用pytorch编写一个生物激励神经网络模型的代码

在PyTorch中，创建一个生物启发式神经网络模型通常需要定义神经元、层和整个网络结构。这里是一个简单的例子，展示如何构建一个基于Leaky ReLU激活函数的简单前馈神经网络，它可能会受到一些生物学习规则如Hebbian学习的影响：

import torch
import torch.nn as nn
import torch.optim as optim

# 定义一个生物启发式的神经元类
class BioInspiredNeuron(nn.Module):
    def __init__(self, in_features, out_features, leakiness=0.01):
        super(BioInspiredNeuron, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = nn.Parameter(torch.Tensor(out_features, in_features).uniform_(-1, 1))  # 假设权重初始化像生物突触一样随机
        self.bias = nn.Parameter(torch.zeros(out_features))
        self.leakiness = leakiness

    def forward(self, x):
        activation = torch.matmul(x, self.weight) + self.bias
        return torch.max(activation, activation * self.leakiness)

# 构建一个多层网络，例如两层
class BioNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size, num_layers=2):
        super(BioNN, self).__init__()
        self.layers = nn.ModuleList()
        for _ in range(num_layers - 1):
            self.layers.append(BioInspiredNeuron(input_size, hidden_size))
            input_size = hidden_size
        self.output_layer = BioInspiredNeuron(hidden_size, output_size)
        
    def forward(self, x):
        for layer in self.layers:
            x = layer(x)
        return self.output_layer(x)

# 使用示例
model = BioNN(input_size=10, hidden_size=20, output_size=5)
criterion = nn.CrossEntropyLoss()  # 假设是分类任务
optimizer = optim.Adam(model.parameters(), lr=0.01)  # 使用Adam优化器

# 训练过程省略，可以按照常见的PyTorch训练流程进行
# train_step(data, labels), loss.backward(), optimizer.step()