构建生成器模块

from torch import nn
import torch
import numpy as np

# 设置隐藏的维度
latent_dim = 100
# 设置图片大小 1x28x28
img_shape = (1,28,28)

class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()

        def block(in_feat, out_feat, normalize=True):
            layers = [nn.Linear(in_feat, out_feat)]
            if normalize:
                layers.append(nn.BatchNorm1d(out_feat, 0.8))
            layers.append(nn.LeakyReLU(0.2, inplace=True))
            return layers

        self.model = nn.Sequential(
            *block(latent_dim, 128, normalize=False),
            *block(128, 256),
            *block(256, 512),
            *block(512, 1024),
            nn.Linear(1024, int(np.prod(img_shape))),
            nn.Tanh()
        )

    def forward(self, z):
        img = self.model(z)
        img = img.view(img.size(0), *img_shape)
        return img

构造生成器对象

generator = Generator()

generator.model

Sequential(
  (0): Linear(in_features=100, out_features=128, bias=True)
  (1): LeakyReLU(negative_slope=0.2, inplace=True)
  (2): Linear(in_features=128, out_features=256, bias=True)
  (3): BatchNorm1d(256, eps=0.8, momentum=0.1, affine=True, track_running_stats=True)
  (4): LeakyReLU(negative_slope=0.2, inplace=True)
  (5): Linear(in_features=256, out_features=512, bias=True)
  (6): BatchNorm1d(512, eps=0.8, momentum=0.1, affine=True, track_running_stats=True)
  (7): LeakyReLU(negative_slope=0.2, inplace=True)
  (8): Linear(in_features=512, out_features=1024, bias=True)
  (9): BatchNorm1d(1024, eps=0.8, momentum=0.1, affine=True, track_running_stats=True)
  (10): LeakyReLU(negative_slope=0.2, inplace=True)
  (11): Linear(in_features=1024, out_features=784, bias=True)
  (12): Tanh()
)