Tensorflow实例：实现基于LSTM的语言模型

RNN

人每次思考时不会重头开始，而是保留之前思考的一些结果为现在的决策提供支持。例如我们对话时，我们会根据上下文的信息理解一句话的含义，而不是对每一句话重头进行分析。传统的神经网络不能实现这个功能，这可能是其一大缺陷。例如卷积神经网络虽然可以对图像进行分类，但是可能无法对视频中每一帧图像发生的事情进行关联分析，我们无法利用前一帧图像的信息，而循环神经网络则可以解决这个问题。

如上图所示，x是RNN的输入，s是RNN的一个节点，而o是输出。我们对这个RNN输入数据x，然后通过网络计算并得到输出结果o，再将某些信息（state，状态）传入到网络的输入。我们将o与label进行比较可以得到误差，有了这个误差之后，就能使用梯度下降（Gradient Descent）和Back-Propagation Through Time（BPTT）方法对网络进行训练，BPTT与训练前馈神经网络的传统BP方法类似，也是使用反向传播求梯度并更新网络参数权重。另外，还有一种方法叫Real-Time Recurrent Learning(RTRL)，它可以正向求解梯度，不过其计算复杂度比较高。
RNN展开后，类似于有一系列输入x和一系列输出o的串联的普通神经网络，上一层的神经网络会传递信息给下一层。这种串联的结构天然就非常适合时间序列数据的处理和分析。需要注意的是，展开后的每一层级的神经网络，其参数都是相同的，我们并不需要训练成百上千层神经网络的参数，只需要训练一层RNN的参数。这就是它结构巧妙的地方，这里共享参数的思想和卷积网络中权值共享的方式也很类似。

LSTM

对于某些简单的问题，可能只需要最后输入的少量时序信息即可解决。但是对某些复杂问题，可能需要更早的一些信息，甚至是时间序列开头的信息，但间隔太远的输入信息，RNN是难以记忆的，因此长程依赖（Long-term Dependencies）是传统RNN的致命伤。
LSTM天生就是为了解决长程依赖而设计的，不需要特别复杂地调试超参数，默认就可以记住长期的信息。

LSTM的内部结构相比RNN更复杂，其中包含了4层神经网络，其中小圈圈是point-wise的操作，比如向量加法、点乘等，而小矩阵则代表一层可学习参数的神经网络。

• LSTM单元上面的那条直线代表了LSTM的状态state，它会贯穿所有串联在一起的LSTM单元，从第一个LSTM单元一直流向最后一个LSTM单元，其中只有少量的线性干预和改变。
• 状态state在这条隧道中传递时，LSTM单元可以对其添加或删除信息，这些对信息流的修改操作由LSTM中的Gates控制。
• 这些Gates中包含了一个Sigmoid层和一个向量点乘的操作，这个Sigmoid层的输出是0-1之间的值，它直接控制了信息传递的比例。
• 每个LSTM单元中包含了3个这样的Gates，用来维护和控制单元的状态信息。凭借对状态信息的存储和修改，LSTM单元就可以实现长程记忆。

Tensorflow实现LSTM

下面我们就使用LSTM来实现一个语言模型，给定上文的语境，即历史出现的单词，语言模型可以预测下一个单词出现的概率，使用的数据集：PTB

#%%# Copyright 2016 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.# ==============================================================================import timeimport numpy as npimport tensorflow as tfimport 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.float32class 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) - 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():      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)    # 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(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_opclass 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 = 10000class 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 = 10000class 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 = 10000class 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 = 10000def 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)raw_data = reader.ptb_raw_data('simple-examples/data/')train_data, valid_data, test_data, _ = raw_dataconfig = SmallConfig()eval_config = SmallConfig()eval_config.batch_size = 1eval_config.num_steps = 1with 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.scalar_summary("Training Loss", m.cost)      #tf.scalar_summary("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.scalar_summary("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()  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()