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我已经执行了以下代码并在最底部显示了错误。我想知道如何解决这个问题。谢谢

import torch.nn as nn
import torch.nn.functional as F
from torch import optim

from torchvision import transforms
_tasks = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

from torchvision.datasets import MNIST
mnist = MNIST("data", download=True, train=True, transform=_tasks)

from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler

create training and validation split
split = int(0.8 * len(mnist))

index_list = list(range(len(mnist)))
train_idx, valid_idx = index_list[:split], index_list[split:]

create sampler objects using SubsetRandomSampler
tr_sampler = SubsetRandomSampler(train_idx)
val_sampler = SubsetRandomSampler(valid_idx)

create iterator objects for train and valid datasets
trainloader = DataLoader(mnist, batch_size=256, sampler=tr_sampler)
validloader = DataLoader(mnist, batch_size=256, sampler=val_sampler)
创建执行模型 
class Model(nn.Module):
  def init(self):
    super().init()
    self.hidden = nn.Linear(784, 128)
    self.output = nn.Linear(128, 10)

  def forward(self, x):
    x = self.hidden(x)
    x = F.sigmoid(x)
    x = self.output(x)
    return x

model = Model()

loss_function = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, weight_decay= 1e-6, momentum = 0.9, nesterov = True)

for epoch in range(1, 11): ## run the model for 10 epochs
  train_loss, valid_loss = [], []

  #training part
  model.train()
  for data, target in trainloader:
    optimizer.zero_grad()

    #1. forward propagation
    output = model(data)

    #2. loss calculation
    loss = loss_function(output, target)

    #3. backward propagation
    loss.backward()

    #4. weight optimization
    optimizer.step()

    train_loss.append(loss.item())

  # evaluation part
  model.eval()
  for data, target in validloader:
     output = model(data)
     loss = loss_function(output, target)
     valid_loss.append(loss.item())

执行此我收到以下错误：

RuntimeError Traceback (most recent call last) in () ----> 1 output = model(data) 2 3 ## 2. 损失计算 4 loss = loss_function(output, target) 5

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in call(self, *input, **kwargs) 487 结果 = self._slow_forward(*input, **kwargs)

/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py 线性（输入，重量，偏差）1352 ret = torch.addmm（torch.jit._unwrap_optional（偏差），输入，重量.t()) 1353 else: -> 1354 output = input.matmul(weight.t()) 1355 如果偏差不是 None: 1356 output += torch.jit._unwrap_optional(bias)

RuntimeError：大小不匹配，m1：[3584 x 28]，m2：[784 x 128] 在 /pytorch/aten/src/TH/generic/THTensorMath.cpp:940

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1 回答 1

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您的输入 MNIST 数据的形状[256, 1, 28, 28]对应于[B, C, H, W]。您需要将输入图像展平为一个 784 长的向量，然后再将其馈送到线性层Linear(784, 128)，以便输入[256, 784]对应于[B, N]您的图像大小，其中 N 为 1x28x28。这可以按如下方式完成：

for data, target in trainloader:

        # Flatten MNIST images into a 784 long vector
        data = data.view(data.shape[0], -1)

        optimizer.zero_grad()
        ...

在验证循环中也需要这样做。

于 2019-01-16T16:02:21.237 回答