밑딥1 - 2.신경망

def step_function(x):
    y = x > 0
    return y.astype(np.int32)

 

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(-5.0, 5.0, 0.1)
y = step_function(x)
plt.plot(x, y)
plt.ylim(-0.1, 1.1)
plt.show()

 

 

 

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

 

x = np.arange(-5.0, 5.0, 0.1)
y = sigmoid(x)
plt.plot(x, y)
plt.ylim(-0.1, 1.1)
plt.show()

def relu(x):
    return np.maximum(0, x)

 

 

 

X = np.array([1, 0.5])
W1 = np.array([
    [0.1, 0.3, 0.5],
    [0.2, 0.4, 0.6]
])
B1 = np.array([0.1, 0.2, 0.3])

print(W1.shape)
print(X.shape)
print(B1.shape)

A1 = np.dot(X, W1) + B1
print(A1.shape)

 

Z1 = sigmoid(A1)
print(A1)
print(Z1)

 

 

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)

 

 

def softmax(a):
    C = np.max(a)
    exp_a = np.exp( a - C)
    sum_exp_a = np.sum(exp_a)
    y = exp_a / sum_exp_a
    return y