updates = Gradient_Cost_Para(cost,params, learning_rate)
'''
testing
'''
binarize_prob = T.where(cosine_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=0.01,
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=70,
filter_size=[3],
maxSentLen=40,
hidden_size=[300, 300]):
model_options = locals().copy()
print "model options", model_options
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
srng = T.shared_randomstreams.RandomStreams(rng.randint(seed))
"load raw data"
all_sentences_l, all_masks_l, all_sentences_r, all_masks_r, all_labels, word2id = load_SNLI_dataset(
maxlen=maxSentLen
) #minlen, include one label, at least one word in the sentence
train_sents_l = np.asarray(all_sentences_l[0], dtype='int32')
dev_sents_l = np.asarray(all_sentences_l[1], dtype='int32')
test_sents_l = np.asarray(all_sentences_l[2], dtype='int32')
train_masks_l = np.asarray(all_masks_l[0], dtype=theano.config.floatX)
dev_masks_l = np.asarray(all_masks_l[1], dtype=theano.config.floatX)
test_masks_l = np.asarray(all_masks_l[2], dtype=theano.config.floatX)
train_sents_r = np.asarray(all_sentences_r[0], dtype='int32')
dev_sents_r = np.asarray(all_sentences_r[1], dtype='int32')
test_sents_r = np.asarray(all_sentences_r[2], dtype='int32')
train_masks_r = np.asarray(all_masks_r[0], dtype=theano.config.floatX)
dev_masks_r = np.asarray(all_masks_r[1], dtype=theano.config.floatX)
test_masks_r = np.asarray(all_masks_r[2], dtype=theano.config.floatX)
train_labels_store = np.asarray(all_labels[0], dtype='int32')
dev_labels_store = np.asarray(all_labels[1], dtype='int32')
test_labels_store = np.asarray(all_labels[2], dtype='int32')
train_size = len(train_labels_store)
dev_size = len(dev_labels_store)
test_size = len(test_labels_store)
print 'train size: ', train_size, ' dev size: ', dev_size, ' test size: ', test_size
vocab_size = len(word2id) + 1
"first randomly initialize each word in the matrix 'rand_values', then load pre-trained word2vec embeddinds to initialize words, uncovered"
"words keep random initialization"
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_word2vec()
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
init_embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
"now, start to build the input form of the model"
sents_ids_l = T.imatrix()
sents_mask_l = T.fmatrix()
sents_ids_r = T.imatrix()
sents_mask_r = T.fmatrix()
labels = T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
'Use word ids in sentences to retrieve word embeddings from matrix "init_embeddings", each sentence will be in'
'tensor2 (emb_size, sen_length), then the minibatch will be in tensor3 (batch_size, emb_size, sen_length) '
embed_input_l = init_embeddings[sents_ids_l.flatten(
)].reshape((batch_size, maxSentLen, emb_size)).dimshuffle(
0, 2, 1
) #embed_input(init_embeddings, sents_ids_l)#embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
embed_input_r = init_embeddings[sents_ids_r.flatten()].reshape(
(batch_size, maxSentLen, emb_size)).dimshuffle(0, 2, 1)
'''create parameters for attentive convolution function '''
conv_W, conv_b = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size, filter_size[0]))
conv_W_context, conv_b_context = create_conv_para(
rng, filter_shape=(hidden_size[0], 1, emb_size, 1))
NN_para = [conv_W, conv_b, conv_W_context]
'''
attentive convolution function
'''
attentive_conv_layer = Attentive_Conv_for_Pair_easy_version(
rng,
input_tensor3=embed_input_l,
input_tensor3_r=embed_input_r,
mask_matrix=sents_mask_l,
mask_matrix_r=sents_mask_r,
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[0], 1, emb_size, 1),
W=conv_W,
b=conv_b,
W_context=conv_W_context,
b_context=conv_b_context)
attentive_sent_embeddings_l = attentive_conv_layer.attentive_maxpool_vec_l
attentive_sent_embeddings_r = attentive_conv_layer.attentive_maxpool_vec_r
"Logistic Regression layer"
LR_input = T.concatenate([
attentive_sent_embeddings_l, attentive_sent_embeddings_r,
attentive_sent_embeddings_l + attentive_sent_embeddings_r,
attentive_sent_embeddings_l * attentive_sent_embeddings_r
],
axis=1)
LR_input_size = 4 * hidden_size[0]
U_a = create_ensemble_para(rng, 3, LR_input_size) # (input_size, 3)
LR_b = theano.shared(value=np.zeros((3, ), dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
LR_para = [U_a, LR_b]
layer_LR = LogisticRegression(
rng,
input=normalize_matrix_col_wise(LR_input),
n_in=LR_input_size,
n_out=3,
W=U_a,
b=LR_b
) #basically it is a multiplication between weight matrix and input feature vector
loss = layer_LR.negative_log_likelihood(
labels
) #for classification task, we usually used negative log likelihood as loss, the lower the better.
params = [init_embeddings] + NN_para + LR_para
cost = loss
"Use AdaGrad to update parameters"
updates = Gradient_Cost_Para(cost, params, learning_rate)
train_model = theano.function(
[sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels],
cost,
updates=updates,
allow_input_downcast=True,
on_unused_input='ignore')
dev_model = theano.function(
[sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels],
layer_LR.errors(labels),
allow_input_downcast=True,
on_unused_input='ignore')
test_model = theano.function(
[sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels],
layer_LR.errors(labels),
allow_input_downcast=True,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
n_train_batches = train_size / batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
n_dev_batches = dev_size / batch_size
dev_batch_start = list(
np.arange(n_dev_batches) * batch_size) + [dev_size - batch_size]
n_test_batches = test_size / batch_size
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
max_acc_dev = 0.0
max_acc_test = 0.0
cost_i = 0.0
train_indices = range(train_size)
while epoch < n_epochs:
epoch = epoch + 1
random.Random(100).shuffle(
train_indices
) #shuffle training set for each new epoch, is supposed to promote performance, but not garrenteed
iter_accu = 0
for batch_id in train_batch_start: #for each batch
# iter means how many batches have been run, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
train_id_batch = train_indices[batch_id:batch_id + batch_size]
cost_i += train_model(train_sents_l[train_id_batch],
train_masks_l[train_id_batch],
train_sents_r[train_id_batch],
train_masks_r[train_id_batch],
train_labels_store[train_id_batch])
#after each 1000 batches, we test the performance of the model on all test data
# if (epoch==1 and iter%1000==0) or (epoch>=2 and iter%5==0):
if iter % 1000 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
past_time = time.time()
'''
test
'''
error_sum = 0.0
for test_batch_id in test_batch_start: # for each test batch
error_i = test_model(
test_sents_l[test_batch_id:test_batch_id + batch_size],
test_masks_l[test_batch_id:test_batch_id + batch_size],
test_sents_r[test_batch_id:test_batch_id + batch_size],
test_masks_r[test_batch_id:test_batch_id + batch_size],
test_labels_store[test_batch_id:test_batch_id +
batch_size])
error_sum += error_i
test_acc = 1.0 - error_sum / (len(test_batch_start))
if test_acc > max_acc_test:
max_acc_test = test_acc
print '\t\tcurrent test_acc:', test_acc, ' ; ', '\t\t\t\t\tmax_test_acc:', max_acc_test
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
return max_acc_test
def evaluate_lenet5(learning_rate=0.02,
n_epochs=4,
L2_weight=0.0000001,
extra_size=4,
use_svm=False,
drop_p=0.1,
div_weight=0.00001,
emb_size=300,
batch_size=50,
filter_size=[3, 3],
maxSentLen=40,
hidden_size=[300, 300],
margin=0.1,
comment='five copies, sum&majority'):
model_options = locals().copy()
print "model options", model_options
first_seed = 1234
np.random.seed(first_seed)
first_rng = np.random.RandomState(
first_seed) #random seed, control the model generates the same results
first_srng = RandomStreams(first_rng.randint(999999))
second_seed = 2345
np.random.seed(second_seed)
second_rng = np.random.RandomState(
second_seed
) #random seed, control the model generates the same results
second_srng = RandomStreams(second_rng.randint(888888))
third_seed = 3456
np.random.seed(third_seed)
third_rng = np.random.RandomState(
third_seed) #random seed, control the model generates the same results
third_srng = RandomStreams(third_rng.randint(777777))
fourth_seed = 4567
np.random.seed(fourth_seed)
fourth_rng = np.random.RandomState(
fourth_seed
) #random seed, control the model generates the same results
fourth_srng = RandomStreams(fourth_rng.randint(666666))
fifth_seed = 5678
np.random.seed(fifth_seed)
fifth_rng = np.random.RandomState(
fifth_seed) #random seed, control the model generates the same results
fifth_srng = RandomStreams(fifth_rng.randint(555555))
all_sentences_l, all_masks_l, all_sentences_r, all_masks_r, all_extra, all_labels, word2id = load_SNLI_dataset_with_extra(
maxlen=maxSentLen
) #minlen, include one label, at least one word in the sentence
train_sents_l = np.asarray(all_sentences_l[0], dtype='int32')
dev_sents_l = np.asarray(all_sentences_l[1], dtype='int32')
# train_sents_l = np.concatenate((train_sents_l, dev_sents_l), axis=0)
test_sents_l = np.asarray(all_sentences_l[2], dtype='int32')
train_masks_l = np.asarray(all_masks_l[0], dtype=theano.config.floatX)
dev_masks_l = np.asarray(all_masks_l[1], dtype=theano.config.floatX)
# train_masks_l = np.concatenate((train_masks_l, dev_masks_l), axis=0)
test_masks_l = np.asarray(all_masks_l[2], dtype=theano.config.floatX)
train_sents_r = np.asarray(all_sentences_r[0], dtype='int32')
dev_sents_r = np.asarray(all_sentences_r[1], dtype='int32')
# train_sents_r = np.concatenate((train_sents_r, dev_sents_r), axis=0)
test_sents_r = np.asarray(all_sentences_r[2], dtype='int32')
train_masks_r = np.asarray(all_masks_r[0], dtype=theano.config.floatX)
dev_masks_r = np.asarray(all_masks_r[1], dtype=theano.config.floatX)
# train_masks_r = np.concatenate((train_masks_r, dev_masks_r), axis=0)
test_masks_r = np.asarray(all_masks_r[2], dtype=theano.config.floatX)
train_extra = np.asarray(all_extra[0], dtype=theano.config.floatX)
dev_extra = np.asarray(all_extra[1], dtype=theano.config.floatX)
test_extra = np.asarray(all_extra[2], dtype=theano.config.floatX)
train_labels_store = np.asarray(all_labels[0], dtype='int32')
dev_labels_store = np.asarray(all_labels[1], dtype='int32')
# train_labels_store = np.concatenate((train_labels_store, dev_labels_store), axis=0)
test_labels_store = np.asarray(all_labels[2], dtype='int32')
train_size = len(train_labels_store)
dev_size = len(dev_labels_store)
test_size = len(test_labels_store)
print 'train size: ', train_size, ' dev size: ', dev_size, ' test size: ', test_size
vocab_size = len(word2id) + 1
rand_values = first_rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
#here, we leave code for loading word2vec to initialize words
rand_values[0] = np.array(np.zeros(emb_size), dtype=theano.config.floatX)
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_word2vec()
# word2vec =extend_word2vec_lowercase(word2vec)
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
# normed_matrix = normalize(rand_values, axis=0, norm='l2')
first_embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
second_rand_values = second_rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
second_rand_values[0] = np.array(np.zeros(emb_size),
dtype=theano.config.floatX)
second_rand_values = load_word2vec_to_init(second_rand_values, id2word,
word2vec)
second_embeddings = theano.shared(
value=np.array(second_rand_values, dtype=theano.config.floatX),
borrow=True
) #wrap up the python variable "rand_values" into theano variable
third_rand_values = third_rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
third_rand_values[0] = np.array(np.zeros(emb_size),
dtype=theano.config.floatX)
third_rand_values = load_word2vec_to_init(third_rand_values, id2word,
word2vec)
third_embeddings = theano.shared(value=np.array(
third_rand_values, dtype=theano.config.floatX),
borrow=True)
fourth_rand_values = fourth_rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
fourth_rand_values[0] = np.array(np.zeros(emb_size),
dtype=theano.config.floatX)
fourth_rand_values = load_word2vec_to_init(fourth_rand_values, id2word,
word2vec)
fourth_embeddings = theano.shared(value=np.array(
fourth_rand_values, dtype=theano.config.floatX),
borrow=True)
fifth_rand_values = fifth_rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
fifth_rand_values[0] = np.array(np.zeros(emb_size),
dtype=theano.config.floatX)
fifth_rand_values = load_word2vec_to_init(fifth_rand_values, id2word,
word2vec)
fifth_embeddings = theano.shared(value=np.array(
fifth_rand_values, dtype=theano.config.floatX),
borrow=True)
#now, start to build the input form of the model
train_flag = T.iscalar()
first_sents_ids_l = T.imatrix()
first_sents_mask_l = T.fmatrix()
first_sents_ids_r = T.imatrix()
first_sents_mask_r = T.fmatrix()
first_labels = T.ivector()
second_sents_ids_l = T.imatrix()
second_sents_mask_l = T.fmatrix()
second_sents_ids_r = T.imatrix()
second_sents_mask_r = T.fmatrix()
second_labels = T.ivector()
third_sents_ids_l = T.imatrix()
third_sents_mask_l = T.fmatrix()
third_sents_ids_r = T.imatrix()
third_sents_mask_r = T.fmatrix()
third_labels = T.ivector()
fourth_sents_ids_l = T.imatrix()
fourth_sents_mask_l = T.fmatrix()
fourth_sents_ids_r = T.imatrix()
fourth_sents_mask_r = T.fmatrix()
fourth_labels = T.ivector()
fifth_sents_ids_l = T.imatrix()
fifth_sents_mask_l = T.fmatrix()
fifth_sents_ids_r = T.imatrix()
fifth_sents_mask_r = T.fmatrix()
fifth_labels = T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
def common_input(emb_matrix, sent_ids):
return emb_matrix[sent_ids.flatten()].reshape(
(batch_size, maxSentLen, emb_size)).dimshuffle(0, 2, 1)
first_common_input_l = common_input(
first_embeddings, first_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
first_common_input_r = common_input(
first_embeddings, first_sents_ids_r
) #embeddings[sents_ids_r.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1)
second_common_input_l = common_input(
second_embeddings, second_sents_ids_l
) #second_embeddings[second_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
second_common_input_r = common_input(
second_embeddings, second_sents_ids_r
) #second_embeddings[second_sents_ids_r.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1)
third_common_input_l = common_input(
third_embeddings, third_sents_ids_l
) #third_embeddings[third_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
third_common_input_r = common_input(
third_embeddings, third_sents_ids_r
) #third_embeddings[third_sents_ids_r.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1)
fourth_common_input_l = common_input(
fourth_embeddings, fourth_sents_ids_l
) #fourth_embeddings[fourth_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
fourth_common_input_r = common_input(
fourth_embeddings, fourth_sents_ids_r
) #fourth_embeddings[fourth_sents_ids_r.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1)
fifth_common_input_l = common_input(
fifth_embeddings, fifth_sents_ids_l
) #fifth_embeddings[fifth_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
fifth_common_input_r = common_input(
fifth_embeddings, fifth_sents_ids_r
) #fifth_embeddings[fifth_sents_ids_r.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1)
gate_filter_shape = (hidden_size[0], 1, emb_size, 1)
def create_CNN_params(rng):
conv_W_2_pre, conv_b_2_pre = create_conv_para(
rng, filter_shape=gate_filter_shape)
conv_W_2_gate, conv_b_2_gate = create_conv_para(
rng, filter_shape=gate_filter_shape)
conv_W_2, conv_b_2 = create_conv_para(rng,
filter_shape=(hidden_size[1], 1,
hidden_size[0],
filter_size[0]))
conv_W_2_context, conv_b_2_context = create_conv_para(
rng, filter_shape=(hidden_size[1], 1, hidden_size[0], 1))
return conv_W_2_pre, conv_b_2_pre, conv_W_2_gate, conv_b_2_gate, conv_W_2, conv_b_2, conv_W_2_context, conv_b_2_context
first_conv_W_pre, first_conv_b_pre, first_conv_W_gate, first_conv_b_gate, first_conv_W, first_conv_b, first_conv_W_context, first_conv_b_context = create_CNN_params(
first_rng)
second_conv_W_pre, second_conv_b_pre, second_conv_W_gate, second_conv_b_gate, second_conv_W, second_conv_b, second_conv_W_context, second_conv_b_context = create_CNN_params(
second_rng)
third_conv_W_pre, third_conv_b_pre, third_conv_W_gate, third_conv_b_gate, third_conv_W, third_conv_b, third_conv_W_context, third_conv_b_context = create_CNN_params(
third_rng)
fourth_conv_W_pre, fourth_conv_b_pre, fourth_conv_W_gate, fourth_conv_b_gate, fourth_conv_W, fourth_conv_b, fourth_conv_W_context, fourth_conv_b_context = create_CNN_params(
fourth_rng)
fifth_conv_W_pre, fifth_conv_b_pre, fifth_conv_W_gate, fifth_conv_b_gate, fifth_conv_W, fifth_conv_b, fifth_conv_W_context, fifth_conv_b_context = create_CNN_params(
fifth_rng)
'''
dropout paras
'''
def dropout_group(rng, conv_W_2_pre, conv_W_2_gate, conv_W_2,
conv_W_2_context):
drop_conv_W_2_pre = dropout_layer(rng, conv_W_2_pre, drop_p,
train_flag)
drop_conv_W_2_gate = dropout_layer(rng, conv_W_2_gate, drop_p,
train_flag)
drop_conv_W_2 = dropout_layer(rng, conv_W_2, drop_p, train_flag)
drop_conv_W_2_context = dropout_layer(rng, conv_W_2_context, drop_p,
train_flag)
return drop_conv_W_2_pre, drop_conv_W_2_gate, drop_conv_W_2, drop_conv_W_2_context
drop_first_conv_W_pre, drop_first_conv_W_gate, drop_first_conv_W, drop_first_conv_W_context = dropout_group(
first_srng, first_conv_W_pre, first_conv_W_gate, first_conv_W,
first_conv_W_context)
drop_second_conv_W_pre, drop_second_conv_W_gate, drop_second_conv_W, drop_second_conv_W_context = dropout_group(
second_srng, second_conv_W_pre, second_conv_W_gate, second_conv_W,
second_conv_W_context)
drop_third_conv_W_pre, drop_third_conv_W_gate, drop_third_conv_W, drop_third_conv_W_context = dropout_group(
third_srng, third_conv_W_pre, third_conv_W_gate, third_conv_W,
third_conv_W_context)
drop_fourth_conv_W_pre, drop_fourth_conv_W_gate, drop_fourth_conv_W, drop_fourth_conv_W_context = dropout_group(
fourth_srng, fourth_conv_W_pre, fourth_conv_W_gate, fourth_conv_W,
fourth_conv_W_context)
drop_fifth_conv_W_pre, drop_fifth_conv_W_gate, drop_fifth_conv_W, drop_fifth_conv_W_context = dropout_group(
fifth_srng, fifth_conv_W_pre, fifth_conv_W_gate, fifth_conv_W,
fifth_conv_W_context)
first_NN_para = [ #conv_W, conv_b,
first_conv_W_pre, first_conv_b_pre, first_conv_W_gate,
first_conv_b_gate, first_conv_W, first_conv_b, first_conv_W_context
]
second_NN_para = [
second_conv_W_pre, second_conv_b_pre, second_conv_W_gate,
second_conv_b_gate, second_conv_W, second_conv_b, second_conv_W_context
]
third_NN_para = [
third_conv_W_pre, third_conv_b_pre, third_conv_W_gate,
third_conv_b_gate, third_conv_W, third_conv_b, third_conv_W_context
]
fourth_NN_para = [
fourth_conv_W_pre, fourth_conv_b_pre, fourth_conv_W_gate,
fourth_conv_b_gate, fourth_conv_W, fourth_conv_b, fourth_conv_W_context
]
fifth_NN_para = [
fifth_conv_W_pre, fifth_conv_b_pre, fifth_conv_W_gate,
fifth_conv_b_gate, fifth_conv_W, fifth_conv_b, fifth_conv_W_context
]
'''
first classifier
'''
def classifier(rng, common_input_l, common_input_r, sents_mask_l,
sents_mask_r, drop_conv_W_2_pre, conv_b_2_pre,
drop_conv_W_2_gate, conv_b_2_gate, drop_conv_W_2, conv_b_2,
drop_conv_W_2_context, conv_b_2_context, labels):
conv_layer_2_gate_l = Conv_with_Mask_with_Gate(
rng,
input_tensor3=common_input_l,
mask_matrix=sents_mask_l,
image_shape=(batch_size, 1, emb_size, maxSentLen),
filter_shape=gate_filter_shape,
W=drop_conv_W_2_pre,
b=conv_b_2_pre,
W_gate=drop_conv_W_2_gate,
b_gate=conv_b_2_gate)
conv_layer_2_gate_r = Conv_with_Mask_with_Gate(
rng,
input_tensor3=common_input_r,
mask_matrix=sents_mask_r,
image_shape=(batch_size, 1, emb_size, maxSentLen),
filter_shape=gate_filter_shape,
W=drop_conv_W_2_pre,
b=conv_b_2_pre,
W_gate=drop_conv_W_2_gate,
b_gate=conv_b_2_gate)
l_input_4_att = conv_layer_2_gate_l.output_tensor3 #conv_layer_2_gate_l.masked_conv_out_sigmoid*conv_layer_2_pre_l.masked_conv_out+(1.0-conv_layer_2_gate_l.masked_conv_out_sigmoid)*common_input_l
r_input_4_att = conv_layer_2_gate_r.output_tensor3 #conv_layer_2_gate_r.masked_conv_out_sigmoid*conv_layer_2_pre_r.masked_conv_out+(1.0-conv_layer_2_gate_r.masked_conv_out_sigmoid)*common_input_r
conv_layer_2 = Conv_for_Pair(
rng,
origin_input_tensor3=common_input_l,
origin_input_tensor3_r=common_input_r,
input_tensor3=l_input_4_att,
input_tensor3_r=r_input_4_att,
mask_matrix=sents_mask_l,
mask_matrix_r=sents_mask_r,
image_shape=(batch_size, 1, hidden_size[0], maxSentLen),
image_shape_r=(batch_size, 1, hidden_size[0], maxSentLen),
filter_shape=(hidden_size[1], 1, hidden_size[0], filter_size[0]),
filter_shape_context=(hidden_size[1], 1, hidden_size[0], 1),
W=drop_conv_W_2,
b=conv_b_2,
W_context=drop_conv_W_2_context,
b_context=conv_b_2_context)
attentive_sent_embeddings_l_2 = conv_layer_2.attentive_maxpool_vec_l
attentive_sent_embeddings_r_2 = conv_layer_2.attentive_maxpool_vec_r
# attentive_sent_sumpool_l_2 = conv_layer_2.attentive_sumpool_vec_l
# attentive_sent_sumpool_r_2 = conv_layer_2.attentive_sumpool_vec_r
HL_layer_1_input = T.concatenate([
attentive_sent_embeddings_l_2, attentive_sent_embeddings_r_2,
attentive_sent_embeddings_l_2 * attentive_sent_embeddings_r_2
],
axis=1)
HL_layer_1_input_size = hidden_size[
1] * 3 #+extra_size#+(maxSentLen*2+10*2)#+hidden_size[1]*3+1
HL_layer_1 = HiddenLayer(rng,
input=HL_layer_1_input,
n_in=HL_layer_1_input_size,
n_out=hidden_size[0],
activation=T.nnet.relu)
HL_layer_2 = HiddenLayer(rng,
input=HL_layer_1.output,
n_in=hidden_size[0],
n_out=hidden_size[0],
activation=T.nnet.relu)
LR_input_size = HL_layer_1_input_size + 2 * hidden_size[0]
U_a = create_ensemble_para(
rng, 3, LR_input_size) # the weight matrix hidden_size*2
LR_b = theano.shared(value=np.zeros((3, ), dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
LR_para = [U_a, LR_b]
LR_input = T.tanh(
T.concatenate(
[HL_layer_1_input, HL_layer_1.output, HL_layer_2.output],
axis=1))
layer_LR = LogisticRegression(
rng, input=LR_input, n_in=LR_input_size, n_out=3, W=U_a, b=LR_b
) #basically it is a multiplication between weight matrix and input feature vector
loss = layer_LR.negative_log_likelihood(
labels
) #for classification task, we usually used negative log likelihood as loss, the lower the better.
return loss, LR_para + HL_layer_1.params + HL_layer_2.params, layer_LR.p_y_given_x, layer_LR.errors(
labels)
first_loss, first_classifier_params, first_test_distr, first_error = classifier(
first_rng, first_common_input_l, first_common_input_r,
first_sents_mask_l, first_sents_mask_r, drop_first_conv_W_pre,
first_conv_b_pre, drop_first_conv_W_gate, first_conv_b_gate,
drop_first_conv_W, first_conv_b, drop_first_conv_W_context,
first_conv_b_context, first_labels)
second_loss, second_classifier_params, second_test_distr, second_error = classifier(
second_rng, second_common_input_l, second_common_input_r,
second_sents_mask_l, second_sents_mask_r, drop_second_conv_W_pre,
second_conv_b_pre, drop_second_conv_W_gate, second_conv_b_gate,
drop_second_conv_W, second_conv_b, drop_second_conv_W_context,
second_conv_b_context, second_labels)
third_loss, third_classifier_params, third_test_distr, third_error = classifier(
third_rng, third_common_input_l, third_common_input_r,
third_sents_mask_l, third_sents_mask_r, drop_third_conv_W_pre,
third_conv_b_pre, drop_third_conv_W_gate, third_conv_b_gate,
drop_third_conv_W, third_conv_b, drop_third_conv_W_context,
third_conv_b_context, third_labels)
fourth_loss, fourth_classifier_params, fourth_test_distr, fourth_error = classifier(
fourth_rng, fourth_common_input_l, fourth_common_input_r,
fourth_sents_mask_l, fourth_sents_mask_r, drop_fourth_conv_W_pre,
fourth_conv_b_pre, drop_fourth_conv_W_gate, fourth_conv_b_gate,
drop_fourth_conv_W, fourth_conv_b, drop_fourth_conv_W_context,
fourth_conv_b_context, fourth_labels)
fifth_loss, fifth_classifier_params, fifth_test_distr, fifth_error = classifier(
fifth_rng, fifth_common_input_l, fifth_common_input_r,
fifth_sents_mask_l, fifth_sents_mask_r, drop_fifth_conv_W_pre,
fifth_conv_b_pre, drop_fifth_conv_W_gate, fifth_conv_b_gate,
drop_fifth_conv_W, fifth_conv_b, drop_fifth_conv_W_context,
fifth_conv_b_context, fifth_labels)
'''
testing, labels == second_labels
'''
all_prop_distr = first_test_distr + second_test_distr + third_test_distr + fourth_test_distr + fifth_test_distr
first_preds = T.argmax(first_test_distr, axis=1).dimshuffle('x',
0) #(1, batch)
second_preds = T.argmax(second_test_distr,
axis=1).dimshuffle('x', 0) #(1, batch)
third_preds = T.argmax(third_test_distr, axis=1).dimshuffle('x',
0) #(1, batch)
fourth_preds = T.argmax(fourth_test_distr,
axis=1).dimshuffle('x', 0) #(1, batch)
fifth_preds = T.argmax(fifth_test_distr, axis=1).dimshuffle('x',
0) #(1, batch)
overall_preds = T.concatenate(
[first_preds, second_preds, third_preds, fourth_preds, fifth_preds],
axis=0) #(5, batch)
all_error = T.mean(T.neq(T.argmax(all_prop_distr, axis=1), first_labels))
# neg_labels = T.where( labels < 2, 2, labels-1)
# loss2=-T.mean(T.log(1.0/(1.0+layer_LR.p_y_given_x))[T.arange(neg_labels.shape[0]), neg_labels])
# rank loss
# entail_prob_batch = T.nnet.softmax(layer_LR.before_softmax.T)[2] #batch
# entail_ids = elementwise_is_two(labels)
# entail_probs = entail_prob_batch[entail_ids.nonzero()]
# non_entail_probs = entail_prob_batch[(1-entail_ids).nonzero()]
#
# repeat_entail = T.extra_ops.repeat(entail_probs, non_entail_probs.shape[0], axis=0)
# repeat_non_entail = T.extra_ops.repeat(non_entail_probs.dimshuffle('x',0), entail_probs.shape[0], axis=0).flatten()
# loss2 = -T.mean(T.log(entail_probs))#T.mean(T.maximum(0.0, margin-repeat_entail+repeat_non_entail))
# zero_matrix = T.zeros((batch_size, 3))
# filled_zero_matrix = T.set_subtensor(zero_matrix[T.arange(batch_size), labels], 1.0)
# prob_batch_posi = layer_LR.p_y_given_x[filled_zero_matrix.nonzero()]
# prob_batch_nega = layer_LR.p_y_given_x[(1-filled_zero_matrix).nonzero()]
#
# repeat_posi = T.extra_ops.repeat(prob_batch_posi, prob_batch_nega.shape[0], axis=0)
# repeat_nega = T.extra_ops.repeat(prob_batch_nega.dimshuffle('x',0), prob_batch_posi.shape[0], axis=0).flatten()
# loss2 = T.mean(T.maximum(0.0, margin-repeat_posi+repeat_nega))
first_params = [first_embeddings] + first_NN_para + first_classifier_params
second_params = [second_embeddings
] + second_NN_para + second_classifier_params
third_params = [third_embeddings] + third_NN_para + third_classifier_params
fourth_params = [fourth_embeddings
] + fourth_NN_para + fourth_classifier_params
fifth_params = [fifth_embeddings] + fifth_NN_para + fifth_classifier_params
params = first_params + second_params + third_params + fourth_params + fifth_params
# L2_reg =L2norm_paraList([embeddings,HL_layer_1.W, HL_layer_2.W])
# diversify_reg= (Diversify_Reg(conv_W_2_pre_to_matrix)+Diversify_Reg(conv_W_2_gate_to_matrix)+
# Diversify_Reg(conv_W_2_to_matrix)+Diversify_Reg(conv_W_2_context_to_matrix))
cost = first_loss + second_loss + third_loss + fourth_loss + fifth_loss #+0.1*loss2#+loss2#+L2_weight*L2_reg
first_updates = Gradient_Cost_Para(first_loss, first_params, learning_rate)
second_updates = Gradient_Cost_Para(second_loss, second_params,
learning_rate)
third_updates = Gradient_Cost_Para(third_loss, third_params, learning_rate)
fourth_updates = Gradient_Cost_Para(fourth_loss, fourth_params,
learning_rate)
fifth_updates = Gradient_Cost_Para(fifth_loss, fifth_params, learning_rate)
updates = first_updates + second_updates + third_updates + fourth_updates + fifth_updates
#train_model = theano.function([sents_id_matrix, sents_mask, labels], cost, updates=updates, on_unused_input='ignore')
train_model = theano.function([
train_flag, first_sents_ids_l, first_sents_mask_l, first_sents_ids_r,
first_sents_mask_r, first_labels, second_sents_ids_l,
second_sents_mask_l, second_sents_ids_r, second_sents_mask_r,
second_labels, third_sents_ids_l, third_sents_mask_l,
third_sents_ids_r, third_sents_mask_r, third_labels,
fourth_sents_ids_l, fourth_sents_mask_l, fourth_sents_ids_r,
fourth_sents_mask_r, fourth_labels, fifth_sents_ids_l,
fifth_sents_mask_l, fifth_sents_ids_r, fifth_sents_mask_r, fifth_labels
],
cost,
updates=updates,
allow_input_downcast=True,
on_unused_input='ignore')
# train_model_pred = theano.function([sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, train_flag,extra,labels,
# second_sents_ids_l,second_sents_mask_l,second_sents_ids_r,second_sents_mask_r,second_labels], [LR_input, labels], allow_input_downcast=True, on_unused_input='ignore')
#
# dev_model = theano.function([sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, train_flag,extra, labels,
# second_sents_ids_l,second_sents_mask_l,second_sents_ids_r,second_sents_mask_r,second_labels], layer_LR.errors(labels), allow_input_downcast=True, on_unused_input='ignore')
test_model = theano.function([
train_flag, first_sents_ids_l, first_sents_mask_l, first_sents_ids_r,
first_sents_mask_r, first_labels, second_sents_ids_l,
second_sents_mask_l, second_sents_ids_r, second_sents_mask_r,
second_labels, third_sents_ids_l, third_sents_mask_l,
third_sents_ids_r, third_sents_mask_r, third_labels,
fourth_sents_ids_l, fourth_sents_mask_l, fourth_sents_ids_r,
fourth_sents_mask_r, fourth_labels, fifth_sents_ids_l,
fifth_sents_mask_l, fifth_sents_ids_r, fifth_sents_mask_r, fifth_labels
], [
first_error, second_error, third_error, fourth_error, fifth_error,
all_error, overall_preds
],
allow_input_downcast=True,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
n_train_batches = train_size / batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
n_dev_batches = dev_size / batch_size
dev_batch_start = list(
np.arange(n_dev_batches) * batch_size) + [dev_size - batch_size]
n_test_batches = test_size / batch_size
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
max_acc_dev = 0.0
max_acc_test = 0.0
max_acc_svm = 0.0
max_acc_lr = 0.0
max_majority_acc = 0.0
cost_i = 0.0
first_train_indices = range(train_size)
second_train_indices = range(train_size)
third_train_indices = range(train_size)
fourth_train_indices = range(train_size)
fifth_train_indices = range(train_size)
while epoch < n_epochs:
epoch = epoch + 1
random.Random(100).shuffle(
first_train_indices
) #shuffle training set for each new epoch, is supposed to promote performance, but not garrenteed
random.Random(200).shuffle(second_train_indices)
random.Random(300).shuffle(third_train_indices)
random.Random(400).shuffle(fourth_train_indices)
random.Random(500).shuffle(fifth_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
first_train_id_batch = first_train_indices[batch_id:batch_id +
batch_size]
second_train_id_batch = second_train_indices[batch_id:batch_id +
batch_size]
third_train_id_batch = third_train_indices[batch_id:batch_id +
batch_size]
fourth_train_id_batch = fourth_train_indices[batch_id:batch_id +
batch_size]
fifth_train_id_batch = fifth_train_indices[batch_id:batch_id +
batch_size]
cost_i += train_model(1, train_sents_l[first_train_id_batch],
train_masks_l[first_train_id_batch],
train_sents_r[first_train_id_batch],
train_masks_r[first_train_id_batch],
train_labels_store[first_train_id_batch],
train_sents_l[second_train_id_batch],
train_masks_l[second_train_id_batch],
train_sents_r[second_train_id_batch],
train_masks_r[second_train_id_batch],
train_labels_store[second_train_id_batch],
train_sents_l[third_train_id_batch],
train_masks_l[third_train_id_batch],
train_sents_r[third_train_id_batch],
train_masks_r[third_train_id_batch],
train_labels_store[third_train_id_batch],
train_sents_l[fourth_train_id_batch],
train_masks_l[fourth_train_id_batch],
train_sents_r[fourth_train_id_batch],
train_masks_r[fourth_train_id_batch],
train_labels_store[fourth_train_id_batch],
train_sents_l[fifth_train_id_batch],
train_masks_l[fifth_train_id_batch],
train_sents_r[fifth_train_id_batch],
train_masks_r[fifth_train_id_batch],
train_labels_store[fifth_train_id_batch])
#after each 1000 batches, we test the performance of the model on all test data
if iter % int(2000 * (50.0 / batch_size)) == 0:
# if iter%int(200*(50.0 / batch_size))==0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
past_time = time.time()
# if epoch >=3 and iter >= len(train_batch_start)*2.0/3 and iter%500==0:
# print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
# past_time = time.time()
# error_sum=0.0
# for dev_batch_id in dev_batch_start: # for each test batch
# error_i=dev_model(
# dev_sents_l[dev_batch_id:dev_batch_id+batch_size],
# dev_masks_l[dev_batch_id:dev_batch_id+batch_size],
# dev_sents_r[dev_batch_id:dev_batch_id+batch_size],
# dev_masks_r[dev_batch_id:dev_batch_id+batch_size],
# dev_labels_store[dev_batch_id:dev_batch_id+batch_size]
# )
#
# error_sum+=error_i
# dev_accuracy=1.0-error_sum/(len(dev_batch_start))
# if dev_accuracy > max_acc_dev:
# max_acc_dev=dev_accuracy
# print 'current dev_accuracy:', dev_accuracy, '\t\t\t\t\tmax max_acc_dev:', max_acc_dev
#best dev model, do test
error_sum_1 = 0.0
error_sum_2 = 0.0
error_sum_3 = 0.0
error_sum_4 = 0.0
error_sum_5 = 0.0
error_both = 0.0
ys = []
gold_ys = []
for test_batch_id in test_batch_start: # for each test batch
error_1, error_2, error_3, error_4, error_5, both_error_batch, batch_ys = test_model(
0, 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],
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],
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],
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],
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_1 += error_1
error_sum_2 += error_2
error_sum_3 += error_3
error_sum_4 += error_4
error_sum_5 += error_5
error_both += both_error_batch
ys.append(batch_ys)
gold_ys.append(
test_labels_store[test_batch_id:test_batch_id +
batch_size])
test_acc_1 = 1.0 - error_sum_1 / (len(test_batch_start))
test_acc_2 = 1.0 - error_sum_2 / (len(test_batch_start))
test_acc_3 = 1.0 - error_sum_3 / (len(test_batch_start))
test_acc_4 = 1.0 - error_sum_4 / (len(test_batch_start))
test_acc_5 = 1.0 - error_sum_5 / (len(test_batch_start))
all_ys = np.concatenate(ys, axis=1) #(5, test_size)
gold_ys = np.concatenate(gold_ys)
majority_ys = mode(np.transpose(all_ys), axis=-1)[0][:, 0]
majority_acc = 1.0 - np.not_equal(
gold_ys, majority_ys).sum() * 1.0 / len(gold_ys)
test_acc_both = 1.0 - error_both / (len(test_batch_start))
if test_acc_1 > max_acc_test:
max_acc_test = test_acc_1
if test_acc_2 > max_acc_test:
max_acc_test = test_acc_2
if test_acc_3 > max_acc_test:
max_acc_test = test_acc_3
if test_acc_4 > max_acc_test:
max_acc_test = test_acc_4
if test_acc_5 > max_acc_test:
max_acc_test = test_acc_5
if test_acc_both > max_acc_test:
max_acc_test = test_acc_both
store_model_to_file(
'/mounts/data/proj/wenpeng/Dataset/StanfordEntailment/model_para_five_copies_'
+ str(max_acc_test), params)
if majority_acc > max_majority_acc:
max_majority_acc = majority_acc
print '\t\tcurrent acc:', test_acc_1, test_acc_2, test_acc_3, test_acc_4, test_acc_5, ' ; ', test_acc_both, majority_acc, '\t\t\t\t\tmax_acc:', max_acc_test, max_majority_acc
# else:
# print 'current dev_accuracy:', dev_accuracy, '\t\t\t\t\tmax max_acc_dev:', max_acc_dev
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
return max_acc_test
def evaluate_lenet5(learning_rate=0.02,
n_epochs=100,
emb_size=300,
batch_size=70,
filter_size=[3, 1],
maxSentLen=70,
hidden_size=[300, 300]):
model_options = locals().copy()
print "model options", model_options
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
srng = T.shared_randomstreams.RandomStreams(rng.randint(seed))
"load raw data"
all_sentences_l, all_masks_l, all_sentences_r, all_masks_r, all_labels, word2id = load_SNLI_dataset(
maxlen=maxSentLen
) #minlen, include one label, at least one word in the sentence
train_sents_l = np.asarray(all_sentences_l[0], dtype='int32')
dev_sents_l = np.asarray(all_sentences_l[1], dtype='int32')
test_sents_l = np.asarray(all_sentences_l[2], dtype='int32')
train_masks_l = np.asarray(all_masks_l[0], dtype=theano.config.floatX)
dev_masks_l = np.asarray(all_masks_l[1], dtype=theano.config.floatX)
test_masks_l = np.asarray(all_masks_l[2], dtype=theano.config.floatX)
train_sents_r = np.asarray(all_sentences_r[0], dtype='int32')
dev_sents_r = np.asarray(all_sentences_r[1], dtype='int32')
test_sents_r = np.asarray(all_sentences_r[2], dtype='int32')
train_masks_r = np.asarray(all_masks_r[0], dtype=theano.config.floatX)
dev_masks_r = np.asarray(all_masks_r[1], dtype=theano.config.floatX)
test_masks_r = np.asarray(all_masks_r[2], dtype=theano.config.floatX)
train_labels_store = np.asarray(all_labels[0], dtype='int32')
dev_labels_store = np.asarray(all_labels[1], dtype='int32')
test_labels_store = np.asarray(all_labels[2], dtype='int32')
train_size = len(train_labels_store)
dev_size = len(dev_labels_store)
test_size = len(test_labels_store)
print 'train size: ', train_size, ' dev size: ', dev_size, ' test size: ', test_size
vocab_size = len(word2id) + 1
"first randomly initialize each word in the matrix 'rand_values', then load pre-trained word2vec embeddinds to initialize words, uncovered"
"words keep random initialization"
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_word2vec()
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
init_embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
"now, start to build the input form of the model"
sents_ids_l = T.imatrix()
sents_mask_l = T.fmatrix()
sents_ids_r = T.imatrix()
sents_mask_r = T.fmatrix()
labels = T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
'Use word ids in sentences to retrieve word embeddings from matrix "init_embeddings", each sentence will be in'
'tensor2 (emb_size, sen_length), then the minibatch will be in tensor3 (batch_size, emb_size, sen_length) '
embed_input_l = init_embeddings[sents_ids_l.flatten(
)].reshape((batch_size, maxSentLen, emb_size)).dimshuffle(
0, 2, 1
) #embed_input(init_embeddings, sents_ids_l)#embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
embed_input_r = init_embeddings[sents_ids_r.flatten()].reshape(
(batch_size, maxSentLen, emb_size)).dimshuffle(0, 2, 1)
'''create parameters for attentive convolution function '''
gate_filter_shape = (emb_size, 1, emb_size, 1)
conv_W_pre, conv_b_pre = create_conv_para(rng,
filter_shape=gate_filter_shape)
conv_W_gate, conv_b_gate = create_conv_para(rng,
filter_shape=gate_filter_shape)
conv_W, conv_b = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size, filter_size[0]))
conv_W_context, conv_b_context = create_conv_para(
rng, filter_shape=(hidden_size[0], 1, emb_size, 1))
conv_W2, conv_b2 = create_conv_para(rng,
filter_shape=(hidden_size[0], 1,
emb_size,
filter_size[1]))
conv_W2_context, conv_b2_context = create_conv_para(
rng, filter_shape=(hidden_size[0], 1, emb_size, 1))
NN_para = [
conv_W, conv_b, conv_W_context, conv_W_pre, conv_b_pre, conv_W_gate,
conv_b_gate, conv_W2, conv_b2, conv_W2_context
]
"A gated convolution layer to form more expressive word representations in each sentence"
"input tensor3 (batch_size, emb_size, sen_length), output tensor3 (batch_size, emb_size, sen_length)"
conv_layer_gate_l = Conv_with_Mask_with_Gate(
rng,
input_tensor3=embed_input_l,
mask_matrix=sents_mask_l,
image_shape=(batch_size, 1, emb_size, maxSentLen),
filter_shape=gate_filter_shape,
W=conv_W_pre,
b=conv_b_pre,
W_gate=conv_W_gate,
b_gate=conv_b_gate)
conv_layer_gate_r = Conv_with_Mask_with_Gate(
rng,
input_tensor3=embed_input_r,
mask_matrix=sents_mask_r,
image_shape=(batch_size, 1, emb_size, maxSentLen),
filter_shape=gate_filter_shape,
W=conv_W_pre,
b=conv_b_pre,
W_gate=conv_W_gate,
b_gate=conv_b_gate)
'''
attentive convolution function, two sizes of filter_width 3&1 are used. Multi-channel
'''
attentive_conv_layer = Attentive_Conv_for_Pair(
rng,
origin_input_tensor3=embed_input_l,
origin_input_tensor3_r=embed_input_r,
input_tensor3=conv_layer_gate_l.output_tensor3,
input_tensor3_r=conv_layer_gate_r.output_tensor3,
mask_matrix=sents_mask_l,
mask_matrix_r=sents_mask_r,
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[0], 1, emb_size, 1),
W=conv_W,
b=conv_b,
W_context=conv_W_context,
b_context=conv_b_context)
attentive_sent_embeddings_l = attentive_conv_layer.attentive_maxpool_vec_l
attentive_sent_embeddings_r = attentive_conv_layer.attentive_maxpool_vec_r
attentive_conv_layer2 = Attentive_Conv_for_Pair(
rng,
origin_input_tensor3=embed_input_l,
origin_input_tensor3_r=embed_input_r,
input_tensor3=conv_layer_gate_l.output_tensor3,
input_tensor3_r=conv_layer_gate_r.output_tensor3,
mask_matrix=sents_mask_l,
mask_matrix_r=sents_mask_r,
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[1]),
filter_shape_context=(hidden_size[0], 1, emb_size, 1),
W=conv_W2,
b=conv_b2,
W_context=conv_W2_context,
b_context=conv_b2_context)
attentive_sent_embeddings_l2 = attentive_conv_layer2.attentive_maxpool_vec_l
attentive_sent_embeddings_r2 = attentive_conv_layer2.attentive_maxpool_vec_r
"Batch normalization for the four output sentence representation vectors"
gamma = theano.shared(np.asarray(rng.uniform(
low=-1.0 / math.sqrt(hidden_size[0]),
high=1.0 / math.sqrt(hidden_size[0]),
size=(hidden_size[0])),
dtype=theano.config.floatX),
borrow=True)
beta = theano.shared(np.zeros((hidden_size[0]),
dtype=theano.config.floatX),
borrow=True)
bn_params = [gamma, beta]
bn_attentive_sent_embeddings_l = batch_normalization(
inputs=attentive_sent_embeddings_l,
gamma=gamma,
beta=beta,
mean=attentive_sent_embeddings_l.mean((0, ), keepdims=True),
std=attentive_sent_embeddings_l.std((0, ), keepdims=True),
mode='low_mem')
bn_attentive_sent_embeddings_r = batch_normalization(
inputs=attentive_sent_embeddings_r,
gamma=gamma,
beta=beta,
mean=attentive_sent_embeddings_r.mean((0, ), keepdims=True),
std=attentive_sent_embeddings_r.std((0, ), keepdims=True),
mode='low_mem')
bn_attentive_sent_embeddings_l2 = batch_normalization(
inputs=attentive_sent_embeddings_l2,
gamma=gamma,
beta=beta,
mean=attentive_sent_embeddings_l2.mean((0, ), keepdims=True),
std=attentive_sent_embeddings_l2.std((0, ), keepdims=True),
mode='low_mem')
bn_attentive_sent_embeddings_r2 = batch_normalization(
inputs=attentive_sent_embeddings_r2,
gamma=gamma,
beta=beta,
mean=attentive_sent_embeddings_r2.mean((0, ), keepdims=True),
std=attentive_sent_embeddings_r2.std((0, ), keepdims=True),
mode='low_mem')
"Before logistic regression layer, we insert a hidden layer. Now form input to HL classifier"
HL_layer_1_input = T.concatenate([
bn_attentive_sent_embeddings_l, bn_attentive_sent_embeddings_r,
bn_attentive_sent_embeddings_l + bn_attentive_sent_embeddings_r,
bn_attentive_sent_embeddings_l * bn_attentive_sent_embeddings_r,
bn_attentive_sent_embeddings_l2, bn_attentive_sent_embeddings_r2,
bn_attentive_sent_embeddings_l2 + bn_attentive_sent_embeddings_r2,
bn_attentive_sent_embeddings_l2 * bn_attentive_sent_embeddings_r2
],
axis=1)
HL_layer_1_input_size = 8 * hidden_size[0]
"Create hidden layer parameters"
HL_layer_1_W, HL_layer_1_b = create_HiddenLayer_para(
rng, HL_layer_1_input_size, hidden_size[1])
HL_layer_1_params = [HL_layer_1_W, HL_layer_1_b]
"Hidden Layer and batch norm to its output again"
HL_layer_1 = HiddenLayer(rng,
input=HL_layer_1_input,
n_in=HL_layer_1_input_size,
n_out=hidden_size[1],
W=HL_layer_1_W,
b=HL_layer_1_b,
activation=T.tanh)
gamma_HL = theano.shared(np.asarray(rng.uniform(
low=-1.0 / math.sqrt(hidden_size[1]),
high=1.0 / math.sqrt(hidden_size[1]),
size=(hidden_size[1])),
dtype=theano.config.floatX),
borrow=True)
beta_HL = theano.shared(np.zeros((hidden_size[1]),
dtype=theano.config.floatX),
borrow=True)
bn_params_HL = [gamma_HL, beta_HL]
bn_HL_output = batch_normalization(inputs=HL_layer_1.output,
gamma=gamma_HL,
beta=beta_HL,
mean=HL_layer_1.output.mean(
(0, ), keepdims=True),
std=HL_layer_1.output.std(
(0, ), keepdims=True),
mode='low_mem')
"Form input to LR classifier"
LR_input = T.concatenate([HL_layer_1_input, bn_HL_output], axis=1)
LR_input_size = HL_layer_1_input_size + hidden_size[1]
U_a = create_ensemble_para(rng, 3, LR_input_size) # (input_size, 3)
LR_b = theano.shared(value=np.zeros((3, ), dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
LR_para = [U_a, LR_b]
"Logistic Regression layer"
layer_LR = LogisticRegression(
rng,
input=normalize_matrix_col_wise(LR_input),
n_in=LR_input_size,
n_out=3,
W=U_a,
b=LR_b
) #basically it is a multiplication between weight matrix and input feature vector
loss = layer_LR.negative_log_likelihood(
labels
) #for classification task, we usually used negative log likelihood as loss, the lower the better.
params = [
init_embeddings
] + NN_para + LR_para + bn_params + HL_layer_1_params + bn_params_HL
cost = loss
"Use AdaGrad to update parameters"
updates = Gradient_Cost_Para(cost, params, learning_rate)
train_model = theano.function(
[sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels],
cost,
updates=updates,
allow_input_downcast=True,
on_unused_input='ignore')
dev_model = theano.function(
[sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels],
layer_LR.errors(labels),
allow_input_downcast=True,
on_unused_input='ignore')
test_model = theano.function(
[sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels],
layer_LR.errors(labels),
allow_input_downcast=True,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
n_train_batches = train_size / batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
n_dev_batches = dev_size / batch_size
dev_batch_start = list(
np.arange(n_dev_batches) * batch_size) + [dev_size - batch_size]
n_test_batches = test_size / batch_size
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
max_acc_dev = 0.0
max_acc_test = 0.0
cost_i = 0.0
train_indices = range(train_size)
while epoch < n_epochs:
epoch = epoch + 1
random.Random(100).shuffle(
train_indices
) #shuffle training set for each new epoch, is supposed to promote performance, but not garrenteed
iter_accu = 0
for batch_id in train_batch_start: #for each batch
# iter means how many batches have been run, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
train_id_batch = train_indices[batch_id:batch_id + batch_size]
cost_i += train_model(train_sents_l[train_id_batch],
train_masks_l[train_id_batch],
train_sents_r[train_id_batch],
train_masks_r[train_id_batch],
train_labels_store[train_id_batch])
if (epoch == 1 and iter % 1000 == 0) or (epoch >= 2
and iter % 5 == 0):
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
past_time = time.time()
dev_error_sum = 0.0
for dev_batch_id in dev_batch_start: # for each test batch
dev_error_i = dev_model(
dev_sents_l[dev_batch_id:dev_batch_id + batch_size],
dev_masks_l[dev_batch_id:dev_batch_id + batch_size],
dev_sents_r[dev_batch_id:dev_batch_id + batch_size],
dev_masks_r[dev_batch_id:dev_batch_id + batch_size],
dev_labels_store[dev_batch_id:dev_batch_id +
batch_size])
dev_error_sum += dev_error_i
dev_acc = 1.0 - dev_error_sum / (len(dev_batch_start))
if dev_acc > max_acc_dev:
max_acc_dev = dev_acc
print '\tcurrent dev_acc:', dev_acc, ' ; ', '\tmax_dev_acc:', max_acc_dev
'''
best dev model, test
'''
error_sum = 0.0
for test_batch_id in test_batch_start: # for each test batch
error_i = test_model(
test_sents_l[test_batch_id:test_batch_id +
batch_size],
test_masks_l[test_batch_id:test_batch_id +
batch_size],
test_sents_r[test_batch_id:test_batch_id +
batch_size],
test_masks_r[test_batch_id:test_batch_id +
batch_size],
test_labels_store[test_batch_id:test_batch_id +
batch_size])
error_sum += error_i
test_acc = 1.0 - error_sum / (len(test_batch_start))
if test_acc > max_acc_test:
max_acc_test = test_acc
print '\t\tcurrent test_acc:', test_acc, ' ; ', '\t\t\t\t\tmax_test_acc:', max_acc_test
else:
print '\tcurrent dev_acc:', dev_acc, ' ; ', '\tmax_dev_acc:', max_acc_dev
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
return max_acc_test
def evaluate_lenet5(learning_rate=0.02, 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
