3층 신경망(3 layer neural network)

#3 layer neural network

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)