关于reshape(-1,…)与view(-1,…)这两个改变张量shape的函数

        它俩的细节参考链接都说的很明白我就不赘述了，我主要讲一下我那lstm处理mnist数据集的时候需要对数据集进行一个处理，方便把数据按模型input_size设定的那样喂给它。

import torch 
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms


# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Hyper-parameters
sequence_length = 28
input_size = 28
hidden_size = 128
num_layers = 2
num_classes = 10
batch_size = 100
num_epochs = 2
learning_rate = 0.01

# MNIST dataset
train_dataset = torchvision.datasets.MNIST(root='../../data/',
                                           train=True, 
                                           transform=transforms.ToTensor(),
                                           download=True)

test_dataset = torchvision.datasets.MNIST(root='../../data/',
                                          train=False, 
                                          transform=transforms.ToTensor())

# Data loader
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           batch_size=batch_size, 
                                           shuffle=True)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                          batch_size=batch_size, 
                                          shuffle=False)

total_step = len(train_loader)
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        print(images.size())
        images = images.reshape(-1,sequence_length,input_size).to(device)
        # images = images.view(-1,sequence_length,input_size).to(device)
        print(images.size())
        labels = labels.to(device)

打印出来的size正如你所见：
通过enumerate(trai_loader)出来的：torch.Size([100, 1, 28, 28])
通过reshape或者view改变shape后的：torch.Size([100, 28, 28])
100是batch_size,1是代表mnist灰色图像只有一个通道，28width，28height

        咱再举一反三打个比方，

        images = images.reshape(-1,2,sequence_length, input_size).to(device)
        # images = images.view(-1,2,sequence_length, input_size).to(device)
        print(images.size())

打印出来的size正如你所见：
通过enumerate(trai_loader)出来的：torch.Size([100, 1, 28, 28])
通过reshape或者view改变shape后的：torch.Size([50,2, 28, 28])
自动把batchsize变成了50，但总的数据量少没有变的，你，学废了吗？