Tensorflow细节-P212-循环神经网络

本节的循环神经网络一图足以说明

import numpy as np

X = [1, 2]

state = [0.0, 0.0]

# 定义RNN的参数

# 以下两个本来是像这样分开的，但是在运算时合并了

w_cell_state = np.asarray([[0.1, 0.2], [0.3, 0.4]])

w_cell_input = np.asarray([0.5, 0.6])

b_cell = np.asarray([0.1, -0.1])

w_output = np.asarray([[1.0], [2.0]])

b_output = 0.1

# 执行前向传播过程

for i in range(len(X)):

    before_activation = np.dot(state, w_cell_state) + X[i] * w_cell_input + b_cell

    state = np.tanh(before_activation)

    final_output = np.dot(state, w_output) + b_output

    print("before activation: ", before_activation)

    print("state: ", state)

    print("output: ", final_output)

计算结果：

巴特西

Tensorflow细节-P212-循环神经网络

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