有关BP神经网络图像分类的代码
时间: 2023-11-22 15:48:46 浏览: 29
以下是BP神经网络图像分类的Python代码示例:
```python
import numpy as np
import matplotlib.pyplot as plt
# 定义sigmoid函数
def sigmoid(x):
return 1 / (1 + np.exp(-x))
# 定义sigmoid函数的导数
def sigmoid_derivative(x):
return x * (1 - x)
# 定义BP神经网络类
class NeuralNetwork:
def __init__(self, x, y):
self.input = x
self.weights1 = np.random.rand(self.input.shape[1], 4)
self.weights2 = np.random.rand(4, 1)
self.y = y
self.output = np.zeros(self.y.shape)
def feedforward(self):
self.layer1 = sigmoid(np.dot(self.input, self.weights1))
self.output = sigmoid(np.dot(self.layer1, self.weights2))
def backprop(self):
d_weights2 = np.dot(self.layer1.T, (2*(self.y - self.output) * sigmoid_derivative(self.output)))
d_weights1 = np.dot(self.input.T, (np.dot(2*(self.y - self.output) * sigmoid_derivative(self.output), self.weights2.T) * sigmoid_derivative(self.layer1)))
self.weights1 += d_weights1
self.weights2 += d_weights2
# 构造数据集
X = np.array([[0, 0, 1],
[0, 1, 1],
[1, 0, 1],
[1, 1, 1]])
y = np.array([[0],[1],[1],[0]])
# 构造BP神经网络
nn = NeuralNetwork(X, y)
# 训练BP神经网络
for i in range(1500):
nn.feedforward()
nn.backprop()
# 输出预测结果
print(nn.output)
# 绘制损失函数曲线
plt.plot(range(1500), nn.loss)
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.show()
```