神经网络算法模板python

神经网络算法模板的Python代码如下所示：

import numpy as np

class NeuralNetwork:
    def __init__(self, num_inputs, num_hidden, num_outputs):
        self.num_inputs = num_inputs
        self.num_hidden = num_hidden
        self.num_outputs = num_outputs
        
        self.weights1 = np.random.randn(self.num_inputs, self.num_hidden)
        self.weights2 = np.random.randn(self.num_hidden, self.num_outputs)
        
    def forward(self, inputs):
        self.hidden = np.dot(inputs, self.weights1)
        self.hidden_activation = self.sigmoid(self.hidden)
        
        self.output = np.dot(self.hidden_activation, self.weights2)
        self.output_activation = self.sigmoid(self.output)
        
        return self.output_activation
    
    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))
    
    def sigmoid_derivative(self, x):
        return x * (1 - x)
    
    def backward(self, inputs, targets, learning_rate):
        output_error = targets - self.output_activation
        output_delta = output_error * self.sigmoid_derivative(self.output_activation)
        
        hidden_error = output_delta.dot(self.weights2.T)
        hidden_delta = hidden_error * self.sigmoid_derivative(self.hidden_activation)
        
        self.weights2 += self.hidden_activation.T.dot(output_delta) * learning_rate
        self.weights1 += inputs.T.dot(hidden_delta) * learning_rate
        
    def train(self, inputs, targets, num_epochs, learning_rate):
        for epoch in range(num_epochs):
            output = self.forward(inputs)
            self.backward(inputs, targets, learning_rate)
        
    def predict(self, inputs):
        return self.forward(inputs)

这是一个简单的三层全连接神经网络的代码模板。通过调用`NeuralNetwork`类的方法，可以进行训练和预测。你可以根据自己的需求和数据进行相应的修改和调整。