# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import tensorflow as tf
import numpy as np
import collections
from ops import input_ops
FLAGS = tf.flags.FLAGS
def read_vocab_embs(vocabulary_file, embedding_matrix_file):
tf.logging.info("Reading vocabulary from %s", vocabulary_file)
with tf.gfile.GFile(vocabulary_file, mode="r") as f:
lines = list(f.readlines())
vocab = [line.decode("utf-8").strip() for line in lines]
with open(embedding_matrix_file, "r") as f:
embedding_matrix = np.load(f)
tf.logging.info("Loaded embedding matrix with shape %s",
embedding_matrix.shape)
word_embedding_dict = collections.OrderedDict(
zip(vocab, embedding_matrix))
return word_embedding_dict
def read_vocab(vocabulary_file):
tf.logging.info("Reading vocabulary from %s", vocabulary_file)
with tf.gfile.GFile(vocabulary_file, mode="r") as f:
lines = list(f.readlines())
reverse_vocab = [line.decode("utf-8").strip() for line in lines]
tf.logging.info("Loaded vocabulary with %d words.", len(reverse_vocab))
#tf.logging.info("Loading embedding matrix from %s", embedding_matrix_file)
# Note: tf.gfile.GFile doesn't work here because np.load() calls f.seek()
# with 3 arguments.
word_embedding_dict = collections.OrderedDict(
zip(reverse_vocab, range(len(reverse_vocab))))
return word_embedding_dict
class s2v(object):
"""Skip-thoughts model."""
def __init__(self, config, mode="train", input_reader=None, input_queue=None):
"""Basic setup. The actual TensorFlow graph is constructed in build().
Args:
config: Object containing configuration parameters.
mode: "train", "eval" or "encode".
input_reader: Subclass of tf.ReaderBase for reading the input serialized
tf.Example protocol buffers. Defaults to TFRecordReader.
Raises:
ValueError: If mode is invalid.
"""
if mode not in ["train", "eval", "encode"]:
raise ValueError("Unrecognized mode: %s" % mode)
self.config = config
self.mode = mode
self.reader = input_reader if input_reader else tf.TFRecordReader()
self.input_queue = input_queue
# Initializer used for non-recurrent weights.
self.uniform_initializer = tf.random_uniform_initializer(
minval=-FLAGS.uniform_init_scale,
maxval=FLAGS.uniform_init_scale)
# Input sentences represented as sequences of word ids. "encode" is the
# source sentence, "decode_pre" is the previous sentence and "decode_post"
# is the next sentence.
# Each is an int64 Tensor with shape [batch_size, padded_length].
self.encode_ids = None
# Boolean masks distinguishing real words (1) from padded words (0).
# Each is an int32 Tensor with shape [batch_size, padded_length].
self.encode_mask = None
# Input sentences represented as sequences of word embeddings.
# Each is a float32 Tensor with shape [batch_size, padded_length, emb_dim].
self.encode_emb = None
# The output from the sentence encoder.
# A float32 Tensor with shape [batch_size, num_gru_units].
self.thought_vectors = None
# The total loss to optimize.
self.total_loss = None
def build_inputs(self):
if self.mode == "encode":
encode_ids = tf.placeholder(tf.int64, (None, None), name="encode_ids")
encode_mask = tf.placeholder(tf.int8, (None, None), name="encode_mask")
else:
# Prefetch serialized tf.Example protos.
input_queue = input_ops.prefetch_input_data(
self.reader,
FLAGS.input_file_pattern,
shuffle=FLAGS.shuffle_input_data,
capacity=FLAGS.input_queue_capacity,
num_reader_threads=FLAGS.num_input_reader_threads)
# Deserialize a batch.
serialized = input_queue.dequeue_many(FLAGS.batch_size)
encode = input_ops.parse_example_batch(serialized)
encode_ids = encode.ids
encode_mask = encode.mask
self.encode_ids = encode_ids
self.encode_mask = encode_mask
def build_word_embeddings(self):
rand_init = self.uniform_initializer
self.word_embeddings = []
self.encode_emb = []
self.init = None
for v in self.config.vocab_configs:
if v.mode == 'fixed':
if self.mode == "train":
word_emb = tf.get_variable(
name=v.name,
shape=[v.size, v.dim],
trainable=False)
embedding_placeholder = tf.placeholder(
tf.float32, [v.size, v.dim])
embedding_init = word_emb.assign(embedding_placeholder)
rand = np.random.rand(1, v.dim)
word_vecs = np.load(v.embs_file)
load_vocab_size = word_vecs.shape[0]
assert(load_vocab_size == v.size - 1)
word_init = np.concatenate((rand, word_vecs), axis=0)
self.init = (embedding_init, embedding_placeholder, word_init)
else:
word_emb = tf.get_variable(
name=v.name,
shape=[v.size, v.dim])
encode_emb = tf.nn.embedding_lookup(word_emb, self.encode_ids)
self.word_emb = word_emb
self.encode_emb.extend([encode_emb, encode_emb])
if v.mode == 'trained':
for inout in ["", "_out"]:
word_emb = tf.get_variable(
name=v.name + inout,
shape=[v.size, v.dim],
initializer=rand_init)
if self.mode == 'train':
self.word_embeddings.append(word_emb)
encode_emb = tf.nn.embedding_lookup(word_emb, self.encode_ids)
self.encode_emb.append(encode_emb)
if v.mode == 'expand':
for inout in ["", "_out"]:
encode_emb = tf.placeholder(tf.float32, (
None, None, v.dim), v.name + inout)
self.encode_emb.append(encode_emb)
word_emb_dict = read_vocab_embs(v.vocab_file + inout + ".txt",
v.embs_file + inout + ".npy")
self.word_embeddings.append(word_emb_dict)
if v.mode != 'expand' and self.mode == 'encode':
word_emb_dict = read_vocab(v.vocab_file)
self.word_embeddings.extend([word_emb_dict, word_emb_dict])
def _initialize_cell(self, num_units, cell_type="GRU"):
if cell_type == "GRU":
return tf.contrib.rnn.GRUCell(num_units=num_units)
elif cell_type == "LSTM":
return tf.contrib.rnn.LSTMCell(num_units=num_units)
else:
raise ValueError("Invalid cell type")
def bow(self, word_embs, mask):
mask_f = tf.expand_dims(tf.cast(mask, tf.float32), -1)
word_embs_mask = word_embs * mask_f
bow = tf.reduce_sum(word_embs_mask, axis=1)
return bow
def rnn(self, word_embs, mask, scope, encoder_dim, cell_type="GRU"):
length = tf.to_int32(tf.reduce_sum(mask, 1), name="length")
if self.config.bidir:
if encoder_dim % 2:
raise ValueError(
"encoder_dim must be even when using a bidirectional encoder.")
num_units = encoder_dim // 2
cell_fw = self._initialize_cell(num_units, cell_type=cell_type)
cell_bw = self._initialize_cell(num_units, cell_type=cell_type)
outputs, states = tf.nn.bidirectional_dynamic_rnn(
cell_fw=cell_fw,
cell_bw=cell_bw,
inputs=word_embs,
sequence_length=length,
dtype=tf.float32,
scope=scope)
if cell_type == "LSTM":
states = [states[0][1], states[1][1]]
state = tf.concat(states, 1)
else:
cell = self._initialize_cell(encoder_dim, cell_type=cell_type)
outputs, state = tf.nn.dynamic_rnn(
cell=cell,
inputs=word_embs,
sequence_length=length,
dtype=tf.float32,
scope=scope)
if cell_type == "LSTM":
state = state[1]
return state
def build_encoder(self):
"""Builds the sentence encoder.
Inputs:
self.encode_emb
self.encode_mask
Outputs:
self.thought_vectors
Raises:
ValueError: if config.bidirectional_encoder is True and config.encoder_dim
is odd.
"""
names = ["","_out"]
self.thought_vectors = []
print(self.config.encoder)
for i in range(2):
with tf.variable_scope("encoder" + names[i]) as scope:
if self.config.encoder == "gru":
sent_rep = self.rnn(self.encode_emb[i], self.encode_mask, scope, self.config.encoder_dim, cell_type="GRU")
elif self.config.encoder == "lstm":
sent_rep = self.rnn(self.encode_emb[i], self.encode_mask, scope, self.config.encoder_dim, cell_type="LSTM")
elif self.config.encoder == 'bow':
sent_rep = self.bow(self.encode_emb[i], self.encode_mask)
else:
raise ValueError("Invalid encoder")
thought_vectors = tf.identity(sent_rep, name="thought_vectors")
self.thought_vectors.append(thought_vectors)
def build_loss(self):
"""Builds the loss Tensor.
Outputs:
self.total_loss
"""
all_sen_embs = self.thought_vectors
if FLAGS.dropout:
mask_shp = [1, self.config.encoder_dim]
bin_mask = tf.random_uniform(mask_shp) > FLAGS.dropout_rate
bin_mask = tf.where(bin_mask, tf.ones(mask_shp), tf.zeros(mask_shp))
src = all_sen_embs[0] * bin_mask
dst = all_sen_embs[1] * bin_mask
scores = tf.matmul(src, dst, transpose_b=True)
else:
scores = tf.matmul(all_sen_embs[0], all_sen_embs[1], transpose_b=True)
# Ignore source sentence
scores = tf.matrix_set_diag(scores, np.zeros(FLAGS.batch_size))
# Targets
targets_np = np.zeros((FLAGS.batch_size, FLAGS.batch_size))
ctxt_sent_pos = range(-FLAGS.context_size, FLAGS.context_size + 1)
ctxt_sent_pos.remove(0)
for ctxt_pos in ctxt_sent_pos:
targets_np += np.eye(FLAGS.batch_size, k=ctxt_pos)
targets_np_sum = np.sum(targets_np, axis=1, keepdims=True)
targets_np = targets_np/targets_np_sum
targets = tf.constant(targets_np, dtype=tf.float32)
# Forward and backward scores
f_scores = scores[:-1]
b_scores = scores[1:]
losses = tf.nn.softmax_cross_entropy_with_logits(
labels=targets, logits=scores)
loss = tf.reduce_mean(losses)
tf.summary.scalar("losses/ent_loss", loss)
self.total_loss = loss
if self.mode == "eval":
f_max = tf.to_int64(tf.argmax(f_scores, axis=1))
b_max = tf.to_int64(tf.argmax(b_scores, axis=1))
targets = range(FLAGS.batch_size - 1)
targets = tf.constant(targets, dtype=tf.int64)
fwd_targets = targets + 1
names_to_values, names_to_updates = tf.contrib.slim.metrics.aggregate_metric_map({
"Acc/Fwd Acc": tf.contrib.slim.metrics.streaming_accuracy(f_max, fwd_targets),
"Acc/Bwd Acc": tf.contrib.slim.metrics.streaming_accuracy(b_max, targets)
})
for name, value in names_to_values.iteritems():
tf.summary.scalar(name, value)
self.eval_op = names_to_updates.values()
def build(self):
"""Creates all ops for training, evaluation or encoding."""
self.build_inputs()
self.build_word_embeddings()
self.build_encoder()
self.build_loss()
def build_enc(self):
"""Creates all ops for training, evaluation or encoding."""
self.build_inputs()
self.build_word_embeddings()
self.build_encoder()
