tf.nn.static_rnn 和 tf.contrib.rnn.static_rnn tf.nn.static_rnn 是一样的,都表示同一个

这里讲解一下 tf.nn.static_rnn

tf.nn.static_rnn

tf.nn.static_rnn(
    cell,
    inputs,
    initial_state=None,
    dtype=None,
    sequence_length=None,
    scope=None
)
\'\'\'
Args:
		cell: An instance of RNNCell.
		inputs: A length T list of inputs, each a Tensor of shape [batch_size, input_size], or a nested tuple of such elements.
		initial_state: (optional) An initial state for the RNN. If cell.state_size is an integer, this must be a Tensor of appropriate type and shape [batch_size, cell.state_size]. If cell.state_size is a tuple, this should be a tuple of tensors having shapes [batch_size, s] for s in cell.state_size.
		dtype: (optional) The data type for the initial state and expected output. Required if initial_state is not provided or RNN state has a heterogeneous dtype.
		sequence_length: Specifies the length of each sequence in inputs. An int32 or int64 vector (tensor) size [batch_size], values in [0, T).
		scope: VariableScope for the created subgraph; defaults to \"rnn\".
Returns:
		A pair (outputs, state) where:
		outputs is a length T list of outputs (one for each input), or a nested tuple of such elements.
		state is the final state
\'\'\'

参数说明

创建一个RNN 通过指定的RNNCell :就是参数cell
cell:一个RNNCell实例
inputs: 长度为T的输入list，每个都是形状的张量[batch_size，input_size]
== initial_state:== (可选)RNN的初始状态。如果cell.state_size是一个整数，那么它必须是一个具有适当类型和形状的张量[batch_size，cell.state_size]（这里也就是state_tuple=False）。 如果cell.state_size是一个元组，那么它应该是一个张量元组，元组的每个元素的形状为[batch_size，s] (s in cell.state_size)（这里也就是state_tuple=True）
sequence_length: 指定输入中每个序列的长度(一旦提供，将动态编码dynamic_rnn)。int32或int64类型的向量（张量）大小[batch_size]，值在[0,T]之间。
返回：
(outputs, state)这样的一个元祖
outputs： 是输出长度T的列表（每个时间步输出一个）
state:最后时间步的RNN的state