static_rnn 和dynamic_rnn的区别

最近在看tensorflow的api接口，发现tensorflow中提供了rnn接口有两种，第一种是静态的rnn，另外一种是动态的rnn，这两种区别查了一些资料其中：https://stackoverflow.com/questions/39734146/whats-the-difference-between-tensorflow-dynamic-rnn-and-rnn

说的比较清楚，原文如下：

tf.nn.rnn creates an unrolled graph for a fixed RNN length. That means, if you call tf.nn.rnn with inputs having 200 time steps you are creating a static graph with 200 RNN steps. First, graph creation is slow. Second, you’re unable to pass in longer sequences (> 200) than you’ve originally specified.
tf.nn.dynamic_rnn solves this. It uses a tf.While loop to dynamically construct the graph when it is executed. That means graph creation is faster and you can feed batches of variable size.

中文大概意思是说：

tf.nn.rnn创建一个展开图的一个固定的网络长度。这意味着，如果有200次输入的步骤你与200步骤创建一个静态的图tf.nn.rnn RNN。首先，创建graphh较慢。第二，您无法传递比最初指定的更长的序列（> 200）。但是动态的tf.nn.dynamic_rnn解决这。当它被执行时，它使用循环来动态构建图形。这意味着图形创建速度更快，并且可以提供可变大小的批处理。

这里就说的比较清楚了，下面  看下编程有啥不同的：

静态的rnn：

def  RNN(_X,weights,biases):
    #第一种用static_rnn效果
    _X = tf.transpose(_X, [1, 0, 2])  # permute n_steps and batch_size
    _X = tf.reshape(_X, [-1, n_inputs])  # (n_steps*batch_size, n_input)
    _X = tf.matmul(_X, weights['in']) + biases['in']
    lstm_cell =tf.nn.rnn_cell.BasicLSTMCell(n_hidden_unis, forget_bias=1.0)
    _init_state=lstm_cell.zero_state(batch_size,dtype=tf.float32)
    _X = tf.split(_X, n_step,0 )  # n_steps * (batch_size, n_hidden)
    outputs, states =tf.nn.static_rnn(lstm_cell, _X, initial_state=_init_state)
    #第二种用dynamic_rnn，是是这种效果
    # lstm_cell =tf.nn.rnn_cell.BasicLSTMCell(n_hidden_unis)
    # outputs, states =tf.nn.dynamic_rnn(lstm_cell, _X,  dtype=tf.float32)
    # outputs = tf.transpose(outputs, [1, 0, 2])
    # Get inner loop last output
    return tf.matmul(outputs[-1], weights['out']) + biases['out']

动态的rnn:

def  RNN(_X,weights,biases):
    #第一种用static_rnn效果
    # _X = tf.transpose(_X, [1, 0, 2])  # permute n_steps and batch_size
    # _X = tf.reshape(_X, [-1, n_inputs])  # (n_steps*batch_size, n_input)
    # _X = tf.matmul(_X, weights['in']) + biases['in']
    # lstm_cell =tf.nn.rnn_cell.BasicLSTMCell(n_hidden_unis, forget_bias=1.0)
    # _init_state=lstm_cell.zero_state(batch_size,dtype=tf.float32)
    # _X = tf.split(_X, n_step,0 )  # n_steps * (batch_size, n_hidden)
    # outputs, states =tf.nn.static_rnn(lstm_cell, _X, initial_state=_init_state)
    #第二种用dynamic_rnn，是是这种效果
    lstm_cell =tf.nn.rnn_cell.BasicLSTMCell(n_hidden_unis)
    outputs, states =tf.nn.dynamic_rnn(lstm_cell, _X,  dtype=tf.float32)
    outputs = tf.transpose(outputs, [1, 0, 2])
    # Get inner loop last output
    return tf.matmul(outputs[-1], weights['out']) + biases['out']