bp神经网络三层回归模型代码
时间: 2023-08-08 20:09:59 浏览: 50
下面是一个使用BP神经网络实现三层回归模型的示例代码:
```python
import numpy as np
class NeuralNetwork:
def __init__(self, input_size, hidden_size, output_size):
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
# 初始化网络参数
self.weights1 = np.random.randn(input_size, hidden_size)
self.bias1 = np.zeros((1, hidden_size))
self.weights2 = np.random.randn(hidden_size, output_size)
self.bias2 = np.zeros((1, output_size))
def forward(self, x):
# 前向传播过程
self.z1 = np.dot(x, self.weights1) + self.bias1
self.a1 = np.tanh(self.z1)
self.z2 = np.dot(self.a1, self.weights2) + self.bias2
self.a2 = self.z2
return self.a2
def backward(self, x, y, learning_rate):
# 反向传播过程
delta2 = (self.a2 - y)
d_weights2 = np.dot(self.a1.T, delta2)
d_bias2 = np.sum(delta2, axis=0, keepdims=True)
delta1 = np.dot(delta2, self.weights2.T) * (1 - np.power(self.a1, 2))
d_weights1 = np.dot(x.T, delta1)
d_bias1 = np.sum(delta1, axis=0)
# 更新参数
self.weights1 -= learning_rate * d_weights1
self.bias1 -= learning_rate * d_bias1
self.weights2 -= learning_rate * d_weights2
self.bias2 -= learning_rate * d_bias2
def train(self, x, y, learning_rate, num_epochs):
for epoch in range(num_epochs):
# 前向传播
output = self.forward(x)
# 反向传播
self.backward(x, y, learning_rate)
# 计算损失函数
loss = np.mean(np.square(output - y))
# 打印损失函数值
if epoch % 1000 == 0:
print("Epoch %d, Loss: %.5f" % (epoch, loss))
def predict(self, x):
# 预测过程
return self.forward(x)
```
这个神经网络模型具有一个输入层、一个隐藏层和一个输出层,并且隐藏层的激活函数为tanh,输出层没有激活函数。在训练过程中,使用均方误差作为损失函数,并通过梯度下降法来更新网络参数。在预测过程中,直接使用前向传播计算模型的输出结果。