循环网络

循环网络的前向传播过程

import numpy as np

X = [1,2]
state = [0.0,0.0]

# 分开定义不同输入部分的权重以方便操作
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])

#定义用于输出的全连接层参数
w_output = np.asarray([1.0,2.0])
b_output = 0.1

#按照时间顺序执行循环神经网络的前向传播过程
for i in range(len(X)):
    #计算循环体中的全连接层神经网络
    before_activation = np.dot(state,w_cell_state) + X[i] * w_cell_input + b_cell
    state = np.tanh(before_activation)
    
    #根据当前时刻状态计算最终输出
    final_output = np.dot(state,w_output) + b_output
    
    print "before activate: ",before_activation
    print "state: ",state 
    print "output: ",final_output
    print "\n"

before activate:  [0.6 0.5]
state:  [0.53704957 0.46211716]
output:  1.561283881518055


before activate:  [1.2923401  1.39225678]
state:  [0.85973818 0.88366641]
output:  2.727071008233731

来源：CSDN

作者：Mikowoo007

链接：https://blog.csdn.net/Mikowoo007/article/details/103569690