pytoch之 encoder,decoder

   ###仅为自己练习，没有其他用途

  1 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
 import numpy as np

 # torch.manual_seed(1)    # reproducible

 # Hyper Parameters
 EPOCH = 10
 BATCH_SIZE = 64
 LR = 0.005         # learning rate
 DOWNLOAD_MNIST = False
 N_TEST_IMG = 5

 # Mnist digits dataset
 train_data = torchvision.datasets.MNIST(
     root='./mnist/',
     train=True,                                     # this is training data
     transform=torchvision.transforms.ToTensor(),    # Converts a PIL.Image or numpy.ndarray to
                                                     # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]
     download=DOWNLOAD_MNIST,                        # download it if you don't have it
 )

 # plot one example
 print(train_data.train_data.size())     # (60000, 28, 28)
 print(train_data.train_labels.size())   # (60000)
 plt.imshow(train_data.train_data[2].numpy(), cmap='gray')
 plt.title('%i' % train_data.train_labels[2])
 plt.show()

 # Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)
 train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)

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

         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),   # compress to 3 features which can be visualized in plt
         )
         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(),       # compress to a range (0, 1)
         )

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

 autoencoder = AutoEncoder()

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

 # initialize figure
 f, a = plt.subplots(2, N_TEST_IMG, figsize=(5, 2))
 plt.ion()   # continuously plot

 # original data (first row) for viewing
 view_data = 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)   # batch x, shape (batch, 28*28)
         b_y = x.view(-1, 28*28)   # batch y, shape (batch, 28*28)

         encoded, decoded = autoencoder(b_x)

         loss = loss_func(decoded, b_y)      # mean square error
         optimizer.zero_grad()               # clear gradients for this training step
         loss.backward()                     # backpropagation, compute gradients
         optimizer.step()                    # apply gradients

         if step % 100 == 0:
             print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy())

             # plotting decoded image (second row)
             _, decoded_data = autoencoder(view_data)
             for i in range(N_TEST_IMG):
                 a[1][i].clear()
                 a[1][i].imshow(np.reshape(decoded_data.data.numpy()[i], (28, 28)), cmap='gray')
                 a[1][i].set_xticks(()); a[1][i].set_yticks(())
             plt.draw(); plt.pause(0.05)

 plt.ioff()
 plt.show()

 # visualize in 3D plot
 view_data = train_data.train_data[:200].view(-1, 28*28).type(torch.FloatTensor)/255.
 encoded_data, _ = autoencoder(view_data)
 fig = plt.figure(2); ax = Axes3D(fig)
 X, Y, Z = encoded_data.data[:, 0].numpy(), encoded_data.data[:, 1].numpy(), encoded_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_ylim(Y.min(), Y.max()); ax.set_zlim(Z.min(), Z.max())
 plt.show()