PyTorch實現簡單的自動編碼器autoencoder

      自動編碼器包括編碼器(Encoder)和解碼器(Decoder)兩部分，編碼器和解碼器都可以是任意的模型，目前神經網絡模型用的較多。輸入的數據經過神經網絡降維到一個編碼(coder)，然后又通過一個神經網絡去解碼得到一個與原輸入數據一模一樣的生成數據，然后通過比較這兩個數據，最小化它們之間的差異來訓練這個網絡中的編碼器和解碼器的參數，當這個過程訓練完之后，拿出這個解碼器，隨機傳入一個編碼，通過解碼器能夠生成一個和原數據差不多的數據。^[1]

      莫煩的PyTorch系列教程^[2]中有關於自動編碼器的介紹以及實現簡單的自動編碼器的代碼。為方便查看，代碼摘錄如下：

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='/Users/wangpeng/Desktop/all/CS/Courses/Deep Learning/mofan_PyTorch/mnist/',    # mnist has been downloaded before, use it directly
    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.data.size())     # (60000, 28, 28)
print(train_data.targets.size())   # (60000)
plt.imshow(train_data.data[2].numpy(), cmap='gray')
plt.title('%i' % train_data.targets[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))    # f是一塊畫布；a是一個大小為2*5的數組，數組中的每個元素都是一個畫圖對象
plt.ion()   # Turn the interactive mode on, continuously plot

# original data (first row) for viewing
view_data = train_data.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(0.02)

plt.ioff()   # Turn the interactive mode off
plt.show()

# visualize in 3D plot
view_data = train_data.data[:200].view(-1, 28*28).type(torch.FloatTensor)/255.
encoded_data, _ = autoencoder(view_data)
fig = plt.figure(2)
ax = Axes3D(fig)    # 3D 圖
# x, y, z 的數據值
X = encoded_data.data[:, 0].numpy()
Y = encoded_data.data[:, 1].numpy()
Z = encoded_data.data[:, 2].numpy()
values = train_data.targets[:200].numpy()    # 標簽值
for x, y, z, s in zip(X, Y, Z, values):
    c = cm.rainbow(int(255*s/9))    # 上色
    ax.text(x, y, z, s, backgroundcolor=c)    # 標位子
ax.set_xlim(X.min(), X.max())
ax.set_ylim(Y.min(), Y.max())
ax.set_zlim(Z.min(), Z.max())
plt.show()


# test the decoder with a random code
code = torch.FloatTensor([[1.7, -2.5, 3.1]])   # 隨機給一個張量
decode = autoencoder.decoder(code)  # decode shape (1, 178)
decode = decode.view(decode.size()[0], 28, 28)
decode_img = decode.squeeze().data.numpy() * 255
plt.figure()
plt.imshow(decode_img.astype(np.uint8), cmap='gray')

 

參考資料：

[1] 深度學習之PyTorch，廖星宇

[2] 莫煩的PyTorch系列教程