实现3层神经网络实现简单的三层神经网络恒等函数和softmax函数

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# 教材p63 手动实现3层的神经网络
import numpy as np
# 从sigmoid.py文件中导入sigmoid函数
from sigmoid import sigmoid


def identity_function(x):
    return x


# init_network()函数用于对每一层权重和偏置进行初始化，并将它们保存在字典变量network中
def init_network():
    network = {}
    network['W1'] = np.array([[0.1, 0.3, 0.5], [0.2, 0.4, 0.6]])
    network['b1'] = np.array([0.1, 0.2, 0.3])
    network['W2'] = np.array([[0.1, 0.4], [0.2, 0.5], [0.3, 0.6]])
    network['b2'] = np.array([0.1, 0.2])
    network['W3'] = np.array([[0.1, 0.3], [0.2, 0.4]])
    network['b3'] = np.array([0.1, 0.2])
    return network


# forward()函数用于封装将输入信号转换为输出信号的处理过程
def forward(network, x):
    W1, W2, W3 = network['W1'], network['W2'], network['W3']
    b1, b2, b3 = network['b1'], network['b2'], network['b3']

    a1 = np.dot(x, W1) + b1
    z1 = sigmoid(a1)
    a2 = np.dot(z1, W2) + b2
    z2 = sigmoid(a2)
    a3 = np.dot(z2, W3) + b3
    y = identity_function(a3)

    return y


network = init_network()
x = np.array([1.0, 0.5])
y = forward(network, x)
print(y)

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def softmax(a):
	c = np.max(a)
	exp_a = np.exp(a - c) # 溢出对策
	sum_exp_a = np.sum(exp_a)
	y = exp_a / sum_exp_a
	return y