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

Posted 2023-03-12

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

【英文标题】：size mismatch, m1: [3584 x 28], m2: [784 x 128] at /pytorch/aten/src/TH/generic/THTensorMath.cpp:940

【发布时间】：2019-06-10 15:08:08

【问题描述】：

我已经执行了以下代码并得到了显示在最底部的错误。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())

执行此操作时出现以下错误：

RuntimeError Traceback (最近一次调用最后一次) 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 呼叫（自我，*输入，**kwargs）487 结果=自我。_slow_forward（*输入， **kwargs)

/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py 在 线性（输入，权重，偏差）1352 ret = torch.addmm（torch.jit._unwrap_optional（偏差），输入，weight.t（））1353 别的： -> 1354 输出 = input.matmul(weight.t()) 1355 如果偏差不是无：1356 输出 += torch.jit._unwrap_optional(bias)

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

【参考方案1】：

您的输入 MNIST 数据的形状为 [256, 1, 28, 28]，对应于 [B, C, H, W]。您需要将输入图像展平为单个 784 长向量，然后将其馈送到线性层 Linear(784, 128)，以便输入变为与 [B, N] 对应的 [256, 784]，其中 N 为 1x28x28，即您的图像大小。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。