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model.py
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model.py
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import tensorflow as tf
from tensorflow.contrib.framework import arg_scope
from layers import *
class Model(object):
def __init__(self, config,
inputs, labels, enc_seq_length, dec_seq_length, mask,
reuse=False, is_critic=False):
self.task = config.task
self.debug = config.debug
self.config = config
self.input_dim = config.input_dim
self.hidden_dim = config.hidden_dim
self.num_layers = config.num_layers
self.max_enc_length = config.max_enc_length
self.max_dec_length = config.max_dec_length
self.num_glimpse = config.num_glimpse
self.init_min_val = config.init_min_val
self.init_max_val = config.init_max_val
self.initializer = \
tf.random_uniform_initializer(self.init_min_val, self.init_max_val)
self.use_terminal_symbol = config.use_terminal_symbol
self.lr_start = config.lr_start
self.lr_decay_step = config.lr_decay_step
self.lr_decay_rate = config.lr_decay_rate
self.max_grad_norm = config.max_grad_norm
self.layer_dict = {}
##############
# inputs
##############
self.is_training = tf.placeholder_with_default(
tf.constant(False, dtype=tf.bool),
shape=(), name='is_training'
)
self.enc_inputs, self.dec_targets, self.enc_seq_length, self.dec_seq_length, self.mask = \
smart_cond(
self.is_training,
lambda: (inputs['train'], labels['train'], enc_seq_length['train'],
dec_seq_length['train'], mask['train']),
lambda: (inputs['test'], labels['test'], enc_seq_length['test'],
dec_seq_length['test'], mask['test'])
)
if self.use_terminal_symbol:
self.dec_seq_length += 1 # terminal symbol
self._build_model()
self._build_steps()
if not reuse:
self._build_optim()
self.train_summary = tf.summary.merge([
tf.summary.scalar("train/total_loss", self.total_loss),
tf.summary.scalar("train/lr", self.lr),
])
self.test_summary = tf.summary.merge([
tf.summary.scalar("test/total_loss", self.total_loss),
])
def _build_steps(self):
def run(sess, fetch, feed_dict, summary_writer, summary):
fetch['step'] = self.global_step
if summary is not None:
fetch['summary'] = summary
result = sess.run(fetch)
if summary_writer is not None:
summary_writer.add_summary(result['summary'], result['step'])
summary_writer.flush()
return result
def train(sess, fetch, summary_writer):
return run(sess, fetch, feed_dict={},
summary_writer=summary_writer, summary=self.train_summary)
def test(sess, fetch, summary_writer=None):
return run(sess, fetch, feed_dict={self.is_training: False},
summary_writer=summary_writer, summary=self.test_summary)
self.train = train
self.test = test
def _build_model(self):
tf.logging.info("Create a model..")
self.global_step = tf.Variable(0, trainable=False)
input_embed = tf.get_variable(
"input_embed", [1, self.input_dim, self.hidden_dim],
initializer=self.initializer)
with tf.variable_scope("encoder"):
self.embeded_enc_inputs = tf.nn.conv1d(
self.enc_inputs, input_embed, 1, "VALID")
batch_size = tf.shape(self.enc_inputs)[0]
with tf.variable_scope("encoder"):
self.enc_cell = LSTMCell(
self.hidden_dim,
initializer=self.initializer)
if self.num_layers > 1:
cells = [self.enc_cell] * self.num_layers
self.enc_cell = MultiRNNCell(cells)
self.enc_init_state = trainable_initial_state(
batch_size, self.enc_cell.state_size)
# self.encoder_outputs : [None, max_time, output_size]
self.enc_outputs, self.enc_final_states = tf.nn.dynamic_rnn(
self.enc_cell, self.embeded_enc_inputs,
self.enc_seq_length, self.enc_init_state)
self.first_decoder_input = tf.expand_dims(trainable_initial_state(
batch_size, self.hidden_dim, name="first_decoder_input"), 1)
if self.use_terminal_symbol:
# 0 index indicates terminal
self.enc_outputs = concat_v2(
[self.first_decoder_input, self.enc_outputs], axis=1)
with tf.variable_scope("decoder"):
self.idx_pairs = index_matrix_to_pairs(self.dec_targets)
self.embeded_dec_inputs = tf.stop_gradient(
tf.gather_nd(self.enc_outputs, self.idx_pairs))
if self.use_terminal_symbol:
tiled_zero_idxs = tf.tile(tf.zeros(
[1, 1], dtype=tf.int32), [batch_size, 1], name="tiled_zero_idxs")
self.dec_targets = concat_v2([self.dec_targets, tiled_zero_idxs], axis=1)
self.embeded_dec_inputs = concat_v2(
[self.first_decoder_input, self.embeded_dec_inputs], axis=1)
self.dec_cell = LSTMCell(
self.hidden_dim,
initializer=self.initializer)
if self.num_layers > 1:
cells = [self.dec_cell] * self.num_layers
self.dec_cell = MultiRNNCell(cells)
self.dec_pred_logits, _, _ = decoder_rnn(
self.dec_cell, self.embeded_dec_inputs,
self.enc_outputs, self.enc_final_states,
self.dec_seq_length, self.hidden_dim,
self.num_glimpse, batch_size, is_train=True,
initializer=self.initializer)
self.dec_pred_prob = tf.nn.softmax(
self.dec_pred_logits, 2, name="dec_pred_prob")
self.dec_pred = tf.argmax(
self.dec_pred_logits, 2, name="dec_pred")
with tf.variable_scope("decoder", reuse=True):
self.dec_inference_logits, _, _ = decoder_rnn(
self.dec_cell, self.first_decoder_input,
self.enc_outputs, self.enc_final_states,
self.dec_seq_length, self.hidden_dim,
self.num_glimpse, batch_size, is_train=False,
initializer=self.initializer,
max_length=self.max_dec_length + int(self.use_terminal_symbol))
self.dec_inference_prob = tf.nn.softmax(
self.dec_inference_logits, 2, name="dec_inference_logits")
self.dec_inference = tf.argmax(
self.dec_inference_logits, 2, name="dec_inference")
def _build_optim(self):
losses = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=self.dec_targets, logits=self.dec_pred_logits)
inference_losses = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=self.dec_targets, logits=self.dec_inference_logits)
def apply_mask(op):
length = tf.cast(op[:1], tf.int32)
loss = op[1:]
return tf.multiply(loss, tf.ones(length, dtype=tf.float32))
batch_loss = tf.div(
tf.reduce_sum(tf.multiply(losses, self.mask)),
tf.reduce_sum(self.mask), name="batch_loss")
batch_inference_loss = tf.div(
tf.reduce_sum(tf.multiply(losses, self.mask)),
tf.reduce_sum(self.mask), name="batch_inference_loss")
tf.losses.add_loss(batch_loss)
total_loss = tf.losses.get_total_loss()
self.total_loss = total_loss
self.target_cross_entropy_losses = losses
self.total_inference_loss = batch_inference_loss
self.lr = tf.train.exponential_decay(
self.lr_start, self.global_step, self.lr_decay_step,
self.lr_decay_rate, staircase=True, name="learning_rate")
optimizer = tf.train.AdamOptimizer(self.lr)
if self.max_grad_norm != None:
grads_and_vars = optimizer.compute_gradients(self.total_loss)
for idx, (grad, var) in enumerate(grads_and_vars):
if grad is not None:
grads_and_vars[idx] = (tf.clip_by_norm(grad, self.max_grad_norm), var)
self.optim = optimizer.apply_gradients(grads_and_vars, global_step=self.global_step)
else:
self.optim = optimizer.minimize(self.total_loss, global_step=self.global_step)