def __init__(self,
batch_size,
num_unroll_steps,
embeddings,
embedding_size,
rnn_size,
num_rnn_layers,
max_grad_norm,
attention_matrix_size,
loss_ratio,
l2_reg_lambda=0.0,
adjust_weight=False,
label_weight=[],
is_training=True,
m=0.1):
# define input variable
self.batch_size = batch_size
self.embeddings = embeddings
self.embedding_size = embedding_size
self.adjust_weight = adjust_weight
self.label_weight = label_weight
self.rnn_size = rnn_size
self.num_rnn_layers = num_rnn_layers
self.num_unroll_steps = num_unroll_steps
self.max_grad_norm = max_grad_norm
self.l2_reg_lambda = l2_reg_lambda
self.is_training = is_training
self.keep_prob = tf.placeholder(tf.float32, name="keep_drop")
self.lr = tf.Variable(0.0, trainable=False)
self.new_lr = tf.placeholder(tf.float32,
shape=[],
name="new_learning_rate")
self._lr_update = tf.assign(self.lr, self.new_lr)
self.ori_input_quests = tf.placeholder(
tf.int32, shape=[None, self.num_unroll_steps])
self.cand_input_quests = tf.placeholder(
tf.int32, shape=[None, self.num_unroll_steps])
self.neg_input_quests = tf.placeholder(
tf.int32, shape=[None, self.num_unroll_steps])
self.test_input_q = tf.placeholder(tf.int32,
shape=[None, self.num_unroll_steps],
name='test_q')
self.test_input_a = tf.placeholder(tf.int32,
shape=[None, self.num_unroll_steps],
name='test_a')
self.cat_ids = tf.placeholder(tf.int32, [None, CAT_NUMBER],
name='cat_ids')
#embedding layer
with tf.device("/cpu:0"), tf.name_scope("embedding_layer"):
W = tf.Variable(tf.to_float(self.embeddings),
trainable=True,
name="W")
ori_quests = tf.nn.embedding_lookup(W, self.ori_input_quests)
cand_quests = tf.nn.embedding_lookup(W, self.cand_input_quests)
neg_quests = tf.nn.embedding_lookup(W, self.neg_input_quests)
test_q = tf.nn.embedding_lookup(W, self.test_input_q)
test_a = tf.nn.embedding_lookup(W, self.test_input_a)
# run lstm without attention
with tf.variable_scope("LSTM_scope") as scope:
ori_q = biLSTM(ori_quests, self.rnn_size)
ori_q_feat = tf.nn.tanh(max_pooling(ori_q))
scope.reuse_variables()
cand_a = biLSTM(cand_quests, self.rnn_size)
neg_a = biLSTM(neg_quests, self.rnn_size)
cand_q_feat = tf.nn.tanh(max_pooling(cand_a))
neg_q_feat = tf.nn.tanh(max_pooling(neg_a))
test_q_out = biLSTM(test_q, self.rnn_size)
test_q_out = tf.nn.tanh(max_pooling(test_q_out))
test_a_out = biLSTM(test_a, self.rnn_size)
test_a_out = tf.nn.tanh(max_pooling(test_a_out))
# build LSTM network
# with tf.variable_scope("LSTM_scope") as scope:
# ori_q = biLSTM(ori_quests, self.rnn_size)
# #ori_q_feat = tf.nn.tanh(max_pooling(ori_q))
#
# scope.reuse_variables()
#
# cand_a = biLSTM(cand_quests, self.rnn_size)
# neg_a = biLSTM(neg_quests, self.rnn_size)
# #cand_q_feat = tf.nn.tanh(max_pooling(cand_a))
# #neg_q_feat = tf.nn.tanh(max_pooling(neg_a))
#
# test_q_out = biLSTM(test_q, self.rnn_size)
# #test_q_out = tf.nn.tanh(max_pooling(test_q_out))
# test_a_out = biLSTM(test_a, self.rnn_size)
# #test_a_out = tf.nn.tanh(max_pooling(test_a_out))
# with tf.name_scope("att_weight"):
# # attention params
# att_W = {
# 'Wam': tf.Variable(tf.truncated_normal([2 * self.rnn_size, attention_matrix_size], stddev=0.1)),
# 'Wqm': tf.Variable(tf.truncated_normal([2 * self.rnn_size, attention_matrix_size], stddev=0.1)),
# 'Wms': tf.Variable(tf.truncated_normal([attention_matrix_size, 1], stddev=0.1))
# }
# ori_q_feat, cand_q_feat = get_feature(ori_q, cand_a, att_W)
# ori_nq_feat, neg_q_feat = get_feature(ori_q, neg_a, att_W)
# test_q_out, test_a_out = get_feature(test_q_out, test_a_out, att_W)
# multitasking
with tf.name_scope("multitasking"):
feature_size = int(ori_q_feat.get_shape()[1])
w = tf.get_variable(name='weights',
shape=(feature_size, CAT_NUMBER),
initializer=tf.random_normal_initializer())
b = tf.get_variable(name='bias',
shape=(1, CAT_NUMBER),
initializer=tf.zeros_initializer())
# positive_qa = tf.concat([out_ori,out_cand],1,name="embedding_for_multitask")
logits = tf.matmul(ori_q_feat, w) + b
entropy = tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=self.cat_ids, name='loss')
loss_multitask = tf.reduce_mean(entropy)
# acc
self.ori_cand_score = feature2cos_sim(ori_q_feat, cand_q_feat)
self.ori_neg_score = feature2cos_sim(ori_q_feat, neg_q_feat)
loss_origin, self.acc = cal_loss_and_acc(self.ori_cand_score,
self.ori_neg_score, m)
self.loss = loss_origin * (1 -
loss_ratio) + loss_multitask * loss_ratio
self.test_q_a = feature2cos_sim(test_q_out, test_a_out)
#multitasking_acc
with tf.name_scope("multi_acc"):
self.preds = tf.nn.softmax(logits)
self.correct_preds = tf.equal(tf.argmax(self.preds, 1),
tf.argmax(self.cat_ids, 1))
self.multi_acc = tf.reduce_sum(
tf.cast(self.correct_preds, tf.float32))
def __init__(self, batch_size, quest_len, answer_len, embeddings, embedding_size, rnn_size, num_rnn_layers, max_grad_norm,loss_ratio, l2_reg_lambda=0.0, adjust_weight=False,label_weight=[],is_training=True,m=0.1):
# define input variable
self.batch_size = batch_size
self.embeddings = embeddings
self.embedding_size = embedding_size
self.adjust_weight = adjust_weight
self.label_weight = label_weight
self.rnn_size = rnn_size
self.num_rnn_layers = num_rnn_layers
self.quest_len = quest_len
self.answer_len = answer_len
self.max_grad_norm = max_grad_norm
self.l2_reg_lambda = l2_reg_lambda
self.is_training = is_training
self.keep_prob = tf.placeholder(tf.float32, name="keep_drop")
self.lr = tf.Variable(0.0,trainable=False)
self.new_lr = tf.placeholder(tf.float32, shape=[],name="new_learning_rate")
self._lr_update = tf.assign(self.lr, self.new_lr)
self.ori_input_quests = tf.placeholder(tf.int32, shape=[None, self.quest_len], name="ori_quest")
self.cand_input_quests = tf.placeholder(tf.int32, shape=[None, self.answer_len], name="cand_quest")
self.neg_input_quests = tf.placeholder(tf.int32, shape=[None, self.answer_len], name="neg_quest")
self.test_input_q = tf.placeholder(tf.int32, shape=[None, self.quest_len], name="test_input_q")
self.test_input_a = tf.placeholder(tf.int32, shape=[None, self.answer_len], name="test_input_a")
self.cat_ids = tf.placeholder(tf.int32, [None, CAT_NUMBER], name='cat_ids')
#embedding layer
with tf.device("/cpu:0"),tf.name_scope("embedding_layer"):
W = tf.Variable(tf.to_float(self.embeddings), trainable=True, name="W")
ori_quests =tf.nn.embedding_lookup(W, self.ori_input_quests)
cand_quests =tf.nn.embedding_lookup(W, self.cand_input_quests)
neg_quests =tf.nn.embedding_lookup(W, self.neg_input_quests)
test_quest =tf.nn.embedding_lookup(W, self.test_input_q)
test_answer =tf.nn.embedding_lookup(W, self.test_input_a)
#ori_quests = tf.nn.dropout(ori_quests, self.keep_prob)
#cand_quests = tf.nn.dropout(cand_quests, self.keep_prob)
#neg_quests = tf.nn.dropout(neg_quests, self.keep_prob)
#build LSTM network
with tf.variable_scope("LSTM_scope", reuse=None):
ori_q = biLSTM(ori_quests, self.rnn_size)
with tf.variable_scope("LSTM_scope", reuse=True):
cand_a = biLSTM(cand_quests, self.rnn_size)
neg_a = biLSTM(neg_quests, self.rnn_size)
test_q = biLSTM(test_quest, self.rnn_size)
test_a = biLSTM(test_answer, self.rnn_size)
#----------------------------- cal attention -------------------------------
with tf.variable_scope("attention", reuse=None) as scope:
U = tf.get_variable("U", [2 * self.rnn_size, 2 * rnn_size], initializer=tf.truncated_normal_initializer(stddev=0.1))
G = tf.matmul(tf.matmul(ori_q, tf.tile(tf.expand_dims(U, 0), [batch_size, 1, 1])), cand_a, adjoint_b=True)
delta_q = tf.nn.softmax(tf.reduce_max(G, 2))
delta_a = tf.nn.softmax(tf.reduce_max(G, 1))
neg_G = tf.matmul(tf.matmul(ori_q, tf.tile(tf.expand_dims(U, 0), [batch_size, 1, 1])), neg_a, adjoint_b=True)
delta_neg_q = tf.nn.softmax(tf.reduce_max(neg_G, 2))
delta_neg_a = tf.nn.softmax(tf.reduce_max(neg_G, 1))
with tf.variable_scope("attention", reuse=True) as scope:
test_G = tf.matmul(tf.matmul(test_q, tf.tile(tf.expand_dims(U, 0), [batch_size, 1, 1])), test_a, adjoint_b=True)
delta_test_q = tf.nn.softmax(tf.reduce_max(test_G, 2))
delta_test_a = tf.nn.softmax(tf.reduce_max(test_G, 1))
#-------------------------- recalculate lstm output -------------------------
#ori_q_feat = tf.squeeze(tf.matmul(ori_q, tf.reshape(delta_q, [-1, self.quest_len, 1]), adjoint_a=True))
#cand_q_feat = tf.squeeze(tf.matmul(cand_a, tf.reshape(delta_a, [-1, self.answer_len, 1]), adjoint_a=True))
#neg_ori_q_feat = tf.squeeze(tf.matmul(ori_q, tf.reshape(delta_neg_q, [-1, self.quest_len, 1]), adjoint_a=True))
#neg_q_feat = tf.squeeze(tf.matmul(neg_a, tf.reshape(delta_neg_a, [-1, self.answer_len, 1]), adjoint_a=True))
#test_q_out = tf.squeeze(tf.matmul(test_q, tf.reshape(delta_test_q, [-1, self.quest_len, 1]), adjoint_a=True))
#test_a_out = tf.squeeze(tf.matmul(test_a, tf.reshape(delta_test_a, [-1, self.answer_len, 1]), adjoint_a=True))
ori_q_feat = max_pooling(tf.multiply(ori_q, tf.reshape(delta_q, [-1, self.quest_len, 1])))
cand_q_feat = max_pooling(tf.multiply(cand_a, tf.reshape(delta_a, [-1, self.answer_len, 1])))
neg_ori_q_feat = max_pooling(tf.multiply(ori_q, tf.reshape(delta_neg_q, [-1, self.quest_len, 1])))
neg_q_feat = max_pooling(tf.multiply(neg_a, tf.reshape(delta_neg_a, [-1, self.answer_len, 1])))
test_q_out = max_pooling(tf.multiply(test_q, tf.reshape(delta_test_q, [-1, self.quest_len, 1])))
test_a_out = max_pooling(tf.multiply(test_a, tf.reshape(delta_test_a, [-1, self.answer_len, 1])))
#-------------------------- recalculate lstm output end ---------------------
# dropout
#self.out_ori = tf.nn.dropout(self.out_ori, self.keep_prob)
#self.out_cand = tf.nn.dropout(self.out_cand, self.keep_prob)
#self.out_neg = tf.nn.dropout(self.out_neg, self.keep_prob)
# multitasking
with tf.name_scope("multitasking"):
feature_size = int(ori_q_feat.get_shape()[1])
fc1 = tf.layers.dense(ori_q_feat, feature_size * 2, activation=tf.nn.relu, name='fc1')
fc2 = tf.layers.dense(fc1, feature_size, activation=tf.nn.relu, name='fc2')
logits = tf.layers.dense(fc2, CAT_NUMBER, activation=tf.nn.sigmoid)
# feature_size = int(ori_q_feat.get_shape()[1])
# w = tf.get_variable(name='weights', shape=(feature_size, CAT_NUMBER, initializer=tf.random_normal_initializer())
# b = tf.get_variable(name='bias', shape=(1, CAT_NUMBER), initializer=tf.zeros_initializer())
# positive_qa = tf.concat([out_ori,out_cand],1,name="embedding_for_multitask")
# logits = tf.matmul(ori_q_feat, w) + b
entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=self.cat_ids, name='loss')
loss_multitask = tf.reduce_mean(entropy)
# acc
self.ori_cand_score = feature2cos_sim(ori_q_feat, cand_q_feat)
self.ori_neg_score = feature2cos_sim(ori_q_feat, neg_q_feat)
loss_origin, self.acc = cal_loss_and_acc(self.ori_cand_score, self.ori_neg_score, m)
self.loss = loss_origin * (1 - loss_ratio) + loss_multitask * loss_ratio
self.test_q_a = feature2cos_sim(test_q_out, test_a_out)
# multitasking_acc
with tf.name_scope("multi_acc"):
self.preds = tf.nn.softmax(logits)
self.correct_preds = tf.equal(tf.argmax(self.preds, 1), tf.argmax(self.cat_ids, 1))
self.multi_acc = tf.reduce_sum(tf.cast(self.correct_preds, tf.float32))
def __init__(self,
batch_size,
num_unroll_steps,
embeddings,
embedding_size,
rnn_size,
num_rnn_layers,
max_grad_norm,
attention_matrix_size,
l2_reg_lambda=0.0,
adjust_weight=False,
label_weight=[],
is_training=True):
"""
LSTM-BASED DEEP LEARNING MODELS FOR NON-FACTOID ANSWER SELECTION
"""
# define input variable
self.batch_size = batch_size
self.embeddings = embeddings
self.embedding_size = embedding_size
self.adjust_weight = adjust_weight
self.label_weight = label_weight
self.rnn_size = rnn_size
self.num_rnn_layers = num_rnn_layers
self.num_unroll_steps = num_unroll_steps
self.max_grad_norm = max_grad_norm
self.l2_reg_lambda = l2_reg_lambda
self.is_training = is_training
self.keep_prob = tf.placeholder(tf.float32, name="keep_drop")
self.lr = tf.Variable(0.0, trainable=False)
self.new_lr = tf.placeholder(tf.float32,
shape=[],
name="new_learning_rate")
self._lr_update = tf.assign(self.lr, self.new_lr)
self.ori_input_quests = tf.placeholder(
tf.int32, shape=[None, self.num_unroll_steps])
self.cand_input_quests = tf.placeholder(
tf.int32, shape=[None, self.num_unroll_steps])
self.neg_input_quests = tf.placeholder(
tf.int32, shape=[None, self.num_unroll_steps])
self.test_input_q = tf.placeholder(tf.int32,
shape=[None, self.num_unroll_steps])
self.test_input_a = tf.placeholder(tf.int32,
shape=[None, self.num_unroll_steps])
#embedding layer
with tf.device("/cpu:0"), tf.name_scope("embedding_layer"):
W = tf.Variable(tf.to_float(self.embeddings),
trainable=True,
name="W")
ori_quests = tf.nn.embedding_lookup(W, self.ori_input_quests)
cand_quests = tf.nn.embedding_lookup(W, self.cand_input_quests)
neg_quests = tf.nn.embedding_lookup(W, self.neg_input_quests)
test_q = tf.nn.embedding_lookup(W, self.test_input_q)
test_a = tf.nn.embedding_lookup(W, self.test_input_a)
#build LSTM network
U = tf.Variable(tf.truncated_normal(
[2 * self.rnn_size, self.embedding_size], stddev=0.1),
name="U")
with tf.variable_scope("LSTM_scope", reuse=None):
ori_q = biLSTM(ori_quests, self.rnn_size)
ori_q_feat = tf.nn.tanh(max_pooling(ori_q))
with tf.variable_scope("LSTM_scope", reuse=True):
cand_att_weight = tf.sigmoid(
tf.batch_matmul(
cand_quests,
tf.reshape(
tf.expand_dims(tf.batch_matmul(ori_q_feat, U), 1),
[-1, self.embedding_size, 1])))
neg_att_weight = tf.sigmoid(
tf.batch_matmul(
neg_quests,
tf.reshape(
tf.expand_dims(tf.batch_matmul(ori_q_feat, U), 1),
[-1, self.embedding_size, 1])))
cand_a = biLSTM(
tf.mul(cand_quests,
tf.tile(cand_att_weight, [1, 1, self.embedding_size])),
self.rnn_size)
neg_a = biLSTM(
tf.mul(neg_quests,
tf.tile(neg_att_weight, [1, 1, self.embedding_size])),
self.rnn_size)
cand_q_feat = tf.nn.tanh(max_pooling(cand_a))
neg_q_feat = tf.nn.tanh(max_pooling(neg_a))
test_q_out = biLSTM(test_q, self.rnn_size)
test_q_out = tf.nn.tanh(max_pooling(test_q_out))
test_att_weight = tf.sigmoid(
tf.batch_matmul(
test_a,
tf.reshape(
tf.expand_dims(tf.batch_matmul(test_q_out, U), 1),
[-1, self.embedding_size, 1])))
test_a_out = biLSTM(
tf.mul(test_a,
tf.tile(test_att_weight, [1, 1, self.embedding_size])),
self.rnn_size)
test_a_out = tf.nn.tanh(max_pooling(test_a_out))
self.ori_cand = feature2cos_sim(ori_q_feat, cand_q_feat)
self.ori_neg = feature2cos_sim(ori_q_feat, neg_q_feat)
self.loss, self.acc = cal_loss_and_acc(self.ori_cand, self.ori_neg)
self.test_q_a = feature2cos_sim(test_q_out, test_a_out)
def __init__(self,
batch_size,
num_unroll_steps,
embeddings,
embedding_size,
rnn_size,
num_rnn_layers,
max_grad_norm,
attention_matrix_size,
loss_ratio,
l2_reg_lambda=0.0,
adjust_weight=False,
label_weight=[],
is_training=True,
m=0.1):
"""
LSTM-BASED DEEP LEARNING MODELS FOR NON-FACTOID ANSWER SELECTION
"""
# define input variable
self.batch_size = batch_size
self.embeddings = embeddings
self.embedding_size = embedding_size
self.adjust_weight = adjust_weight
self.label_weight = label_weight
self.rnn_size = rnn_size
self.num_rnn_layers = num_rnn_layers
self.num_unroll_steps = num_unroll_steps
self.max_grad_norm = max_grad_norm
self.l2_reg_lambda = l2_reg_lambda
self.is_training = is_training
self.keep_prob = tf.placeholder(tf.float32, name="keep_drop")
self.lr = tf.Variable(0.0, trainable=False)
self.new_lr = tf.placeholder(tf.float32,
shape=[],
name="new_learning_rate")
self._lr_update = tf.assign(self.lr, self.new_lr)
self.ori_input_quests = tf.placeholder(
tf.int32, shape=[None, self.num_unroll_steps])
self.cand_input_quests = tf.placeholder(
tf.int32, shape=[None, self.num_unroll_steps])
self.neg_input_quests = tf.placeholder(
tf.int32, shape=[None, self.num_unroll_steps])
self.test_input_q = tf.placeholder(tf.int32,
shape=[None, self.num_unroll_steps])
self.test_input_a = tf.placeholder(tf.int32,
shape=[None, self.num_unroll_steps])
self.cat_ids = tf.placeholder(tf.int32, [None, CAT_NUMBER],
name='cat_ids')
#embedding layer
with tf.device("/cpu:0"), tf.name_scope("embedding_layer"):
W = tf.Variable(tf.to_float(self.embeddings),
trainable=True,
name="W")
ori_quests = tf.nn.embedding_lookup(W, self.ori_input_quests)
cand_quests = tf.nn.embedding_lookup(W, self.cand_input_quests)
neg_quests = tf.nn.embedding_lookup(W, self.neg_input_quests)
test_q = tf.nn.embedding_lookup(W, self.test_input_q)
test_a = tf.nn.embedding_lookup(W, self.test_input_a)
#build LSTM network
U = tf.Variable(tf.truncated_normal(
[2 * self.rnn_size, self.embedding_size], stddev=0.1),
name="U")
with tf.variable_scope("LSTM_scope", reuse=None):
ori_q = biLSTM(ori_quests, self.rnn_size)
ori_q_feat = tf.nn.tanh(max_pooling(ori_q))
with tf.variable_scope("LSTM_scope", reuse=True):
cand_att_weight = tf.sigmoid(
tf.matmul(
cand_quests,
tf.reshape(tf.expand_dims(tf.matmul(ori_q_feat, U), 1),
[-1, self.embedding_size, 1])))
neg_att_weight = tf.sigmoid(
tf.matmul(
neg_quests,
tf.reshape(tf.expand_dims(tf.matmul(ori_q_feat, U), 1),
[-1, self.embedding_size, 1])))
cand_a = biLSTM(
tf.multiply(
cand_quests,
tf.tile(cand_att_weight, [1, 1, self.embedding_size])),
self.rnn_size)
neg_a = biLSTM(
tf.multiply(
neg_quests,
tf.tile(neg_att_weight, [1, 1, self.embedding_size])),
self.rnn_size)
cand_q_feat = tf.nn.tanh(max_pooling(cand_a))
neg_q_feat = tf.nn.tanh(max_pooling(neg_a))
test_q_out = biLSTM(test_q, self.rnn_size)
test_q_out = tf.nn.tanh(max_pooling(test_q_out))
test_att_weight = tf.sigmoid(
tf.matmul(
test_a,
tf.reshape(tf.expand_dims(tf.matmul(test_q_out, U), 1),
[-1, self.embedding_size, 1])))
test_a_out = biLSTM(
tf.multiply(
test_a,
tf.tile(test_att_weight, [1, 1, self.embedding_size])),
self.rnn_size)
test_a_out = tf.nn.tanh(max_pooling(test_a_out))
# multitasking
with tf.name_scope("multitasking"):
feature_size = int(ori_q_feat.get_shape()[1])
fc1 = tf.layers.dense(ori_q_feat,
feature_size * 2,
activation=tf.nn.relu,
name='fc1')
fc2 = tf.layers.dense(fc1,
feature_size,
activation=tf.nn.relu,
name='fc2')
logits = tf.layers.dense(fc2, CAT_NUMBER, activation=tf.nn.sigmoid)
# feature_size = int(ori_q_feat.get_shape()[1])
# w = tf.get_variable(name='weights', shape=(feature_size, CAT_NUMBER, initializer=tf.random_normal_initializer())
# b = tf.get_variable(name='bias', shape=(1, CAT_NUMBER), initializer=tf.zeros_initializer())
# positive_qa = tf.concat([out_ori,out_cand],1,name="embedding_for_multitask")
# logits = tf.matmul(ori_q_feat, w) + b
entropy = tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=self.cat_ids, name='loss')
loss_multitask = tf.reduce_mean(entropy)
# acc
self.ori_cand_score = feature2cos_sim(ori_q_feat, cand_q_feat)
self.ori_neg_score = feature2cos_sim(ori_q_feat, neg_q_feat)
loss_origin, self.acc = cal_loss_and_acc(self.ori_cand_score,
self.ori_neg_score, m)
self.loss = loss_origin * (1 -
loss_ratio) + loss_multitask * loss_ratio
self.test_q_a = feature2cos_sim(test_q_out, test_a_out)
# multitasking_acc
with tf.name_scope("multi_acc"):
self.preds = tf.nn.softmax(logits)
self.correct_preds = tf.equal(tf.argmax(self.preds, 1),
tf.argmax(self.cat_ids, 1))
self.multi_acc = tf.reduce_sum(
tf.cast(self.correct_preds, tf.float32))
def assign_new_lr(self, session, lr_value):
session.run(self._lr_update, feed_dict={self.new_lr: lr_value})
def __init__(self,
batch_size,
num_unroll_steps,
embeddings,
embedding_size,
rnn_size,
num_rnn_layers,
max_grad_norm,
l2_reg_lambda=0.0,
adjust_weight=False,
label_weight=[],
is_training=True):
# define input variable
self.batch_size = batch_size
self.embeddings = embeddings
self.embedding_size = embedding_size
self.adjust_weight = adjust_weight
self.label_weight = label_weight
self.rnn_size = rnn_size
self.num_rnn_layers = num_rnn_layers
self.num_unroll_steps = num_unroll_steps
self.max_grad_norm = max_grad_norm
self.l2_reg_lambda = l2_reg_lambda
self.is_training = is_training
self.keep_prob = tf.placeholder(tf.float32, name="keep_drop")
self.lr = tf.Variable(0.0, trainable=False)
self.new_lr = tf.placeholder(tf.float32,
shape=[],
name="new_learning_rate")
self._lr_update = tf.assign(self.lr, self.new_lr)
self.ori_input_quests = tf.placeholder(
tf.int32, shape=[None, self.num_unroll_steps])
self.cand_input_quests = tf.placeholder(
tf.int32, shape=[None, self.num_unroll_steps])
self.neg_input_quests = tf.placeholder(
tf.int32, shape=[None, self.num_unroll_steps])
self.test_input_q = tf.placeholder(tf.int32,
shape=[None, self.num_unroll_steps])
self.test_input_a = tf.placeholder(tf.int32,
shape=[None, self.num_unroll_steps])
#embedding layer
with tf.device("/cpu:0"), tf.name_scope("embedding_layer"):
W = tf.Variable(tf.to_float(self.embeddings),
trainable=True,
name="W")
ori_quests = tf.nn.embedding_lookup(W, self.ori_input_quests)
cand_quests = tf.nn.embedding_lookup(W, self.cand_input_quests)
neg_quests = tf.nn.embedding_lookup(W, self.neg_input_quests)
test_q = tf.nn.embedding_lookup(W, self.test_input_q)
test_a = tf.nn.embedding_lookup(W, self.test_input_a)
#build LSTM network
with tf.variable_scope("LSTM_scope", reuse=None):
ori_q = biLSTM(ori_quests, self.rnn_size)
ori_q_feat = tf.nn.tanh(max_pooling(ori_q))
with tf.variable_scope("LSTM_scope", reuse=True):
cand_a = biLSTM(cand_quests, self.rnn_size)
neg_a = biLSTM(neg_quests, self.rnn_size)
cand_q_feat = tf.nn.tanh(max_pooling(cand_a))
neg_q_feat = tf.nn.tanh(max_pooling(neg_a))
test_q_out = biLSTM(test_q, self.rnn_size)
test_q_out = tf.nn.tanh(max_pooling(test_q_out))
test_a_out = biLSTM(test_a, self.rnn_size)
test_a_out = tf.nn.tanh(max_pooling(test_a_out))
self.ori_cand = feature2cos_sim(ori_q_feat, cand_q_feat)
self.ori_neg = feature2cos_sim(ori_q_feat, neg_q_feat)
self.loss, self.acc = cal_loss_and_acc(self.ori_cand, self.ori_neg)
self.test_q_a = feature2cos_sim(test_q_out, test_a_out)