帮我写一个神经网络分类模型代码
时间: 2023-03-28 12:02:14 浏览: 53
好的,以下是一个简单的神经网络分类模型代码:
```python
import numpy as np
# 定义 sigmoid 函数
def sigmoid(x):
return 1 / (1 + np.exp(-x))
# 定义神经网络模型
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
# 初始化权重
self.weights1 = np.random.randn(self.input_size, self.hidden_size)
self.weights2 = np.random.randn(self.hidden_size, self.output_size)
def forward(self, X):
# 前向传播
self.z = np.dot(X, self.weights1)
self.z2 = sigmoid(self.z)
self.z3 = np.dot(self.z2, self.weights2)
y_hat = sigmoid(self.z3)
return y_hat
def backward(self, X, y, y_hat, learning_rate):
# 反向传播
delta3 = (y_hat - y) * sigmoid(self.z3) * (1 - sigmoid(self.z3))
d_weights2 = np.dot(self.z2.T, delta3)
delta2 = np.dot(delta3, self.weights2.T) * sigmoid(self.z2) * (1 - sigmoid(self.z2))
d_weights1 = np.dot(X.T, delta2)
# 更新权重
self.weights1 -= learning_rate * d_weights1
self.weights2 -= learning_rate * d_weights2
def train(self, X, y, learning_rate, epochs):
for i in range(epochs):
y_hat = self.forward(X)
self.backward(X, y, y_hat, learning_rate)
def predict(self, X):
y_hat = self.forward(X)
return np.round(y_hat)
```
希望能对你有所帮助!