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The Most Common Errors in Deep Learning (Shape Errors)
Artificial Intelligence   Latest   Machine Learning

The Most Common Errors in Deep Learning (Shape Errors)

Author(s): Fatma Elik

Originally published on Towards AI.

Photo by Siora Photography on Unsplash

Shape errors occur when the shape of the input tensor does not match the expected shape for the tensor operation. These errors occur quite often when dealing with complex neural networks.

Here are some errors in Pytorch:

Incorrect Input ShapeBatch Size MismatchBroadcasting ErrorsMismatched Tensor Dimensions in General

In PyTorch, incorrect input shape errors occur when the shape of an input tensor in the neural network layer differs from the intended shape for that layer. This can happen for a variety of reasons, including utilizing the improper input shape for the network’s initial layer, delivering tensors of incompatible forms across layers, or mistakenly reshaping tensors.

Consider a basic neural network with an input layer that accepts input tensors of shapes like batch_size , num_features. If you attempt to supply an input tensor with a different shape, such as batch_size , height, width, or channels (for example, an image tensor), you will receive an improper input shape error.

import torchimport torch.nn as nn# Define a simple neural networkclass SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc1 = nn.Linear(3 , 32 , 32, 128) # Assume input shape of (3, 32, 32) def forward(self, x): # flatten tensor x = x.view(-1, 3 , 32 ,… Read the full blog for free on Medium.

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