tensorflow16《TensorFlow实战Google深度学习框架》笔记-08-01 RNN前向传播 code

# 《TensorFlow实战Google深度学习框架》08 循环神经网络
# win10 Tensorflow1.0.1 python3.5.3
# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1
# filename:ts08.01.py # RNN前向传播

# rnn 每一层都有输出，每一次还有一个输出状态
# rnn每一层需要两组权重和偏执，一组用于该层输出，一组用于该层状态输出

import numpy as np

# 1. 定义RNN的参数
X = [1,2]
state = [0.0, 0.0]
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

# 2. 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)
'''
before activation: [ 0.6 0.5]
state: [ 0.53704957 0.46211716]
output: [ 1.56128388]
before activation: [ 1.2923401 1.39225678]
state: [ 0.85973818 0.88366641]
output: [ 2.72707101]
'''