Pytorch AutoEncoder 自编码 全部源代码复现

PyTorch 自编码的实现：

下面是显示效果，感觉还是可以的，最起码该出现的都出现了！

import torch
import torch.nn as nn
import torch.utils.data as Data
import torchvision
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from torch.autograd import Variable
import numpy as np

# 超参数定义
Epoch = 10
Batch_size = 100
LR = 0.005
Downloads_MNIST = False
N_Test_img = 5  # 测试图片的显示效果，5张为一批

train_data = torchvision.datasets.MNIST(
    root=r'./mnist_data/',
    train=True,
    transform=torchvision.transforms.ToTensor(),
    download=Downloads_MNIST
)
print(train_data.train_data.size())

train_loader = Data.DataLoader(dataset=train_data, batch_size=Batch_size, shuffle=True)


class AutoEncoder(nn.Module):
    def __init__(self):
        super(AutoEncoder, self).__init__()
        # 定义编码网络
        self.encoder = nn.Sequential(
            nn.Linear(28 * 28, 128),
            nn.Tanh(),
            nn.Linear(128, 64),
            nn.Tanh(),
            nn.Linear(64, 12),
            nn.Tanh(),
            nn.Linear(12, 3)
            # 压缩成3个特征进行3D图像可视化
        )
        # 定义解码网络
        self.decoder = nn.Sequential(
            nn.Linear(3, 12),
            nn.Tanh(),
            nn.Linear(12, 64),
            nn.Tanh(),
            nn.Linear(64, 128),
            nn.Tanh(),
            nn.Linear(128, 28 * 28),
            nn.Sigmoid()
        )

    def forward(self, x):
        encoder = self.encoder(x)
        decoder = self.decoder(encoder)
        return encoder, decoder


if __name__ == '__main__':
    autoencoder = AutoEncoder()

    optimizer = torch.optim.Adam(autoencoder.parameters(), lr=LR)
    loss_func = nn.MSELoss()

    # 初始化图像
    f, a = plt.subplots(2, N_Test_img, figsize=(5, 2))
    plt.ion()
    view_data = Variable(train_data.train_data[:N_Test_img].view(-1, 28 * 28).type(torch.FloatTensor) / 255.)

    for i in range(N_Test_img):
        a[0][i].imshow(np.reshape(view_data.data.numpy()[i], (28, 28)), cmap='gray')
        a[0][i].set_xticks(())
        a[0][i].set_yticks(())

    for epoch in range(Epoch):
        for step, (x, b_label) in enumerate(train_loader):
            b_x = x.view(-1, 28 * 28)
            b_y = x.view(-1, 28 * 28)

            encoder_out, decoder_out = autoencoder(b_x)

            loss = loss_func(decoder_out, b_y)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            if step % 100 == 0:
                print('Epoch:{}, Train_loss:{:.4f}'.format(epoch, loss.item()))

                _, decoder_data = autoencoder(view_data)
                for i in range(N_Test_img):
                    a[1][i].clear()
                    a[1][i].imshow(np.reshape(decoder_data.data.numpy()[i], (28, 28)), cmap='gray')
                    a[1][i].set_xticks(())
                    a[1][i].set_yticks(())
                plt.draw()
                plt.pause(0.08)
    plt.ioff()
    plt.show()

    view_data = train_data.train_data[:200].view(-1, 28 * 28).type(torch.FloatTensor) / 255.
    encoder_data, _ = autoencoder(view_data)
    fig = plt.figure(2)
    ax = Axes3D(fig) #这个3D画图
    X = encoder_data.data[:, 0].numpy()
    Y = encoder_data.data[:, 1].numpy()
    Z = encoder_data.data[:, 2].numpy()
    values = train_data.train_labels[:200].numpy()
    for x, y, z, s in zip(X, Y, Z, values):
        c = cm.rainbow(int(255 * s / 9))
        ax.text(x, y, z, s, backgroundcolor=c)
    ax.set_xlim(X.min(), X.max())
    ax.set_xlim(Y.min(), Y.max())
    ax.set_xlim(Z.min(), Z.max())
    plt.show()

这个3D画图出来的效果