请用pytorch构建复值神经网络

好的，以下是用PyTorch构建复值神经网络的示例代码：

import torch
import torch.nn as nn

class ComplexLinear(nn.Module):
    def __init__(self, in_features, out_features, bias=True):
        super().__init__()
        self.weight_r = nn.Parameter(torch.Tensor(out_features, in_features))
        self.weight_i = nn.Parameter(torch.Tensor(out_features, in_features))
        if bias:
            self.bias_r = nn.Parameter(torch.Tensor(out_features))
            self.bias_i = nn.Parameter(torch.Tensor(out_features))
        else:
            self.register_parameter('bias_r', None)
            self.register_parameter('bias_i', None)
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.kaiming_uniform_(self.weight_r, a=math.sqrt(5))
        nn.init.kaiming_uniform_(self.weight_i, a=math.sqrt(5))
        if self.bias_r is not None:
            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight_r)
            bound = 1 / math.sqrt(fan_in)
            nn.init.uniform_(self.bias_r, -bound, bound)
            nn.init.uniform_(self.bias_i, -bound, bound)

    def forward(self, input):
        output_r = nn.functional.linear(input.real, self.weight_r, self.bias_r)
        output_i = nn.functional.linear(input.imag, self.weight_i, self.bias_i)
        return torch.complex(output_r, output_i)

class ComplexMLP(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.fc1 = ComplexLinear(input_size, hidden_size)
        self.fc2 = ComplexLinear(hidden_size, output_size)

    def forward(self, input):
        x = self.fc1(input)
        x = torch.tanh(x)
        x = self.fc2(x)
        return x

这是一个简单的复值多层感知机（MLP）模型，包含两个复值全连接层（ComplexLinear），激活函数使用的是双曲正切函数。你可以根据自己的需求修改模型的参数和结构。