129 lines
4.3 KiB
Python
129 lines
4.3 KiB
Python
"""
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View more, visit my tutorial page: https://mofanpy.com/tutorials/
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My Youtube Channel: https://www.youtube.com/user/MorvanZhou
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Dependencies:
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torch: 0.4
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matplotlib
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numpy
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"""
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import torch
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import torch.nn as nn
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import torch.utils.data as Data
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import torchvision
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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from matplotlib import cm
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import numpy as np
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# torch.manual_seed(1) # reproducible
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# Hyper Parameters
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EPOCH = 10
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BATCH_SIZE = 64
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LR = 0.005 # learning rate
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DOWNLOAD_MNIST = False
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N_TEST_IMG = 5
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# Mnist digits dataset
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train_data = torchvision.datasets.MNIST(
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root='./mnist/',
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train=True, # this is training data
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transform=torchvision.transforms.ToTensor(), # Converts a PIL.Image or numpy.ndarray to
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# torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]
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download=DOWNLOAD_MNIST, # download it if you don't have it
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)
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# plot one example
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print(train_data.train_data.size()) # (60000, 28, 28)
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print(train_data.train_labels.size()) # (60000)
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plt.imshow(train_data.train_data[2].numpy(), cmap='gray')
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plt.title('%i' % train_data.train_labels[2])
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plt.show()
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# Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)
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train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
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class AutoEncoder(nn.Module):
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def __init__(self):
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super(AutoEncoder, self).__init__()
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self.encoder = nn.Sequential(
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nn.Linear(28*28, 128),
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nn.Tanh(),
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nn.Linear(128, 64),
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nn.Tanh(),
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nn.Linear(64, 12),
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nn.Tanh(),
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nn.Linear(12, 3), # compress to 3 features which can be visualized in plt
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)
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self.decoder = nn.Sequential(
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nn.Linear(3, 12),
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nn.Tanh(),
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nn.Linear(12, 64),
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nn.Tanh(),
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nn.Linear(64, 128),
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nn.Tanh(),
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nn.Linear(128, 28*28),
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nn.Sigmoid(), # compress to a range (0, 1)
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)
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def forward(self, x):
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encoded = self.encoder(x)
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decoded = self.decoder(encoded)
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return encoded, decoded
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autoencoder = AutoEncoder()
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optimizer = torch.optim.Adam(autoencoder.parameters(), lr=LR)
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loss_func = nn.MSELoss()
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# initialize figure
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f, a = plt.subplots(2, N_TEST_IMG, figsize=(5, 2))
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plt.ion() # continuously plot
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# original data (first row) for viewing
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view_data = train_data.train_data[:N_TEST_IMG].view(-1, 28*28).type(torch.FloatTensor)/255.
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for i in range(N_TEST_IMG):
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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(())
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for epoch in range(EPOCH):
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for step, (x, b_label) in enumerate(train_loader):
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b_x = x.view(-1, 28*28) # batch x, shape (batch, 28*28)
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b_y = x.view(-1, 28*28) # batch y, shape (batch, 28*28)
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encoded, decoded = autoencoder(b_x)
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loss = loss_func(decoded, b_y) # mean square error
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optimizer.zero_grad() # clear gradients for this training step
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loss.backward() # backpropagation, compute gradients
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optimizer.step() # apply gradients
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if step % 100 == 0:
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print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy())
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# plotting decoded image (second row)
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_, decoded_data = autoencoder(view_data)
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for i in range(N_TEST_IMG):
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a[1][i].clear()
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a[1][i].imshow(np.reshape(decoded_data.data.numpy()[i], (28, 28)), cmap='gray')
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a[1][i].set_xticks(()); a[1][i].set_yticks(())
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plt.draw(); plt.pause(0.05)
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plt.ioff()
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plt.show()
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# visualize in 3D plot
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view_data = train_data.train_data[:200].view(-1, 28*28).type(torch.FloatTensor)/255.
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encoded_data, _ = autoencoder(view_data)
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fig = plt.figure(2); ax = Axes3D(fig)
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X, Y, Z = encoded_data.data[:, 0].numpy(), encoded_data.data[:, 1].numpy(), encoded_data.data[:, 2].numpy()
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values = train_data.train_labels[:200].numpy()
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for x, y, z, s in zip(X, Y, Z, values):
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c = cm.rainbow(int(255*s/9)); ax.text(x, y, z, s, backgroundcolor=c)
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ax.set_xlim(X.min(), X.max()); ax.set_ylim(Y.min(), Y.max()); ax.set_zlim(Z.min(), Z.max())
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plt.show()
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