深度学习入门 3 神经网络
import numpy as np
import matplotlib.pylab as plt
def step_function(x):
return np.array(x > 0, dtype=np.int64)
x = np.arange(-5.0, 5.0, 0.1)
y = step_function(x)
plt.plot(x, y)
plt.ylim(-0.1, 1.1) # 指定y轴的范围
plt.show()
import numpy as np
import matplotlib.pylab as plt
def step_function(x):
return np.array(x > 0, dtype=np.int64)
def sigmoid(x):
return 1 / (1 + np.exp(-x))
x = np.arange(-5.0, 5.0, 0.1)
# y = step_function(x)
y = sigmoid(x)
plt.plot(x, y)
plt.ylim(-0.1, 1.1) # 指定y轴的范围
plt.show()
import matplotlib.pylab as plt
def step_function(x):
return np.array(x > 0, dtype=np.int64)
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def relu(x):
return np.maximum(0, x)
x = np.arange(-5.0, 5.0, 0.1)
# y = step_function(x)
# y = sigmoid(x)
y = relu(x)
# 绘图
plt.plot(x, y)
plt.ylim(-0.1, 1.1) # 指定y轴的范围
plt.show()
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) # [0.31682708 0.69627909]