从pytorch层获取矩阵尺寸

Question

这是我从Pytorch教程中创建的一个自动编码器：

epochs = 1000
from pylab import plt
plt.style.use('seaborn')
import torch.utils.data as data_utils
import torch
import torchvision
import torch.nn as nn
from torch.autograd import Variable
cuda = torch.cuda.is_available()
FloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
import numpy as np
import pandas as pd
import datetime as dt

features = torch.tensor(np.array([ [1,2,3],[1,2,3],[100,200,500] ]))
print(features)
batch = 10
data_loader = torch.utils.data.DataLoader(features, batch_size=2, shuffle=False)
encoder = nn.Sequential(nn.Linear(3,batch), nn.Sigmoid())
decoder = nn.Sequential(nn.Linear(batch,3), nn.Sigmoid())
autoencoder = nn.Sequential(encoder, decoder)
optimizer = torch.optim.Adam(params=autoencoder.parameters(), lr=0.001)
encoded_images = []
for i in range(epochs):
for j, images in enumerate(data_loader):
#     images = images.view(images.size(0), -1) 
images = Variable(images).type(FloatTensor)
optimizer.zero_grad()
reconstructions = autoencoder(images)
loss = torch.dist(images, reconstructions)
loss.backward()
optimizer.step()
#     encoded_images.append(encoder(images))
# print(decoder(torch.tensor(np.array([1,2,3])).type(FloatTensor)))
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))

我可以看到编码的图像确实有新创建的维度10。为了理解引擎盖下正在进行的矩阵操作，我试图打印encoder和decoder的矩阵维度，但shape在nn.Sequential上不可用

如何打印nn.Sequential的矩阵维度？

Answer 1

nn.Sequential不是一个"层"，而是一个"容器"。它可以存储多个层并管理它们的执行(以及一些其他功能(
在您的情况下，每个nn.Sequential都包含线性层和非线性nn.Sigmoid激活。要获得nn.Sequential中第一层权重的形状，您可以简单地执行：

encoder[0].weight.shape