莫烦pytorch学习笔记5

程序员文章站 2022-07-06 10:41:09

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莫烦pytorch学习笔记5

1 自编码器
2代码实现

1 自编码器

自编码，又称自编码器（autoencoder），是神经网络的一种，经过训练后能尝试将输入复制到输出。自编码器（autoencoder）内部有一个隐藏层h，可以产生编码（code）表示输入。该网络可以看作由两部分组成：一个由函数h = f(x) 表示的编码器和一个生成重构的解码器r = g(h)。

首先，自编码器是一个神经网络。
如果我们得到的数据是正确标注的，不论是图像或音频或文本，我们就很幸运了。深度学习在有标注数据集上非常有效。这是因为总有一个函数代表了变量之间的关系。
比如如果我们的输入数据是一堆数字，还有定义了输入数据是偶数或者奇数的标签，那么代表这两列数字关系的函数就很简单：如果输入数据能被2整除，则这个数是偶数，不然就是奇数。
所有数据类型（视频或文本）均可用数字表示。因此总是有一个函数能映射关系。只不过比我们刚刚讨论过的函数更复杂一点。
莫烦pytorch学习笔记5

2代码实现

import torch
import torch.nn as nn
from torch.autograd import Variable
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
import time
# 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()

print(autoencoder)


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

# original data (first row) for viewing
view_data = Variable(train_data.train_data[:N_TEST_IMG].view(-1, 28*28).type(torch.FloatTensor)/255.)
print(train_data.train_data[:N_TEST_IMG])
print(type(train_data.train_data[:N_TEST_IMG]))
print(train_data.train_data[:N_TEST_IMG].size())
print("----------------------------")
print(train_data.train_data[:N_TEST_IMG].view(-1, 28*28))
print(type(train_data.train_data[:N_TEST_IMG].view(-1, 28*28)))
print(train_data.train_data[:N_TEST_IMG].view(-1, 28*28).size())
print("----------------------------")
print(train_data.train_data[:N_TEST_IMG].view(-1, 28*28).type(torch.FloatTensor)/255.)
print(type(train_data.train_data[:N_TEST_IMG].view(-1, 28*28).type(torch.FloatTensor)/255.))
print(train_data.train_data[:N_TEST_IMG].view(-1, 28*28).type(torch.FloatTensor)/255.)

for epoch in range(EPOCH):
    for step, (x, y) in enumerate(train_loader):
        b_x = Variable(x.view(-1, 28 * 28))  # batch x, shape (batch, 28*28)
        b_y = Variable(x.view(-1, 28 * 28))  # batch y, shape (batch, 28*28)
        b_label = Variable(y)  # batch label

        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 % 500 == 0 and epoch in [0, 5, EPOCH - 1]:
            print('Epoch: ', epoch, '| train loss: ', loss.data)

            # plotting decoded image (second row)
            _, decoded_data = autoencoder(view_data)

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

            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 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.show();

# visualize in 3D plot
view_data = Variable(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()