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  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[], x[]))

    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[ : batch_size * batch_len], [batch_size, batch_len])

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