Hi,
When running this piece of code using GPU on google colab, I expect to receive “RuntimeError: mat1 and mat2 shapes cannot be multiplied”, however, I do not. Obviously, the shape of those consecutive Linear layers mismatch, and I get that RuntimeError while using CPU. Could anyone explain why?
import os
import torch
from torch import nn

device = 'cuda' if torch.cuda.is_available() else 'cpu'

output:

Using cuda device

class NeuralNetwork(nn.Module):
    def __init__(self):
        super(NeuralNetwork, self).__init__()
        self.flatten = nn.Flatten()
        self.linear_relu_stack = nn.Sequential(
            nn.Linear(28*28, 513),
            nn.ReLU(),
            nn.Linear(512, 520),
            nn.ReLU(),
            nn.Linear(512, 10),
        )

    def forward(self, x):
        x = self.flatten(x)
        logits = self.linear_relu_stack(x)
        return logits

model = NeuralNetwork().to(device)
print(model)

output:

NeuralNetwork(
(flatten): Flatten(start_dim=1, end_dim=-1)
(linear_relu_stack): Sequential(
(0): Linear(in_features=784, out_features=513, bias=True)
(1): ReLU()
(2): Linear(in_features=512, out_features=520, bias=True)
(3): ReLU()
(4): Linear(in_features=512, out_features=10, bias=True)
)
)

X = torch.rand(1, 28, 28, device=device)
logits = model(X)
pred_probab = nn.Softmax(dim=1)(logits)
y_pred = pred_probab.argmax(1)
print(f"Predicted class: {y_pred}")

Output:

Predicted class: tensor([5], device=‘cuda:0’)