Question

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())

执行此操作，我收到以下错误：

  （p）中的
RuntimeError Traceback（最近一次通话最近）   ----> 1输出=模型（数据）2 3 ## 2.损失计算4损失= loss_function（输出，目标）5

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

/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py在   线性（输入，重量，偏置）1352 ret =   torch.addmm（torch.jit._unwrap_optional（bias），input，weight.t（））1353   其他：   -> 1354输出= input.matmul（weight.t（））1355如果没有偏置，则无：1356输出+ = torch.jit._unwrap_optional（bias）

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

Answer 1

您输入的MNIST数据具有与URL相对应的形状[256, 1, 28, 28]。您需要先将输入图像展平为一个784长的矢量，然后再将其馈送到线性层[B, C, H, W]，以使输入变为与Linear(784, 128)相对应的[256, 784]，其中N为1x28x28，即图像尺寸。可以按照以下步骤进行操作：

[B, N]

在验证循环中需要执行相同的操作。

尺寸不匹配，m1：[3584 x 28]，m2：[784 x 128]，位于/pytorch/aten/src/TH/generic/THTensorMath.cpp:940

1 个答案: