bp神经网络多元回归预测单层隐藏层relu函数代码
时间: 2023-12-31 18:06:05 浏览: 29
以下是一个使用单层隐藏层和ReLU函数的BP神经网络多元回归预测的代码示例:
```python
import numpy as np
class BPNN:
def __init__(self, input_size, hidden_size, output_size):
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
# 初始化权重和偏置
self.W1 = np.random.randn(self.input_size, self.hidden_size) / np.sqrt(self.input_size)
self.b1 = np.zeros((1, self.hidden_size))
self.W2 = np.random.randn(self.hidden_size, self.output_size) / np.sqrt(self.hidden_size)
self.b2 = np.zeros((1, self.output_size))
def relu(self, x):
return np.maximum(0, x)
def relu_derivative(self, x):
x[x<=0] = 0
x[x>0] = 1
return x
def forward(self, X):
# 前向传播
self.z1 = np.dot(X, self.W1) + self.b1
self.a1 = self.relu(self.z1)
self.z2 = np.dot(self.a1, self.W2) + self.b2
self.y_hat = self.z2
def backward(self, X, y, learning_rate):
# 反向传播
delta3 = self.y_hat - y
dW2 = np.dot(self.a1.T, delta3)
db2 = np.sum(delta3, axis=0, keepdims=True)
delta2 = np.dot(delta3, self.W2.T) * self.relu_derivative(self.a1)
dW1 = np.dot(X.T, delta2)
db1 = np.sum(delta2, axis=0)
# 更新权重和偏置
self.W2 -= learning_rate * dW2
self.b2 -= learning_rate * db2
self.W1 -= learning_rate * dW1
self.b1 -= learning_rate * db1
def train(self, X, y, epochs, learning_rate):
for i in range(epochs):
self.forward(X)
self.backward(X, y, learning_rate)
def predict(self, X):
self.forward(X)
return self.y_hat
```
其中,`input_size`、`hidden_size`和`output_size`分别表示输入层、隐藏层和输出层的神经元数量;`W1`、`b1`、`W2`和`b2`分别表示输入层到隐藏层、隐藏层到输出层的权重矩阵和偏置向量;`relu`函数是ReLU激活函数,`relu_derivative`函数是ReLU函数的导数函数;`forward`函数实现前向传播,`backward`函数实现反向传播,`train`函数用于训练模型,`predict`函数用于预测结果。