我试图实现反向传播到我的简单神经网络,它看起来像这样:2个输入,2个隐藏(sigmoid),1个输出(sigmoid)。但它似乎不能正常工作。你知道吗
import numpy as np
# Set inputs and labels
X = np.array([ [0, 1],
[0, 1],
[1, 0],
[1, 0] ])
Y = np.array([[0, 0, 1, 1]]).T
# Make random always the same
np.random.seed(1)
# Initialize weights
w_0 = 2 * np.random.rand(2, 2) - 1
w_1 = 2 * np.random.rand(1, 2) - 1
# Learning Rate
lr = 0.1
# Sigmoid Function/Derivative of Sigmoid Function
def sigmoid(x, deriv=False):
if(deriv==True):
return x * (1 - x)
return 1/(1 + np.exp(-x))
# Neural network
def network(x, y, w_0, w_1):
inputs = np.array(x, ndmin=2).T
label = np.array(y, ndmin=2).T
# Forward Pass
hidden = sigmoid(np.dot(w_0, inputs))
output = sigmoid(np.dot(w_1, hidden))
# Calculate error and delta
error = label - output
delta = error * sigmoid(output, True)
hidden_error = np.dot(w_1.T, error)
delta_hidden = error * sigmoid(hidden, True)
# Update weight
w_1 += np.dot(delta, hidden.T) * lr
w_0 += np.dot(delta_hidden, record.T) * lr
return error
# Train
for i in range(6000):
for j in range(X.shape[0]):
error = network(X[j], Y[j], w_0, w_1)
if(i%1000==0):
print(error)
这是不对的,因为它不接近0。你知道吗
当我将delta改为:
delta = error
但为什么呢?在进一步传递之前,我们不应该把误差乘以sigmoid函数的导数吗?你知道吗
我想,应该是
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