tf.nn.dynamic.rnn的用法以及输出outputs、state介绍

通过RNNCell建立一个RNN网络，要输入的参数：

tf.nn.dynamic_rnn(
    cell,
    inputs,
    sequence_length=None,
    initial_state=None,
    dtype=None,
    parallel_iterations=None,
    swap_memory=False,
    time_major=False,
    scope=None
)

例子，使用RNNCell，

# create a BasicRNNCell
rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)

# 'outputs' is a tensor of shape [batch_size, max_time, cell_state_size]

# defining initial state
initial_state = rnn_cell.zero_state(batch_size, dtype=tf.float32)

# 'state' is a tensor of shape [batch_size, cell_state_size]
outputs, state = tf.nn.dynamic_rnn(rnn_cell, input_data,
                                   initial_state=initial_state,
                                   dtype=tf.float32)

使用两层LSTM：

# create 2 LSTMCells
rnn_layers = [tf.nn.rnn_cell.LSTMCell(size) for size in [128, 256]]

# create a RNN cell composed sequentially of a number of RNNCells
multi_rnn_cell = tf.nn.rnn_cell.MultiRNNCell(rnn_layers)

# 'outputs' is a tensor of shape [batch_size, max_time, 256]
# 'state' is a N-tuple where N is the number of LSTMCells containing a
# tf.contrib.rnn.LSTMStateTuple for each cell
outputs, state = tf.nn.dynamic_rnn(cell=multi_rnn_cell,
                                   inputs=data,
                                   dtype=tf.float32)

输出是outputs与state：

• outputs: The RNN output Tensor.

  If time_major == False (default), this will be a Tensor shaped: [batch_size, max_time, cell.output_size].

  If time_major == True, this will be a Tensor shaped: [max_time, batch_size, cell.output_size].

  Note, if cell.output_size is a (possibly nested) tuple of integers or TensorShape objects, then outputs will be a tuple having the same structure as cell.output_size, containing Tensors having shapes corresponding to the shape data in cell.output_size.

• state: The final state. If cell.state_size is an int, this will be shaped [batch_size, cell.state_size]. If it is a TensorShape, this will be shaped [batch_size] + cell.state_size. If it is a (possibly nested) tuple of ints or TensorShape, this will be a tuple having the corresponding shapes. If cells are LSTMCells state will be a tuple containing a LSTMStateTuple for each cell.

outputs就是输出的张量结果，state是最后一个cell的输出结果，也就是最后一个时刻的状态，是一个tensor。

outputs里面，包含了所有时刻的输出 H；

state里面，包含了最后一个时刻的输出 H 和 C。