Blame view
egs/wsj/s5/steps/tfrnnlm/reader.py
3.64 KB
8dcb6dfcb
first commit
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 |
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
# Copyright (C) 2017 Intellisist, Inc. (Author: Hainan Xu)
#
# 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.
# ==============================================================================
"""Utilities for parsing RNNLM text files."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import os
import tensorflow as tf
def _read_words(filename):
with tf.gfile.GFile(filename, "r") as f:
return f.read().decode("utf-8").split()
def _build_vocab(filename):
words = _read_words(filename)
word_to_id = dict(list(zip(words, list(range(len(words))))))
return word_to_id
def _file_to_word_ids(filename, word_to_id):
data = _read_words(filename)
return [word_to_id[word] for word in data if word in word_to_id]
def rnnlm_raw_data(data_path, vocab_path):
"""Load RNNLM raw data from data directory "data_path".
Args:
data_path: string path to the directory where train/valid files are stored
Returns:
tuple (train_data, valid_data, test_data, vocabulary)
where each of the data objects can be passed to RNNLMIterator.
"""
train_path = os.path.join(data_path, "train")
valid_path = os.path.join(data_path, "valid")
word_to_id = _build_vocab(vocab_path)
train_data = _file_to_word_ids(train_path, word_to_id)
valid_data = _file_to_word_ids(valid_path, word_to_id)
vocabulary = len(word_to_id)
return train_data, valid_data, vocabulary, word_to_id
def rnnlm_producer(raw_data, batch_size, num_steps, name=None):
"""Iterate on the raw RNNLM data.
This chunks up raw_data into batches of examples and returns Tensors that
are drawn from these batches.
Args:
raw_data: one of the raw data outputs from rnnlm_raw_data.
batch_size: int, the batch size.
num_steps: int, the number of unrolls.
name: the name of this operation (optional).
Returns:
A pair of Tensors, each shaped [batch_size, num_steps]. The second element
of the tuple is the same data time-shifted to the right by one.
Raises:
tf.errors.InvalidArgumentError: if batch_size or num_steps are too high.
"""
with tf.name_scope(name, "RNNLMProducer", [raw_data, batch_size, num_steps]):
raw_data = tf.convert_to_tensor(raw_data, name="raw_data", dtype=tf.int32)
data_len = tf.size(raw_data)
batch_len = data_len // batch_size
data = tf.reshape(raw_data[0 : batch_size * batch_len],
[batch_size, batch_len])
epoch_size = (batch_len - 1) // num_steps
assertion = tf.assert_positive(
epoch_size,
message="epoch_size == 0, decrease batch_size or num_steps")
with tf.control_dependencies([assertion]):
epoch_size = tf.identity(epoch_size, name="epoch_size")
i = tf.train.range_input_producer(epoch_size, shuffle=False).dequeue()
x = tf.strided_slice(data, [0, i * num_steps],
[batch_size, (i + 1) * num_steps])
x.set_shape([batch_size, num_steps])
y = tf.strided_slice(data, [0, i * num_steps + 1],
[batch_size, (i + 1) * num_steps + 1])
y.set_shape([batch_size, num_steps])
return x, y
|