tensorflow 的rnn的示例 ptb_word_lm.py 的完整代码

其训练数据源在我的空间里，名字为：tensorflow的ptb-word-lm示例的训练数据源.tgz

讲解参见另一篇文章： http://www.cnblogs.com/welhzh/p/6739370.html

"""Example / benchmark for building a PTB LSTM model.
Trains the model described in:
(Zaremba, et. al.) Recurrent Neural Network Regularization http://arxiv.org/abs/1409.2329 There are 3 supported model configurations:
===========================================
| config | epochs | train | valid  | test
===========================================
| small  | 13     | 37.99 | 121.39 | 115.91
| medium | 39     | 48.45 |  86.16 |  82.07
| large  | 55     | 37.87 |  82.62 |  78.29
The exact results may vary depending on the random initialization.
The hyperparameters used in the model:
- init_scale - the initial scale of the weights
- learning_rate - the initial value of the learning rate
- max_grad_norm - the maximum permissible norm of the gradient
- num_layers - the number of LSTM layers
- num_steps - the number of unrolled steps of LSTM
- hidden_size - the number of LSTM units
- max_epoch - the number of epochs trained with the initial learning rate
- max_max_epoch - the total number of epochs for training
- keep_prob - the probability of keeping weights in the dropout layer
- lr_decay - the decay of the learning rate for each epoch after "max_epoch"
- batch_size - the batch size
The data required for this example is in the data/ dir of the
PTB dataset from Tomas Mikolov's webpage:
$ wget http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz $ tar xvf simple-examples.tgz
To run:
$ python ptb_word_lm.py --data_path=simple-examples/data/
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import inspect
import time

import numpy as np
import tensorflow as tf

# 这个 reader 包位于 tensorflow 源代码的 tag 0.12.0-rc1 分支的 tensorflow/models/rnn/ptb/reader.py 位置。
import reader

flags = tf.flags
logging = tf.logging

flags.DEFINE_string( "model", "small", "A type of model. Possible options are: small, medium, large.")
flags.DEFINE_string("data_path", "/home/hzh/delll/pydev/ptb/simple-examples/data",
"Where the training/test data is stored.")
flags.DEFINE_string("save_path", None, "Model output directory.")
flags.DEFINE_bool("use_fp16", False, "Train using 16-bit floats instead of 32bit floats")

FLAGS = flags.FLAGS

def data_type():
return tf.float16 if FLAGS.use_fp16 else tf.float32

class PTBInput(object):
"""The input data."""
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 决定了data的个数的最小值，若len(data) 小于 batch_size*(num_steps+1)，则无法训练。
# 若要在数据量很小的时候继续训练，则需要减小 batch_size 或 num_steps， 建议减小 batch_size
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):
"""The PTB model."""
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

# Slightly better results can be obtained with forget gate biases
# initialized to 1 but the hyperparameters of the model would need to be
# different than reported in the paper.
def lstm_cell():
# With the latest TensorFlow source code (as of Mar 27, 2017),
# the BasicLSTMCell will need a reuse parameter which is unfortunately not
# defined in TensorFlow 1.0. To maintain backwards compatibility, we add
# an argument check here:
if 'reuse' in inspect.getargspec(tf.contrib.rnn.BasicLSTMCell.__init__).args:
return tf.contrib.rnn.BasicLSTMCell(
size, forget_bias=0.0, state_is_tuple=True,
reuse=tf.get_variable_scope().reuse)
else:
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, data_type())

with tf.device("/cpu:0"):
embedding = tf.get_variable("embedding", [vocab_size, size], dtype=data_type())
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)

# Simplified version of models/tutorials/rnn/rnn.py's rnn().
# This builds an unrolled LSTM for tutorial purposes only.
# In general, use the rnn() or state_saving_rnn() from rnn.py.
#
# The alternative version of the code below is:
#
# inputs = tf.unstack(inputs, num=num_steps, axis=1)
# outputs, state = tf.contrib.rnn.static_rnn(
#     cell, inputs, initial_state=self._initial_state)
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(axis=1, values=outputs), [-1, size])
softmax_w = tf.get_variable("softmax_w", [size, vocab_size], dtype=data_type())
softmax_b = tf.get_variable("softmax_b", [vocab_size], dtype=data_type())
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=data_type())])
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):
"""Small config."""
init_scale = 0.1
learning_rate = 1.0
max_grad_norm = 5
num_layers = 2        # 堆叠的层数
num_steps = 20        # unrolled 之后的级联cell数
hidden_size = 200     # 单个cell中，在对输入进行embedding之后，单个cell的状态及单个cell的输入的维度
max_epoch = 4
max_max_epoch = 13
keep_prob = 1.0
lr_decay = 0.5
batch_size = 20
vocab_size = 10000

class MediumConfig(object):
"""Medium config."""
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):
"""Large config."""
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):
"""Tiny config, for testing."""
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):
"""Runs the model on the given data."""
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("%.3f 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)

def get_config():
if FLAGS.model == "small":
return SmallConfig()
elif FLAGS.model == "medium":
return MediumConfig()
elif FLAGS.model == "large":
return LargeConfig()
elif FLAGS.model == "test":
return TestConfig()
else:
raise ValueError("Invalid model: %s", FLAGS.model)

def main(_):
if not FLAGS.data_path:
raise ValueError("Must set --data_path to PTB data directory")

raw_data = reader.ptb_raw_data(FLAGS.data_path)
train_data, valid_data, test_data, _ = raw_data

config = get_config()
eval_config = get_config()
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)
tf.summary.scalar("Training Loss", m.cost)
tf.summary.scalar("Learning Rate", m.lr)

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)
tf.summary.scalar("Validation Loss", mvalid.cost)

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(logdir=FLAGS.save_path)
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: %.3f" % (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)

if FLAGS.save_path:
print("Saving model to %s." % FLAGS.save_path)
sv.saver.save(session, FLAGS.save_path, global_step=sv.global_step)

if __name__ == "__main__":
tf.app.run()

如果运行时出现： WARNING:tensorflow:Standard services need a 'logdir' passed to the SessionManager 警告，是因为文中调用的 tf.train.Supervisor 需要一个非 None 的 logdir。

=====

把 reader.py 也列在下面，以方便保存自己添加的注释：

# 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:
# commented by hzh
#return f.read().decode("utf-8").replace("\n", "<eos>").split()
return f.read().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")

# hzh modify
# aaa = 0
i = tf.train.range_input_producer(epoch_size, shuffle=False).dequeue()
# print(i)
x = tf.slice(data, [0, i * num_steps], [batch_size, num_steps])
y = tf.slice(data, [0, i * num_steps + 1], [batch_size, num_steps])
"""
with tf.Session() as ss:
if aaa == 0:
aaa += 1
xxx = ss.run(x)
yyy = ss.run(y)
print(xxx)
print(yyy)
"""
return x, y