5-RNN-02_RNN基本api

import tensorflow as tf


"""
tf.nn.rnn_cell        # 定义rnn 细胞核相关的信息的
tf.nn.rnn_cell_impl   # 定义rnn细胞核具体是如何实现的
tf.nn.dynamic_rnn()   # 单向动态rnn。
tf.nn.bidirectional_dynamic_rnn()   # 双向动态rnn
tf.nn.static_rnn()    # 单向静态rnn
tf.nn.static_bidirectional_rnn()   # 双向静态rnn
"""

"""
tf.nn.dynamic_rnn()   # 单向动态rnn。
     特点 1、在每个批次执行之间构建rnn的执行结构。 允许每个批量数据的 时间步不一致。 效率慢。
          2、输入要求：3-D tensor[N, n_steps, num_classes]
          3、输出形状: 3-D tensor[N, n_steps, lstm_size]
          
tf.nn.static_rnn()    # 单向静态rnn
     1、静态rnn在执行之前，执行结构已经构建好， 每个批量数据的时间步必须一致。 效果快
     2、输入的要求是一个列表（每一个值对应一个时刻的输入）：
           [[N, num_classes], [N, num_classes],[N, num_classes] ....]
     3、返回的输出，也是一个列表（每一个值对应一个时刻的输出）
          [[N, lstm_size], [N, lstm_size],[N, lstm_size] ....]
"""

# 一、关于rnn中的细胞核cell
"""
tf.nn.rnn_cell.BasicLSTMCell     # 基础的LSTM cell
tf.nn.rnn_cell.LSTMCell()        # 带peephole的  LSTM cell
tf.nn.rnn_cell.BasicRNNCell()    # 基础的rnn   vanilla rnn
tf.nn.rnn_cell.GRUCell()         # GRU实现

tf.nn.rnn_cell.MultiRNNCell()     # 堆栈多层隐藏层的
tf.nn.rnn_cell.DropoutWrapper()   # rnn的dropout
tf.nn.rnn_cell.RNNCell    # 所有cell实现的父类
"""

def BasicRNN():
    """
    学习tf.nn.rnn_cell.BasicRNNCell() 使用
    :return:
    """
    cell = tf.nn.rnn_cell.BasicRNNCell(num_units=64, activation=tf.nn.tanh)
    print(cell.state_size, cell.output_size)


def BasicRNN_n_steps():
    # 定义一个输入，每个时刻2个样本(batch_size)，每个样本由3个维度组成(one-hot)
    batch_size = tf.placeholder_with_default(2, shape=[], name='batch')
    inputs1 = tf.placeholder(tf.float32, shape=[2, 3])
    inputs2 = tf.placeholder(tf.float32, shape=[2, 3])
    inputs = [inputs1, inputs2]

    # 实例化一个细胞核
    cell = tf.nn.rnn_cell.BasicRNNCell(num_units=4)

    # 初始化一个状态值
    state0 = cell.zero_state(batch_size, tf.float32)
    # 获取t=1时刻的输出 （需要传入 t=1时刻的输入和 上一时刻的状态值）
    # output1, state1 = cell.__call__(inputs[0], state0)
    output1, state1 = cell(inputs[0], state0)
    print(output1, state1)

    # 获取t=2时刻的输出 （需要传入 t=2时刻的输入和 上一时刻的状态值state1）
    output2, state2 = cell(inputs[1], state1)
    print(output2, state2)

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        data1 = [
            [1.2, 2.3, 3.4],
            [2.3, 3.5, 3.8]
        ]
        data2 = [
            [1.32, 2.33, 3.34],
            [2.33, 3.35, 3.38]
        ]
        feed = {inputs1: data1, inputs2: data2}
        output1_, state1_, output2_, state2_ = sess.run(
            [output1, state1, output2, state2], feed)
        print(output1_, state1_)
        print('**'*56)
        print(output2_, state2_)


def BasicLSTM():
    """
    学习lstm cell
    :return:
    """
    cell = tf.nn.rnn_cell.BasicLSTMCell(num_units=3)

    # 定义一个输入，总共2个时刻（时间步），每个时刻输入4个样本（batch_size,），每个样本由2个维度组成(one-hot维度)
    old_inputs = tf.placeholder(tf.float32, [8, 2])
    inputs = tf.split(old_inputs, num_or_size_splits=2, axis=0)
    print(inputs)

    # 初始化状态值
    s0 = cell.zero_state(batch_size=4, dtype=tf.float32)

    # 直接将输入 传入静态rnn。
    tf.nn.static_bidirectional_rnn()
    rnn_outputs, final_state = tf.nn.static_rnn(cell=cell, inputs=inputs, initial_state=s0)
    # rnn_outputs  [[N, lstm_size], [N, lstm_size]]
    print(rnn_outputs)

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())

        data = [
            [2.3, 2.5],
            [2.33, 2.5],
            [2.34, 2.45],
            [2.3, 23.5],
            [2.33, 2.45],
            [2.3, 2.5],
            [2.33, 2.65],
            [2.3, 2.5]
        ]
        print(sess.run([rnn_outputs, final_state], feed_dict={old_inputs: data}))


if __name__ == '__main__':
    BasicRNN()
    BasicRNN_n_steps()
    BasicLSTM()

D:\Anaconda\python.exe D:/AI20/HJZ/04-深度学习/4-RNN/20191228___AI20_RNN/02_RNN基本api.py
64 64
Tensor("basic_rnn_cell/Tanh:0", shape=(2, 4), dtype=float32) Tensor("basic_rnn_cell/Tanh:0", shape=(2, 4), dtype=float32)
Tensor("basic_rnn_cell/Tanh_1:0", shape=(2, 4), dtype=float32) Tensor("basic_rnn_cell/Tanh_1:0", shape=(2, 4), dtype=float32)
2020-02-18 10:24:39.299294: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
[[-0.48828983  0.9539078  -0.66409636  0.6394202 ]
 [-0.6233796   0.9742228  -0.8671773   0.5339205 ]] [[-0.48828983  0.9539078  -0.66409636  0.6394202 ]
 [-0.6233796   0.9742228  -0.8671773   0.5339205 ]]
****************************************************************************************************************
[[-0.4096039   0.71057636 -0.49753952  0.4326929 ]
 [-0.4899121   0.7949111  -0.80847675  0.07424883]] [[-0.4096039   0.71057636 -0.49753952  0.4326929 ]
 [-0.4899121   0.7949111  -0.80847675  0.07424883]]
[<tf.Tensor 'split:0' shape=(4, 2) dtype=float32>, <tf.Tensor 'split:1' shape=(4, 2) dtype=float32>]
[<tf.Tensor 'rnn/basic_lstm_cell/Mul_2:0' shape=(4, 3) dtype=float32>, <tf.Tensor 'rnn/basic_lstm_cell/Mul_5:0' shape=(4, 3) dtype=float32>]
[[array([[1.9140340e-01, 1.6005790e-02, 2.5509506e-01],
       [1.8950416e-01, 1.5850056e-02, 2.5679973e-01],
       [1.8151365e-01, 1.6453112e-02, 2.5943112e-01],
       [2.0262927e-02, 1.5629010e-12, 2.4361372e-05]], dtype=float32), array([[0.31692508, 0.02951055, 0.37255847],
       [0.32423702, 0.0286498 , 0.36590496],
       [0.33835158, 0.02595335, 0.35356337],
       [0.20455933, 0.01601724, 0.36540356]], dtype=float32)], LSTMStateTuple(c=array([[0.46875435, 0.19634287, 1.4682989 ],
       [0.4777323 , 0.19432597, 1.4763477 ],
       [0.49135807, 0.18868431, 1.4950181 ],
       [0.2809511 , 0.09612122, 1.6794734 ]], dtype=float32), h=array([[0.31692508, 0.02951055, 0.37255847],
       [0.32423702, 0.0286498 , 0.36590496],
       [0.33835158, 0.02595335, 0.35356337],
       [0.20455933, 0.01601724, 0.36540356]], dtype=float32))]

Process finished with exit code 0