Pytorch CNN：预期输入有 1 个通道，但得到的是 60000 个通道

Question

在为 Fashion MNIST 数据集实施 NN 时，出现以下错误：

RuntimeError: Given groups=1, weight of size [6, 1, 5, 5], expected input[1, 60000, 28, 28] to have 1 channels, but got 60000 channels instead

我推断 60000 是我整个数据集的长度，但不确定为什么算法会出现此错误。有人可以帮我解决这个问题吗？

我的数据集：

(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()
train_data = []
test_data = []
train_data.append([X_train, y_train])
test_data.append([X_test, y_test])

trainloader = torch.utils.data.DataLoader(train_data, shuffle=True, batch_size=100)
testloader = torch.utils.data.DataLoader(test_data, shuffle=True, batch_size=100)

我按以下顺序收到错误（根据堆栈跟踪）：

      8     #making predictions
----> 9     y_pred = model(images)

     32     #first hidden layer
---> 33     x = self.conv1(x)

更新 1

添加了行：

images = images.transpose(0, 1)

按照 Ivan 的指示转置图像，但现在出现错误：

RuntimeError: expected scalar type Byte but found Float

Answer 1

您输入的形状是 (1, 60000, 28, 28)，而它应该是 (60000, 1, 28, 28)。您可以通过调换前两个轴来解决此问题：

>>> x.transpose(0, 1)

Answer 2

看来你用的是TensorFlow的数据集。我用过 torchvison 的数据集，它工作正常

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision.datasets import FashionMNIST
from torchvision import transforms


class Network(nn.Module):
  def __init__(self):
    super(Network,self).__init__()
    self.conv1 = nn.Conv2d(in_channels=1,out_channels=6,kernel_size=5)
    self.conv2 = nn.Conv2d(in_channels=6,out_channels=12,kernel_size=5)
    self.fc1 = nn.Linear(in_features=12*4*4,out_features=120)
    self.fc2 = nn.Linear(in_features=120,out_features=60)
    self.fc3 = nn.Linear(in_features=60,out_features=40)
    self.out = nn.Linear(in_features=40,out_features=10)
  def forward(self,x):
    #input layer
    x = x
    #first hidden layer
    x = self.conv1(x)
    x = F.relu(x)
    x = F.max_pool2d(x,kernel_size=2,stride=2)
    #second hidden layer
    x = self.conv2(x)
    x = F.relu(x)
    x = F.max_pool2d(x,kernel_size=2,stride=2)
    #third hidden layer
    x = x.reshape(-1,12*4*4)
    x = self.fc1(x)
    x = F.relu(x)
    #fourth hidden layer
    x = self.fc2(x)
    x = F.relu(x)
    #fifth hidden layer
    x = self.fc3(x)
    x = F.relu(x)
    #output layer
    x = self.out(x)
    return x


batch_size = 1000
train_dataset = FashionMNIST(
    '../data', train=True, download=True, 
    transform=transforms.ToTensor())
trainloader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

test_dataset = FashionMNIST(
    '../data', train=False, download=True, 
    transform=transforms.ToTensor())
testloader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True)

model = Network()

losses = []
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters())
epochs = 10

for i in range(epochs):
    batch_loss = []
    for j, (data, targets) in enumerate(trainloader):
        optimizer.zero_grad()
        ypred = model(data)
        loss = criterion(ypred, targets.reshape(-1))
        loss.backward()
        optimizer.step()
        batch_loss.append(loss.item())
    if i>10: 
        optimizer.lr = 0.0005
    losses .append(sum(batch_loss) / len(batch_loss))
    print('Epoch {}:\tloss {:.4f}'.format(i, losses [-1]))

Pytorch CNN：预期输入有 1 个通道，但得到的是 60000 个通道

Pytorch CNN: Expected input to have 1 channel but got 60000 channels instead

python

neural-network

mnist

conv-neural-network

pytorch