RuntimeError: size mismatch m1: [a x b], m2: [c x d]
Solution 1
All you have to care is b=c
and you are done:
m1: [a x b], m2: [c x d]
m1
is [a x b]
which is [batch size x in features]
m2
is [c x d]
which is [in features x out features]
Solution 2
You have a size mismatch!
Your first layer of model
expects a 784-dim input (I assume you got this value by 28x28=784, the size of mnist digits).
However, your trainset
applies transforms.CenterCrop(10)
- that is it crops a 10x10 region from the center of the image, and thus your input dimension is actually 100.
To summaries:
- Your first layer: nn.Linear(784, 128)
expects a 784-dim input and outouts a 128-dim hidden feature vector (per input). This layer's weight matrix is thus [784 x 128]
("m2
" in your error message).
- Your input is center cropped to 10x10 pixels (total 100-dim), and you have batch_size=64
such images at each batch, total [64 x 100]
input size ("m1
" in your error message).
- You cannot compute a dot-product between matrices with mismatch sizes: 100 != 784, therefore pytorch gives you an error.
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Urvi Soni
Updated on June 04, 2022Comments
-
Urvi Soni almost 2 years
Can anyone help me in this.? I am getting below error. I use Google Colab. How to Solve this error.?
size mismatch, m1: [64 x 100], m2: [784 x 128] at /pytorch/aten/src/TH/generic/THTensorMath.cpp:2070
Below Code I am trying to Run.
import torch from torch import nn import torch.nn.functional as F from torchvision import datasets, transforms # Define a transform to normalize the data transform = transforms.Compose([transforms.CenterCrop(10),transforms.ToTensor(),]) # Download the load the training data trainset = datasets.MNIST('~/.pytorch/MNIST_data/', download=True, train=True, transform=transform) trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True) # Build a feed-forward network model = nn.Sequential(nn.Linear(784, 128),nn.ReLU(),nn.Linear(128, 64),nn.ReLU(),nn.Linear(64, 10)) # Define the loss criterion = nn.CrossEntropyLoss() # Get our data images, labels = next(iter(trainloader)) # Faltten images images = images.view(images.shape[0], -1) # Forward pass, get our logits logits = model(images) # Calculate the loss with the logits and the labels loss = criterion(logits, labels) print(loss)
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Khawar Islam over 3 yearshow can i solve this error RuntimeError: size mismatch, m1: [4 x 2048], m2: [1568 x 10]?