简单的深度神经网络

import numpy as np

def sigmoid(x):
    return 1/(1+np.exp(-x))
def identity_function(x):
    return x
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
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)