Question

使用下面的代码，我尝试将mnist中的图像编码为低维表示形式：

import warnings
warnings.filterwarnings('ignore')
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib import pyplot as plt
from sklearn import metrics
import datetime
from sklearn.preprocessing import MultiLabelBinarizer
import seaborn as sns
sns.set_style("darkgrid")
from ast import literal_eval
import numpy as np
from sklearn.preprocessing import scale
import seaborn as sns
sns.set_style("darkgrid")
import torch
import torch
import torchvision
import torch.nn as nn
from torch.autograd import Variable

%matplotlib inline

low_dim_rep = 32
epochs = 2

cuda = torch.cuda.is_available() # True if cuda is available, False otherwise
FloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
print('Training on %s' % ('GPU' if cuda else 'CPU'))

# Loading the MNIST data set
transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor(),
                torchvision.transforms.Normalize((0.1307,), (0.3081,))])
mnist = torchvision.datasets.MNIST(root='../data/', train=True, transform=transform, download=True)

# Loader to feed the data batch by batch during training.
batch = 100
data_loader = torch.utils.data.DataLoader(mnist, batch_size=batch, shuffle=True)


encoder = nn.Sequential(
                # Encoder
                nn.Linear(28 * 28, 64),
                nn.PReLU(64),
                nn.BatchNorm1d(64),

                # Low-dimensional representation
                nn.Linear(64, low_dim_rep),
                nn.PReLU(low_dim_rep),
                nn.BatchNorm1d(low_dim_rep))

decoder = nn.Sequential(
                # Decoder
                nn.Linear(low_dim_rep, 64),
                nn.PReLU(64),
                nn.BatchNorm1d(64),
                nn.Linear(64, 28 * 28))

autoencoder = nn.Sequential(encoder, decoder)

encoder = encoder.type(FloatTensor)
decoder = decoder.type(FloatTensor)
autoencoder = autoencoder.type(FloatTensor)

optimizer = torch.optim.Adam(params=autoencoder.parameters(), lr=0.00001)


data_size = int(mnist.train_labels.size()[0])

print('data_size' , data_size)
for i in range(epochs):
    for j, (images, _) in enumerate(data_loader):
        images = images.view(images.size(0), -1) # from (batch 1, 28, 28) to (batch, 28, 28)
        images = Variable(images).type(FloatTensor)

        autoencoder.zero_grad()
        reconstructions = autoencoder(images)
        loss = torch.dist(images, reconstructions)
        loss.backward()
        optimizer.step()
    print('Epoch %i/%i loss %.2f' % (i + 1, epochs, loss.data[0]))

print('Optimization finished.')

# Get the encoded images here
encoded_images = []
for j, (images, _) in enumerate(data_loader):
    images = images.view(images.size(0), -1) 
    images = Variable(images).type(FloatTensor)

    encoded_images.append(encoder(images))

此代码完成后

当我希望长度与mnist中的图像数量相匹配时，

len(encoded_images)为600：len(mnist)-60'000。

如何将图像编码为32（low_dim_rep = 32的低维表示？我没有正确定义网络参数？

Answer 1

您在60000中有mnist张图片，而您的batch = 100中有。这就是您len(encoded_images)=600的原因，因为在生成编码图像时会进行60000/100=600迭代。您最终得到600个元素的列表，其中每个元素的形状均为[100, 32]。您可以执行以下操作

encoded_images = torch.zeros(len(mnist), 32)
for j, (images, _) in enumerate(data_loader):
    images = images.view(images.size(0), -1) 
    images = Variable(images).type(FloatTensor)
    encoded_images[j * batch : (j+1) * batch] = encoder(images)

自动编码器尺寸的结果不正确

1 个答案: