可变多隐层神经网络的python实现

很高兴回答你的问题！以下是可变多隐层神经网络的Python实现：

import numpy as np

class VariableMLP:
    def __init__(self, input_dim, num_hidden_layers, hidden_layer_sizes, output_dim):
        self.input_dim = input_dim
        self.num_hidden_layers = num_hidden_layers
        self.hidden_layer_sizes = hidden_layer_sizes
        self.output_dim = output_dim
        
        self.weights = []
        self.biases = []
        
        # Initialize weights and biases for each layer
        prev_size = input_dim
        for i in range(num_hidden_layers):
            size = hidden_layer_sizes[i]
            layer_weights = np.random.rand(prev_size, size)
            layer_biases = np.random.rand(size)
            self.weights.append(layer_weights)
            self.biases.append(layer_biases)
            prev_size = size
        
        # Initialize weights and biases for output layer
        layer_weights = np.random.rand(prev_size, output_dim)
        layer_biases = np.random.rand(output_dim)
        self.weights.append(layer_weights)
        self.biases.append(layer_biases)
            
    def forward(self, x):
        # Feed forward through each layer
        for i in range(self.num_hidden_layers):
            x = np.dot(x, self.weights[i]) + self.biases[i]
            x = self.relu(x)
            
        output = np.dot(x, self.weights[-1]) + self.biases[-1]
        return output
    
    def relu(self, x):
        return np.maximum(0, x)

希望能够帮助到您！如果您还有其他问题，请随时问我。