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？