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model.py
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import tensorflow as tf
from layers import dropout, native_gru, cudnn_gru, ptr_layer, summ, dot_attention
from layers import native_sru
class Model(object):
def __init__(self, config, batch, word_mat=None, char_mat=None, trainable=True, opt=True):
self.config = config
self.trainable = trainable
self.global_step = tf.get_variable(
"global_step", shape=[], dtype=tf.int32,
initializer=tf.zeros_initializer(), trainable=False)
self.c, self.q, self.ch, self.qh, self.y1, self.y2, self.qa_id = batch.get_next()
self.is_train = tf.get_variable(
"is_train", shape=[], dtype=tf.bool, trainable=False)
self.word_mat = tf.get_variable(
"word_mat", dtype=tf.float32, initializer=tf.constant(word_mat, dtype=tf.float32),
trainable=False)
self.char_mat = tf.get_variable(
"char_mat", dtype=tf.float32, initializer=tf.constant(char_mat, dtype=tf.float32),
trainable=True)
self.c_mask = tf.cast(self.c, tf.bool)
self.q_mask = tf.cast(self.q, tf.bool)
self.c_len = tf.reduce_sum(tf.cast(self.c_mask, tf.int32), axis=1)
self.q_len = tf.reduce_sum(tf.cast(self.q_mask, tf.int32), axis=1)
if opt:
N, CL = config.batch_size, config.char_limit
self.c_maxlen = tf.reduce_max(self.c_len)
self.q_maxlen = tf.reduce_max(self.q_len)
self.c = tf.slice(self.c, [0, 0], [N, self.c_maxlen])
self.q = tf.slice(self.q, [0, 0], [N, self.q_maxlen])
self.c_mask = tf.slice(self.c_mask, [0, 0], [N, self.c_maxlen])
self.q_mask = tf.slice(self.q_mask, [0, 0], [N, self.q_maxlen])
self.ch = tf.slice(self.ch, [0, 0, 0], [N, self.c_maxlen, CL])
self.qh = tf.slice(self.qh, [0, 0, 0], [N, self.q_maxlen, CL])
self.y1 = tf.slice(self.y1, [0, 0], [N, self.c_maxlen])
self.y2 = tf.slice(self.y2, [0, 0], [N, self.c_maxlen])
else:
self.c_maxlen = config.para_limit
self.q_maxlen = config.ques_limit
self.ch_len = tf.reshape(tf.reduce_sum(
tf.cast(tf.cast(self.ch, tf.bool), tf.int32), axis=2), [-1])
self.qh_len = tf.reshape(tf.reduce_sum(
tf.cast(tf.cast(self.qh, tf.bool), tf.int32), axis=2), [-1])
self.forward()
if self.trainable:
self.lr = tf.get_variable(
"lr", shape=[], dtype=tf.float32, trainable=False)
self.opt = tf.train.AdadeltaOptimizer(learning_rate=self.lr, epsilon=1e-6)
grads = self.opt.compute_gradients(self.loss)
gradients, variables = zip(*grads)
capped_grads, _ = tf.clip_by_global_norm(gradients, config.grad_clip)
self.train_op = self.opt.apply_gradients(
zip(capped_grads, variables), global_step=self.global_step)
def forward(self):
# in: c, q, c_mask, q_mask, ch, qh, y1, y2
# out: yp1, yp2, loss
config = self.config
N, PL, QL, CL, d, dc, dg = config.batch_size, self.c_maxlen, self.q_maxlen, config.char_limit, config.hidden, config.char_dim, config.char_hidden
gru = cudnn_gru if config.use_cudnn else native_gru
gru = native_sru if config.use_sru else gru
with tf.variable_scope('emb'):
with tf.variable_scope('char'):
ch_emb = tf.reshape(
tf.nn.embedding_lookup(self.char_mat, self.ch), [N * PL, CL, dc])
qh_emb = tf.reshape(
tf.nn.embedding_lookup(self.char_mat, self.qh), [N * QL, CL, dc])
ch_emb = dropout(ch_emb, keep_prob=config.keep_prob, is_train=self.is_train)
qh_emb = dropout(qh_emb, keep_prob=config.keep_prob, is_train=self.is_train)
cell_fw = tf.contrib.rnn.GRUCell(dg)
cell_bw = tf.contrib.rnn.GRUCell(dg)
_, (state_fw, state_bw) = tf.nn.bidirectional_dynamic_rnn(
cell_fw, cell_bw, ch_emb, self.ch_len, dtype=tf.float32)
ch_emb = tf.concat([state_fw, state_bw], axis=1)
_, (state_fw, state_bw) = tf.nn.bidirectional_dynamic_rnn(
cell_fw, cell_bw, qh_emb, self.qh_len, dtype=tf.float32)
qh_emb = tf.concat([state_fw, state_bw], axis=1)
qh_emb = tf.reshape(qh_emb, [N, QL, 2 * dg])
ch_emb = tf.reshape(ch_emb, [N, PL, 2 * dg])
with tf.variable_scope('word'):
c_emb = tf.nn.embedding_lookup(self.word_mat, self.c)
q_emb = tf.nn.embedding_lookup(self.word_mat, self.q)
c_emb = tf.concat([c_emb, ch_emb], axis=2)
q_emb = tf.concat([q_emb, qh_emb], axis=2)
with tf.variable_scope('encoding'):
rnn = gru(num_layers=3, num_units=d,
batch_size=N, input_size=c_emb.get_shape().as_list()[-1],
keep_prob=config.keep_prob, is_train=self.is_train)
c = rnn(c_emb, seq_len=self.c_len)
tf.get_variable_scope().reuse_variables()
q = rnn(q_emb, seq_len=self.q_len)
with tf.variable_scope('attention'):
qc_att = dot_attention(inputs=c, memory=q,
hidden_size=d, mask=self.q_mask,
keep_prob=config.keep_prob,
is_train=self.is_train)
rnn = gru(num_layers=1, num_units=d,
batch_size=N, input_size=qc_att.get_shape().as_list()[-1],
keep_prob=config.keep_prob, is_train=self.is_train)
att = rnn(qc_att, seq_len=self.c_len)
with tf.variable_scope('match'):
self_att = dot_attention(inputs=att, memory=att,
hidden_size=d, mask=self.c_mask,
keep_prob=config.keep_prob,
is_train=self.is_train)
rnn = gru(num_layers=1, num_units=d,
batch_size=N, input_size=self_att.get_shape().as_list()[-1],
keep_prob=config.keep_prob, is_train=self.is_train)
match = rnn(self_att, seq_len=self.c_len)
with tf.variable_scope('pointer'):
init = summ(q[:,:,-2 * d:], d, mask=self.q_mask,
keep_prob=config.keep_prob, is_train=self.is_train)
pointer = ptr_layer(batch_size=N,
hidden_size=init.get_shape().as_list()[-1],
keep_prob=config.keep_prob,
is_train=self.is_train)
logits1, logits2 = pointer(init, match, d, self.c_mask)
with tf.variable_scope('predict'):
outer = tf.matmul(tf.expand_dims(tf.nn.softmax(logits1), axis=2),
tf.expand_dims(tf.nn.softmax(logits2), axis=1))
outer = tf.matrix_band_part(outer, 0, 15)
self.yp1 = tf.argmax(tf.reduce_max(outer, axis=2), axis=1)
self.yp2 = tf.argmax(tf.reduce_max(outer, axis=1), axis=1)
# loss1 = tf.nn.softmax_cross_entropy_with_logits_v2(
# logits=logits1, labels=tf.stop_gradient(self.y1))
loss1 = tf.nn.softmax_cross_entropy_with_logits(
logits=logits1, labels=tf.stop_gradient(self.y1))
# loss2 = tf.nn.softmax_cross_entropy_with_logits_v2(
# logits=logits2, labels=tf.stop_gradient(self.y2))
loss2 = tf.nn.softmax_cross_entropy_with_logits(
logits=logits2, labels=tf.stop_gradient(self.y2))
self.loss = tf.reduce_mean(loss1 + loss2)
def get_loss(self):
return self.loss
def get_global_step(self):
return self.global_step
RNet = Model