从 pytorch 层获取矩阵维度

Question

这是我根据 Pytorch 教程创建的自动编码器：

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))

我可以看到编码图像确实有新创建的 10 维。为了理解幕后进行的矩阵运算，我尝试打印 encoder 和 [=12 的矩阵维数=] 但 shape 在 nn.Sequential

上不可用

如何打印 nn.Sequential 的矩阵维度？

Answer 1

一个nn.Sequential is not a "layer", but rather a "container"。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，您可以简单地执行以下操作：

encoder[0].weight.shape

从 pytorch 层获取矩阵维度

Get matrix dimensions from pytorch layers

autoencoder

pytorch