pytoch之 encoder,decoder

  1 import torch
  2 import torch.nn as nn
  3 import torch.utils.data as Data
  4 import torchvision
  5 import matplotlib.pyplot as plt
  6 from mpl_toolkits.mplot3d import Axes3D
  7 from matplotlib import cm
  8 import numpy as np
  9 
 10 
 11 # torch.manual_seed(1)    # reproducible
 12 
 13 # Hyper Parameters
 14 EPOCH = 10
 15 BATCH_SIZE = 64
 16 LR = 0.005         # learning rate
 17 DOWNLOAD_MNIST = False
 18 N_TEST_IMG = 5
 19 
 20 # Mnist digits dataset
 21 train_data = torchvision.datasets.MNIST(
 22     root=‘./mnist/‘,
 23     train=True,                                     # this is training data
 24     transform=torchvision.transforms.ToTensor(),    # Converts a PIL.Image or numpy.ndarray to
 25                                                     # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]
 26     download=DOWNLOAD_MNIST,                        # download it if you don‘t have it
 27 )
 28 
 29 # plot one example
 30 print(train_data.train_data.size())     # (60000, 28, 28)
 31 print(train_data.train_labels.size())   # (60000)
 32 plt.imshow(train_data.train_data[2].numpy(), cmap=‘gray‘)
 33 plt.title(‘%i‘ % train_data.train_labels[2])
 34 plt.show()
 35 
 36 # Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)
 37 train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
 38 
 39 
 40 class AutoEncoder(nn.Module):
 41     def __init__(self):
 42         super(AutoEncoder, self).__init__()
 43 
 44         self.encoder = nn.Sequential(
 45             nn.Linear(28*28, 128),
 46             nn.Tanh(),
 47             nn.Linear(128, 64),
 48             nn.Tanh(),
 49             nn.Linear(64, 12),
 50             nn.Tanh(),
 51             nn.Linear(12, 3),   # compress to 3 features which can be visualized in plt
 52         )
 53         self.decoder = nn.Sequential(
 54             nn.Linear(3, 12),
 55             nn.Tanh(),
 56             nn.Linear(12, 64),
 57             nn.Tanh(),
 58             nn.Linear(64, 128),
 59             nn.Tanh(),
 60             nn.Linear(128, 28*28),
 61             nn.Sigmoid(),       # compress to a range (0, 1)
 62         )
 63 
 64     def forward(self, x):
 65         encoded = self.encoder(x)
 66         decoded = self.decoder(encoded)
 67         return encoded, decoded
 68 
 69 
 70 autoencoder = AutoEncoder()
 71 
 72 optimizer = torch.optim.Adam(autoencoder.parameters(), lr=LR)
 73 loss_func = nn.MSELoss()
 74 
 75 # initialize figure
 76 f, a = plt.subplots(2, N_TEST_IMG, figsize=(5, 2))
 77 plt.ion()   # continuously plot
 78 
 79 # original data (first row) for viewing
 80 view_data = train_data.train_data[:N_TEST_IMG].view(-1, 28*28).type(torch.FloatTensor)/255.
 81 for i in range(N_TEST_IMG):
 82     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(())
 83 
 84 for epoch in range(EPOCH):
 85     for step, (x, b_label) in enumerate(train_loader):
 86         b_x = x.view(-1, 28*28)   # batch x, shape (batch, 28*28)
 87         b_y = x.view(-1, 28*28)   # batch y, shape (batch, 28*28)
 88 
 89         encoded, decoded = autoencoder(b_x)
 90 
 91         loss = loss_func(decoded, b_y)      # mean square error
 92         optimizer.zero_grad()               # clear gradients for this training step
 93         loss.backward()                     # backpropagation, compute gradients
 94         optimizer.step()                    # apply gradients
 95 
 96         if step % 100 == 0:
 97             print(‘Epoch: ‘, epoch, ‘| train loss: %.4f‘ % loss.data.numpy())
 98 
 99             # plotting decoded image (second row)
100             _, decoded_data = autoencoder(view_data)
101             for i in range(N_TEST_IMG):
102                 a[1][i].clear()
103                 a[1][i].imshow(np.reshape(decoded_data.data.numpy()[i], (28, 28)), cmap=‘gray‘)
104                 a[1][i].set_xticks(()); a[1][i].set_yticks(())
105             plt.draw(); plt.pause(0.05)
106 
107 plt.ioff()
108 plt.show()
109 
110 # visualize in 3D plot
111 view_data = train_data.train_data[:200].view(-1, 28*28).type(torch.FloatTensor)/255.
112 encoded_data, _ = autoencoder(view_data)
113 fig = plt.figure(2); ax = Axes3D(fig)
114 X, Y, Z = encoded_data.data[:, 0].numpy(), encoded_data.data[:, 1].numpy(), encoded_data.data[:, 2].numpy()
115 values = train_data.train_labels[:200].numpy()
116 for x, y, z, s in zip(X, Y, Z, values):
117     c = cm.rainbow(int(255*s/9)); ax.text(x, y, z, s, backgroundcolor=c)
118 ax.set_xlim(X.min(), X.max()); ax.set_ylim(Y.min(), Y.max()); ax.set_zlim(Z.min(), Z.max())
119 plt.show()