tensorflow38《TensorFlow实战》笔记-07-02 TensorFlow实现基于LSTM的语言模型 code

01 reader.py

# Copyright 2015 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.
# ==============================================================================

"""Utilities for parsing PTB 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").replace("\n", "<eos>").split()

def _build_vocab(filename):
data = _read_words(filename)

counter = collections.Counter(data)
count_pairs = sorted(counter.items(), key=lambda x: (-x[1], x[0]))

words, _ = list(zip(*count_pairs))
word_to_id = dict(zip(words, 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 ptb_raw_data(data_path=None):
"""Load PTB raw data from data directory "data_path".

Reads PTB text files, converts strings to integer ids,
and performs mini-batching of the inputs.

The PTB dataset comes from Tomas Mikolov's webpage:
 http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz 
Args:
data_path: string path to the directory where simple-examples.tgz has
been extracted.

Returns:
tuple (train_data, valid_data, test_data, vocabulary)
where each of the data objects can be passed to PTBIterator.
"""

train_path = os.path.join(data_path, "ptb.train.txt")
valid_path = os.path.join(data_path, "ptb.valid.txt")
test_path = os.path.join(data_path, "ptb.test.txt")

word_to_id = _build_vocab(train_path)
train_data = _file_to_word_ids(train_path, word_to_id)
valid_data = _file_to_word_ids(valid_path, word_to_id)
test_data = _file_to_word_ids(test_path, word_to_id)
vocabulary = len(word_to_id)
return train_data, valid_data, test_data, vocabulary

def ptb_producer(raw_data, batch_size, num_steps, name=None):
"""Iterate on the raw PTB 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 ptb_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, "PTBProducer", [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

02 TensorFlow实现基于LSTM的语言模型

# 《TensorFlow实战》07 TensorFlow实现循环神经网络及Word2Vec
# win10 Tensorflow1.0.1 python3.5.3
# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1
# filename:sz07.02.py # TensorFlow实现基于LSTM的语言模型

# 源码位置
# https://github.com/tensorflow/models/blob/master/tutorials/rnn/ptb/ptb_word_lm.py # https://github.com/tensorflow/models/blob/master/tutorials/rnn/ptb/reader.py # tensorflow_models\tutorials\rnn\ptb\ptb_word_lm.py
# tensorflow_models\tutorials\rnn\ptb\reader.py

# 下载数据文件
# wget http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz # tar xvf simple-examples.tgz
import time
import numpy as np
import tensorflow as tf
import reader

class PTBInput(object):
def __init__(self, config, data, name=None):
self.batch_size = batch_size = config.batch_size
self.num_steps = num_steps = config.num_steps
self.epoch_size = ((len(data) // batch_size) - 1) // num_steps
self.input_data, self.targets = reader.ptb_producer(data, batch_size, num_steps, name=name)

class PTBModel(object):
def __init__(self, is_training, config, input_):
self._input = input_
batch_size = input_.batch_size
num_steps = input_.num_steps
size = config.hidden_size
vocab_size = config.vocab_size

def lstm_cell():
return tf.contrib.rnn.BasicLSTMCell(size, forget_bias=0.0, state_is_tuple=True)

attn_cell = lstm_cell
if is_training and config.keep_prob < 1:
def attn_cell():
return tf.contrib.rnn.DropoutWrapper(lstm_cell(), output_keep_prob=config.keep_prob)
cell = tf.contrib.rnn.MultiRNNCell(
[attn_cell() for _ in range(config.num_layers)],
state_is_tuple=True)

self._initial_state = cell.zero_state(batch_size, tf.float32)

with tf.device("/cpu:0"):
embedding = tf.get_variable("embedding", [vocab_size, size], dtype=tf.float32)
inputs = tf.nn.embedding_lookup(embedding, input_.input_data)

if is_training and config.keep_prob < 1:
inputs = tf.nn.dropout(inputs, config.keep_prob)

outputs = []
state = self._initial_state
with tf.variable_scope("RNN"):
for time_step in range(num_steps):
if time_step > 0:
tf.get_variable_scope().reuse_variables()
(cell_output, state) = cell(inputs[:, time_step, :], state)
outputs.append(cell_output)

output = tf.reshape(tf.concat(outputs, 1), [-1, size])
softmax_w = tf.get_variable("softmax_w", [size, vocab_size], dtype=tf.float32)
softmax_b = tf.get_variable("softmax_b", [vocab_size], dtype=tf.float32)
logits = tf.matmul(output, softmax_w) + softmax_b
loss = tf.contrib.legacy_seq2seq.sequence_loss_by_example(
[logits],
[tf.reshape(input_.targets, [-1])],
[tf.ones([batch_size * num_steps], dtype=tf.float32)])
self._cost = cost = tf.reduce_sum(loss) / batch_size
self._final_state = state

if not is_training:
return

self._lr = tf.Variable(0.0, trainable=False)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(cost, tvars), config.max_grad_norm)
optimizer = tf.train.GradientDescentOptimizer(self._lr)
self._train_op = optimizer.apply_gradients(zip(grads, tvars),
global_step = tf.contrib.framework.get_or_create_global_step())
self._new_lr = tf.placeholder(tf.float32, shape=[], name="new_learning_rate")
self._lr_update = tf.assign(self._lr, self._new_lr)

def assign_lr(self, session, lr_value):
session.run(self._lr_update, feed_dict={self._new_lr: lr_value})

@property
def input(self):
return self._input

@property
def initial_state(self):
return self._initial_state

@property
def cost(self):
return self._cost

@property
def final_state(self):
return self._final_state

@property
def lr(self):
return self._lr
@property
def train_op(self):
return self._train_op

class SmallConfig(object):
init_scale = 0.1
learning_rate = 1.0
max_grad_norm = 5
num_layers = 2
num_steps = 20
hidden_size = 200
max_epoch = 4
max_max_epoch = 13
keep_prob = 1.0
lr_decay = 0.5
batch_size = 20
vocab_size = 10000

class MediumConfig(object):
init_scale = 0.05
learning_rate = 1.0
max_grad_norm = 5
num_layers = 2
num_steps = 35
hidden_size = 650
max_epoch = 6
max_max_epoch = 39
keep_prob = 0.5
lr_decay = 0.8
batch_size = 20
vocab_size = 10000

class LargeConfig(object):
init_scale = 0.04
learning_rate = 1.0
max_grad_norm = 10
num_layers = 2
num_steps = 35
hidden_size = 1500
max_epoch = 14
max_max_epoch = 55
keep_prob = 0.35
lr_decay = 1 / 1.15
batch_size = 20
vocab_size = 10000

class TestConfig(object):
init_scale = 0.1
learning_rate = 1.0
max_grad_norm = 1
num_layers = 1
num_steps = 2
hidden_size = 2
max_epoch = 1
max_max_epoch = 1
keep_prob = 1.0
lr_decay = 0.5
batch_size = 20
vocab_size = 10000

def run_epoch(session, model, eval_op=None, verbose=False):
start_time = time.time()
costs = 0.0
iters = 0
state = session.run(model.initial_state)

fetches = {"cost": model.cost, "final_state": model.final_state,}
if eval_op is not None:
fetches["eval_op"] = eval_op

for step in range(model.input.epoch_size):
feed_dict = {}
for i, (c, h) in enumerate(model.initial_state):
feed_dict[c] = state[i].c
feed_dict[h] = state[i].h

vals = session.run(fetches, feed_dict)
cost = vals["cost"]
state = vals["final_state"]

costs += cost
iters += model.input.num_steps

if verbose and step % (model.input.epoch_size // 10) == 10:
print("%.03f perplexity: %.3f speed: %.0f wps" %
(step * 1.0 / model.input.epoch_size, np.exp(costs / iters),
iters * model.input.batch_size / (time.time() - start_time)))
return np.exp(costs / iters)

raw_data = reader.ptb_raw_data('simple-examples/data/')
train_data, valid_data, test_data, _ = raw_data

config = SmallConfig()
eval_config = SmallConfig()
eval_config.batch_size = 1
eval_config.num_steps = 1

with tf.Graph().as_default():
initializer = tf.random_uniform_initializer(-config.init_scale, config.init_scale)
with tf.name_scope('Train'):
train_input = PTBInput(config=config, data=train_data, name='TrainInput')
with tf.variable_scope("Model", reuse=None, initializer=initializer):
m = PTBModel(is_training=True, config=config, input_=train_input)
with tf.name_scope("Valid"):
valid_input = PTBInput(config=config, data=valid_data, name="ValidInput")
with tf.variable_scope("Model", reuse=True, initializer=initializer):
mvalid = PTBModel(is_training=False, config=config, input_=valid_input)
with tf.name_scope("Test"):
test_input = PTBInput(config=eval_config, data=test_data, name="TestInput")
with tf.variable_scope("Model", reuse=True, initializer=initializer):
mtest = PTBModel(is_training=False, config=eval_config, input_=test_input)

sv = tf.train.Supervisor()
with sv.managed_session() as session:
for i in range(config.max_max_epoch):
lr_decay = config.lr_decay ** max(i + 1 - config.max_epoch, 0.0)
m.assign_lr(session, config.learning_rate * lr_decay)
print("Epoch: %d Learning rate: %.03f" % (i + 1, session.run(m.lr)))
train_perplexity = run_epoch(session, m, eval_op = m.train_op, verbose=True)
print("Epoch: %d Train Perplexity: %.3f" % (i + 1, train_perplexity))
valid_perplexity = run_epoch(session, mvalid)
print("Epoch: %d Valid Perplexity: %.3f" % (i + 1, valid_perplexity))
test_perplexity = run_epoch(session, mtest)
print("Test Perplexity: %.3f" % test_perplexity)
'''
Epoch: 1 Learning rate: 1.000
0.004 perplexity: 6015.388 speed: 4857 wps
0.104 perplexity: 845.091 speed: 9944 wps
0.204 perplexity: 626.239 speed: 10178 wps
0.304 perplexity: 505.402 speed: 10246 wps
...
0.604 perplexity: 44.433 speed: 10363 wps
0.703 perplexity: 43.784 speed: 10356 wps
0.803 perplexity: 43.080 speed: 10367 wps
0.903 perplexity: 41.675 speed: 10367 wps
Epoch: 13 Train Perplexity: 40.776
Epoch: 13 Valid Perplexity: 119.103
Test Perplexity: 114.648
'''