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
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