在生成对抗网络特征统计混合正则化的模型中，损失函数包括两部分：对抗性损失和FSMR正则化损失的模型代码

下面是一个基于PyTorch框架的生成对抗网络特征统计混合正则化的模型的代码，其中包括了对抗性损失和FSMR正则化损失的损失函数：

import torch
import torch.nn as nn
from torch.autograd import Variable

class Generator(nn.Module):
    def __init__(self, input_dim=100, output_dim=1, input_size=32):
        super(Generator, self).__init__()

        self.input_dim = input_dim
        self.output_dim = output_dim
        self.input_size = input_size

        self.fc1 = nn.Linear(self.input_dim, 256)
        self.fc2 = nn.Linear(256, 512)
        self.fc3 = nn.Linear(512, 1024)
        self.fc4 = nn.Linear(1024, self.output_dim*self.input_size*self.input_size)
        self.bn1 = nn.BatchNorm1d(256)
        self.bn2 = nn.BatchNorm1d(512)
        self.bn3 = nn.BatchNorm1d(1024)
        self.bn4 = nn.BatchNorm2d(self.output_dim)

    def forward(self, x):
        x = nn.LeakyReLU(0.2)(self.bn1(self.fc1(x)))
        x = nn.LeakyReLU(0.2)(self.bn2(self.fc2(x)))
        x = nn.LeakyReLU(0.2)(self.bn3(self.fc3(x)))
        x = self.bn4(self.fc4(x))
        x = x.view(-1, self.output_dim, self.input_size, self.input_size)
        x = nn.Sigmoid()(x)
        return x

class Discriminator(nn.Module):
    def __init__(self, input_dim=1, output_dim=1, input_size=32):
        super(Discriminator, self).__init__()

        self.input_dim = input_dim
        self.output_dim = output_dim
        self.input_size = input_size

        self.conv1 = nn.Conv2d(self.input_dim, 64, 4, 2, 1)
        self.conv2 = nn.Conv2d(64, 128, 4, 2, 1)
        self.conv3 = nn.Conv2d(128, 256, 4, 2, 1)
        self.conv4 = nn.Conv2d(256, 512, 4, 2, 1)
        self.conv5 = nn.Conv2d(512, self.output_dim, 4, 1, 0)
        self.bn1 = nn.BatchNorm2d(64)
        self.bn2 = nn.BatchNorm2d(128)
        self.bn3 = nn.BatchNorm2d(256)
        self.bn4 = nn.BatchNorm2d(512)

    def forward(self, x):
        x = nn.LeakyReLU(0.2)(self.bn1(self.conv1(x)))
        x = nn.LeakyReLU(0.2)(self.bn2(self.conv2(x)))
        x = nn.LeakyReLU(0.2)(self.bn3(self.conv3(x)))
        x = nn.LeakyReLU(0.2)(self.bn4(self.conv4(x)))
        x = self.conv5(x)
        x = x.view(-1, self.output_dim)
        x = nn.Sigmoid()(x)
        return x

class GAN(nn.Module):
    def __init__(self, input_dim=100, output_dim=1, input_size=32):
        super(GAN, self).__init__()

        self.input_dim = input_dim
        self.output_dim = output_dim
        self.input_size = input_size

        self.generator = Generator(self.input_dim, self.output_dim, self.input_size)
        self.discriminator = Discriminator(self.output_dim, self.output_dim, self.input_size)

    def forward(self, x):
        return self.generator(x)

    def backward_D(self, real_images, fake_images, optimizer_D):
        optimizer_D.zero_grad()

        # real images
        real_logits = self.discriminator(real_images)
        real_labels = Variable(torch.ones(real_logits.size())).cuda()
        real_loss = nn.BCELoss()(real_logits, real_labels)

        # fake images
        fake_logits = self.discriminator(fake_images.detach())
        fake_labels = Variable(torch.zeros(fake_logits.size())).cuda()
        fake_loss = nn.BCELoss()(fake_logits, fake_labels)

        # total loss
        d_loss = real_loss + fake_loss
        d_loss.backward()
        optimizer_D.step()

        return d_loss

    def backward_G(self, fake_images, optimizer_G, feature_statistic_fn, lambda_f):
        optimizer_G.zero_grad()

        # adversarial loss
        fake_logits = self.discriminator(fake_images)
        real_labels = Variable(torch.ones(fake_logits.size())).cuda()
        adversarial_loss = nn.BCELoss()(fake_logits, real_labels)

        # feature statistic loss
        feature_statistic_loss = feature_statistic_fn(fake_images)

        # total loss
        g_loss = adversarial_loss + lambda_f * feature_statistic_loss
        g_loss.backward()
        optimizer_G.step()

        return g_loss

其中，`Generator`是生成器模型，`Discriminator`是判别器模型，`GAN`是整个生成对抗网络模型。在`backward_D`函数中，计算了对抗性损失；在`backward_G`函数中，计算了对抗性损失和FSMR正则化损失。在`backward_G`函数中，`feature_statistic_fn`是用来计算特征统计值的函数，`lambda_f`是FSMR正则化超参数。