dnn1.py

#%%
import numpy as np 

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

def softmax(x):
    exp_x = np.exp(x)
    sum_exp_x = np.sum(exp_x)
    y = exp_x / sum_exp_x
    return y

def indentify_function(x):
    return x 

def int_network():
    network = {}
    network['W1'] = np.array([[0.1, 0.3, 0.5], [0.2, 0.4, 0.6], [0.3, 0.5, 0.7]])
    network['b1'] = np.array([0.1, 0.2, 0.3])
    network['W2'] = np.array([[0.1, 0.4, 0.5], [0.2, 0.5, 0.7], [0.3, 0.6, 0.8]])
    network['b2'] = np.array([0.1, 0.2, 0.3])
    network['W3'] = np.array([[0.1, 0.3, 0.4], [0.2, 0.4, 0.5], [0.3, 0.5, 0.6]])
    network['b3'] = np.array([0.1, 0.2, 0.3])
    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 = softmax(a3)
    y = a3
    return y

network = int_network()
x = np.array([1.0, 0.5, 0.3])
y1 = forward(network, x)
print y1
y2 = softmax(y1)
print y2