LSTM代码

tensorflow的关于LSTM的代码，经过反复的调试和修改，终于运行成功了，可以把训练过程的结果保存起来，然后预测的时候直接取出来。花了很长时间才把官网上的代码调试成功，里面的坑有很多需要填补，还有源代码，都需要认真解读，关于tensorflow的高级结构，比如队列和多线程，也涉及到了。

import time
import numpy as np
import tensorflow as tf
import tensorflow.models.rnn.ptb.reader as reader

# flags = tf.flags
# logging = tf.logging
# 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 = ((len(data) // batch_size)) // num_steps
        self.x, self.y = input,target = reader.ptb_producer(data,batch_size,num_steps)
        self.input_data = tf.placeholder(shape=[batch_size,num_steps],dtype=tf.int32)
        self.targets = tf.placeholder(shape=[batch_size,num_steps],dtype=tf.int32)

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():
            return tf.nn.rnn_cell.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.nn.rnn_cell.DropoutWrapper(
                    lstm_cell(), output_keep_prob=config.keep_prob)
        cell = tf.nn.rnn_cell.MultiRNNCell(
            [attn_cell() for _ in range(config.num_layers)], state_is_tuple=True)
        self._initial_state = cell.zero_state(batch_size, tf.float32)
        print('initial_state:',self._initial_state)
        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)
        # 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.nn.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(1, outputs), [-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.nn.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
        tf.add_to_collection("final_state",self._final_state)
        print("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)

        self.saver = saver = tf.train.Saver()

    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
    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):
    """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,data,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

        x,y = session.run([model.input.x,model.input.y])
        feed_dict[model.input.input_data] = x
        feed_dict[model.input.targets] = y

        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)

#获取句子的向量
def predict(session,model,data,verbose=False):
    result = []#存储表示句子的向量
    state = session.run(model.initial_state)

    saver = tf.train.import_meta_graph("E:/LSTM/models/model.ckpt.meta")
    saver.restore(session,"E:/LSTM/models/model.ckpt")
    final_state = tf.get_collection("final_state")[0]
    fetches = {"final_state":final_state}
    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

        x, y = session.run([model.input.x, model.input.y])
        feed_dict[model.input.input_data] = x
        feed_dict[model.input.targets] = y
        vals = session.run(fetches, feed_dict)
        result.append(vals[-1].h)
        print(vals[-1].h)
    return result

raw_data = reader.ptb_raw_data('E:/LSTM/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() as g:
    initializer = tf.random_uniform_initializer(-config.init_scale,
                                                config.init_scale)
    with g.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.scalar_summary("Training Loss", m.cost)
            # tf.scalar_summary("Learning Rate", m.lr)
    with g.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.scalar_summary("Validation Loss", mvalid.cost)
    with g.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:
        summary_writer = tf.train.SummaryWriter('E:/LSTM/lstm_logs', session.graph)
        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, data=train_data,eval_op=m.train_op,
                                         verbose=True)
            print("Epoch: %d Train Perplexity: %.3f" % (i + 1, train_perplexity))
            valid_perplexity = run_epoch(session, mvalid,data=valid_data)
            print("Epoch: %d Valid Perplexity: %.3f" % (i + 1, valid_perplexity))

        m.saver.save(session, "E:/LSTM/models/model.ckpt")
        sentences = predict(session, mtest,data=test_data)#获取句子向量