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

Question

I have executed the following code and getting the error shown at extreme bottom. I would like to know how to resolve this. thanks

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)

Creating model for execution

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())

Executing this I am getting the following error :

RuntimeError Traceback (most recent call last) in () ----> 1 output = model(data) 2 3 ## 2. loss calculation 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 result = self._slow_forward(*input, **kwargs)

/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in linear(input, weight, bias) 1352 ret = torch.addmm(torch.jit._unwrap_optional(bias), input, weight.t()) 1353 else: -> 1354 output = input.matmul(weight.t()) 1355 if bias is not None: 1356 output += torch.jit._unwrap_optional(bias)

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

Answer 1

Your input MNIST data has shape [256, 1, 28, 28] corresponding to [B, C, H, W]. You need to flatten the input images into a single 784 long vector before feeding it to the Linear layer Linear(784, 128) such that the input becomes [256, 784] corresponding to [B, N], where N is 1x28x28, your image size. This can be done as follows:

for data, target in trainloader:

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

        optimizer.zero_grad()
        ...

The same is needed to be done in the validation loop.