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NonStructured_Encoder.py.bak
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import tensorflow as tf
import numpy as np
import os
from libs import DropoutWrapper
class NonStructured_Encoder():
def __init__(self, sess, FLAGS, embed, num_units=None, scope="Sentence_Encoder"):
self.sess = sess
self.dim_embed_word = FLAGS.dim_embed_word
self.num_units = num_units if (num_units is not None) else FLAGS.num_units
self.num_layers = FLAGS.num_layers
self.train_keep_prob = FLAGS.keep_prob
self.fixed_noise = tf.placeholder(tf.int32)
self.keep_prob = tf.placeholder_with_default(1.0, ())
self.learning_rate = tf.placeholder(tf.float32)
if FLAGS.use_adam:
self.optimizer = tf.train.AdamOptimizer(self.learning_rate)
else:
self.optimizer = tf.train.GradientDescentOptimizer(self.learning_rate)
self.recurrent_noise_in, self.recurrent_noise_out, self.recurrent_noise = [], [], None
with tf.variable_scope(scope):
self._build_embedding(embed)
self._build_input()
self._build_encoders()
self.params = []
for var in tf.trainable_variables():
if var.name.find(os.path.join(tf.contrib.framework.get_name_scope(), scope)) == 0:
self.params.append(var)
self.grad_out = tf.gradients(
tf.concat([self.enc_text, self.enc_text_cont], axis=-1),
self.params,
tf.concat([self.grad_enc_text, self.grad_enc_text_cont], axis=-1)
)
self.grad_out[0] = tf.convert_to_tensor(self.grad_out[0])
self.grad_in = [
tf.placeholder(tf.float32, param.shape)
for param in self.params
]
self.train_op = self.optimizer.apply_gradients(zip(self.grad_in, self.params))
def _build_embedding(self, embed):
self.symbol2index = tf.contrib.lookup.MutableHashTable(
key_dtype=tf.string,
value_dtype=tf.int64,
default_value=0,
shared_name="in_table",
name="in_table",
checkpoint=True)
self.index2symbol = tf.contrib.lookup.MutableHashTable(
key_dtype=tf.int64,
value_dtype=tf.string,
default_value="UNK",
shared_name="out_table",
name="out_table",
checkpoint=True)
self.embed = tf.get_variable("word_embedding", dtype=tf.float32, initializer=embed)
def _build_input(self):
with tf.variable_scope("input"):
self.num_posts = tf.placeholder(tf.int32, (None,), "num_posts")
self.text_string = tf.placeholder(tf.string, (None, None, None), "text_string")
self.text = tf.nn.embedding_lookup(
self.embed, self.symbol2index.lookup(self.text_string))
self.text_len = tf.placeholder(tf.int32, (None, None,), "text_len")
def _build_encoders(self):
with tf.variable_scope("encoders"):
self.enc_text = self._build_encoder(
tf.reshape(
self.text,
[tf.shape(self.text)[0] * tf.shape(self.text)[1], tf.shape(self.text)[2], self.dim_embed_word]
),
tf.reshape(self.text_len, [-1]),
self.dim_embed_word,
True,
"enc_text"
)
self.enc_text_cont = tf.reshape(
self._build_encoder(
tf.reshape(
self.enc_text,
[tf.shape(self.text)[0], tf.shape(self.text)[1], self.num_units]
),
self.num_posts,
self.num_units,
False,
"enc_text_cont"
),
[-1, self.num_units]
)
self.grad_enc_text = tf.placeholder(tf.float32, self.enc_text.shape)
self.grad_enc_text_cont = tf.placeholder(tf.float32, self.enc_text_cont.shape)
def _build_encoder(self, inputs, length, input_size, use_biencoder, scope, reuse=None):
with tf.variable_scope(scope, reuse=reuse):
if use_biencoder:
cell_fw, cell_bw = self._build_biencoder_cell(input_size)
outputs, states = tf.nn.bidirectional_dynamic_rnn(
cell_fw=cell_fw,
cell_bw=cell_bw,
inputs=inputs,
sequence_length=length,
dtype=tf.float32
)
enc_state = []
for i in range(self.num_layers):
enc_state.append(tf.concat([states[0][i],states[1][i]], axis=-1))
return enc_state[-1]
else:
cell = self._build_cell(self.num_units, input_size)
outputs, states = tf.nn.dynamic_rnn(
cell=cell,
inputs=inputs,
sequence_length=length,
dtype=tf.float32
)
return outputs
def _build_cell(self, num_units, input_size):
cells = []
for i in range(self.num_layers):
self.recurrent_noise_in.append(tf.placeholder(tf.float32, (1, input_size)))
dropout = DropoutWrapper(
tf.contrib.rnn.GRUCell(num_units), self.keep_prob,
input_size=input_size, dtype=tf.float32,
noise_input=self.recurrent_noise_in[-1],
fixed_noise=self.fixed_noise
)
self.recurrent_noise_out.append(dropout.recurrent_input_noise)
cells.append(dropout)
return tf.contrib.rnn.MultiRNNCell(cells)
def _build_biencoder_cell(self, input_size):
cell_fw = self._build_cell(self.num_units / 2, input_size)
cell_bw = self._build_cell(self.num_units / 2, input_size)
return cell_fw, cell_bw
def initialize(self, vocab):
op_in = self.symbol2index.insert(
tf.constant(vocab), tf.constant(range(len(vocab)), dtype=tf.int64))
op_out = self.index2symbol.insert(
tf.constant(range(len(vocab)), dtype=tf.int64), tf.constant(vocab))
self.sess.run([op_in, op_out])
def format_data(self, data):
def padding(sent, l):
return sent + ["EOS"] + ["PAD"] * (l - len(sent) - 1)
length = 0
for dialog in data:
for text in dialog:
length = max(length, len(text))
length += 1
text_string, text_len = [], []
for dialog in data:
text_string.append([])
text_len.append([])
for text in dialog:
text_string[-1].append(padding(text, length))
text_len[-1].append(len(text) + 1)
return {
"text_string": np.array(text_string),
"text_len": np.array(text_len)
}
def get_gradients(self, data, num_posts, grad_enc_text, grad_enc_text_cont, buffered=False):
data = self.format_data(data)
input_feed = {
self.text_string: data["text_string"],
self.text_len: data["text_len"],
self.num_posts: num_posts,
self.grad_enc_text: grad_enc_text,
self.grad_enc_text_cont: grad_enc_text_cont,
self.keep_prob: self.train_keep_prob,
self.fixed_noise: 1
}
for i in range(len(self.recurrent_noise)):
input_feed[self.recurrent_noise_in[i]] = self.recurrent_noise[i]
if buffered:
return ([self.grad_out], input_feed)
else:
return self.sess.run(self.grad_out, input_feed)
def train(self, grad, learning_rate, buffered=False):
input_feed = {}
for i in range(len(grad)):
input_feed[self.grad_in[i]] = grad[i]
input_feed[self.learning_rate] = learning_rate
if buffered:
return ([self.train_op], input_feed)
else:
self.sess.run(self.train_op, input_feed)
def infer(self, data, num_posts, is_train, buffered=False):
data = self.format_data(data)
input_feed = {
self.text_string: data["text_string"],
self.text_len: data["text_len"],
self.num_posts: num_posts,
self.fixed_noise: 0
}
for noise in self.recurrent_noise_in:
input_feed[noise] = np.zeros(noise.shape)
if is_train:
input_feed[self.keep_prob] = self.train_keep_prob
output_feed = [self.enc_text, self.enc_text_cont, self.recurrent_noise_out]
if buffered:
return (output_feed, input_feed)
else:
return self.sess.run(output_feed, input_feed)