Tensorflow中循环神经网络及其Wrappers

• tf.nn.rnn_cell.LSTMCell

  • 又名：tf.nn.rnn_cell.BasicLSTMCell、tf.contrib.rnn.LSTMCell

  • 参见: tf.nn.rnn_cell.LSTMCell

  • 输出：

    • output：LSTM单元输出h，与LSTM cell state的区别在于该输出又经过激活以及和一个sigmoid函数输出相乘。shape: [batch_size,num_units]
    • new_state：当前时间步上的LSTM cell state和LSTM output，LSTMStateTuple：(c,h)，其中，h与上述的output张量相同。shape: ([batch_size,num_units],[batch_size,num_units])

  • 示例：

    batch_size=10
    embedding_dim=300
    inputs=tf.Variable(tf.random_normal([batch_size,embedding_dim]))
    previous_state=(tf.Variable(tf.random_normal([batch_size,128])),tf.Variable(tf.random_normal([batch_size,128])))
    lstmcell=tf.nn.rnn_cell.LSTMCell(128)
    outputs,(c_state,h_state)=lstmcell(inputs,previous_state)

    输出：

    (<tf.Tensor 'lstm_cell/mul_2:0' shape=(10, 128) dtype=float32>,
     LSTMStateTuple(c=<tf.Tensor 'lstm_cell/add_1:0' shape=(10, 128) dtype=float32>, h=<tf.Tensor 'lstm_cell/mul_2:0' shape=(10, 128) dtype=float32>))

• tf.nn.rnn_cell.MultiRNNCell

  • 参见：tf.nn.rnn_cell.MultiRNNCell

  • 输出：

    • outputs: 最顶层cell的最后一个时间步的输出。shape：[batch_size,cell.output_size]
    • states：每一层的state，M层LSTM则输出M个LSTMStateTuple组成的Tuple。

  • 示例：

    batch_size=10
    inputs=tf.Variable(tf.random_normal([batch_size,128]))
    previous_state0=(tf.random_normal([batch_size,100]),tf.random_normal([batch_size,100]))
    previous_state1=(tf.random_normal([batch_size,200]),tf.random_normal([batch_size,200]))
    previous_state2=(tf.random_normal([batch_size,300]),tf.random_normal([batch_size,300]))
    num_units=[100,200,300]
    cells=[tf.nn.rnn_cell.LSTMCell(num_unit) for num_unit in num_units]
    mul_cells=tf.nn.rnn_cell.MultiRNNCell(cells)
    outputs,states=mul_cells(inputs,(previous_state0,previous_state1,previous_state2))

    输出：

    outputs:
    <tf.Tensor 'multi_rnn_cell_1/cell_2/lstm_cell/mul_2:0' shape=(10, 300) dtype=float32>
    states:
    
    Out[29]:
    (LSTMStateTuple(c=<tf.Tensor 'multi_rnn_cell_1/cell_0/lstm_cell/add_1:0' shape=(10, 100) dtype=float32>, h=<tf.Tensor 'multi_rnn_cell_1/cell_0/lstm_cell/mul_2:0' shape=(10, 100) dtype=float32>),
     LSTMStateTuple(c=<tf.Tensor 'multi_rnn_cell_1/cell_1/lstm_cell/add_1:0' shape=(10, 200) dtype=float32>, h=<tf.Tensor 'multi_rnn_cell_1/cell_1/lstm_cell/mul_2:0' shape=(10, 200) dtype=float32>),
     LSTMStateTuple(c=<tf.Tensor 'multi_rnn_cell_1/cell_2/lstm_cell/add_1:0' shape=(10, 300) dtype=float32>, h=<tf.Tensor 'multi_rnn_cell_1/cell_2/lstm_cell/mul_2:0' shape=(10, 300) dtype=float32>))

• tf.nn.dynamic_rnn

  • 参见：tf.nn.dynamic_rnn

  • 输出：

    • outputs: 每个时间步上的LSTM输出；若有多层LSTM，则为每一个时间步上最顶层的LSTM的输出。shape: [batch_size,max_time,cell.output_size]
    • state：最后一个时间步的状态，该状态使用LSTMStateTuple结构输出；若有M层LSTM，则输出M个LSTMStateTuple。单层LSTM输出：[batch_size,cell.output_size]；M层LSTM输出：M个LSTMStateTuple组成的Tuple，这也即是说：outputs[:,-1,:]==state[-1,:,:]。

  • 示例：

    batch_size=10
    max_time=20
    data=tf.Variable(tf.random_normal([batch_size,max_time,128]))
    # create a BasicRNNCell
    rnn_cell = tf.nn.rnn_cell.BasicRNNCell(num_units=128)
    
    # '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(cell=rnn_cell, inputs=data,
                                       initial_state=initial_state,
                                       dtype=tf.float32)

    输出：

    outpus:
    <tf.Tensor 'rnn_2/transpose_1:0' shape=(10, 20, 128) dtype=float32>
    state:
    <tf.Tensor 'rnn_2/while/Exit_3:0' shape=(10, 128) dtype=float32>

    batch_size=10
    max_time=20
    data=tf.Variable(tf.random_normal([batch_size,max_time,128]))
    # 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)

    outputs:
    <tf.Tensor 'rnn_1/transpose_1:0' shape=(10, 20, 256) dtype=float32>
    state:
    
    Out[20]:
    (LSTMStateTuple(c=<tf.Tensor 'rnn_1/while/Exit_3:0' shape=(10, 128) dtype=float32>, h=<tf.Tensor 'rnn_1/while/Exit_4:0' shape=(10, 128) dtype=float32>),
     LSTMStateTuple(c=<tf.Tensor 'rnn_1/while/Exit_5:0' shape=(10, 256) dtype=float32>, h=<tf.Tensor 'rnn_1/while/Exit_6:0' shape=(10, 256) dtype=float32>))

• tf.nn.bidirectional_dynamic_rnn

  • 参见：tf.nn.bidirectional_dynamic_rnn

  • 输出：

    • outputs：(output_fw,output_bw)：前向cell+后向cell

      其中，output_fw、output_bw：[batch_size,max_time,cell.output_size]

    • state：(output_state_fw,output_state_bw)：包含前向和后向隐状态组成的元组

      其中，output_state_fw、output_state_bw均为LSTMStateTuple。LSTMStateTuple:(c,h)，分别为cell_state，hidden_output

• tf.contrib.seq2seq.dynamic_decode

  • 输出：
    • final_outputs，包含rnn_output和sample_id，分别可用final_output.rnn_output和final_outputs.sample_id获取到。
    • final_state，可以从最后一个解码器状态获取alignments，alignments = tf.transpose(final_decoder_state.alignment_history.stack(), [1, 2, 0])
    • final_sequence_lengths

标签：Wrappers,rnn,state,batch,cell,神经网络,tf,Tensorflow,size
来源： https://www.cnblogs.com/mengnan/p/10384484.html