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What shapes does nn.Linear accept?
PyTorch defines a linear layer as y = xA^T + b. Its input shape is (*, H_in), where H_in must equal in_features. Its output shape is (*, H_out), where H_out is out_features. The asterisk represents any number of leading dimensions: the layer does not require a two-dimensional input.
For example, with a batch of 128 vectors, each containing 20 features:
layer = torch.nn.Linear(in_features=20, out_features=30)
x = torch.randn(128, 20)
y = layer(x)
# x: (128, 20)
# layer.weight: (30, 20)
# y: (128, 30)The same last-axis rule applies when there are more leading dimensions. An input shaped (batch, sequence, features) is processed at each batch and sequence position independently along its final, feature axis; those leading dimensions remain in the output.
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How are in_features and the weight shape related?
The learnable weight has shape (out_features, in_features). In the documented operation, PyTorch multiplies the input by the transpose of that weight, then adds the bias if enabled. The bias shape is (out_features,). As a result, the input’s final dimension must match the weight’s second dimension, not its first.
For a layer written as nn.Linear(20, 30), set in_features to 20 because each input vector must end in 20 features. The 30 is the number of output features; it does not describe the input size or batch size. The batch dimension is a leading dimension and is preserved.
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How to diagnose “mat1 and mat2 shapes cannot be multiplied”
- Find the failing call. Read the traceback to identify the exact
nn.Linearinvocation that raises the error. If the network has multiple linear layers, the error alone does not tell you which one is mismatched. - Check the input right before that layer. Inspect its shape at the failing call, then compare the last dimension with that layer’s
in_features. The required condition isinput.shape[-1] == layer.in_features. - Decide whether the layer or the tensor layout is wrong. If the tensor already contains the intended features in the correct arrangement, configure the layer’s
in_featuresto match their count. If the features are on a different axis than intended, fix the upstream transformation or axis order instead. - Preserve the meaning of each axis. Before flattening, transposing, or permuting, identify which dimensions represent examples, sequence positions, channels, spatial positions, or features. A transformation should make the final dimension the intended feature vector without mixing examples or otherwise changing the model’s intended structure.
For a two-dimensional input, transposing may make the dimensions line up numerically, but that does not make it the right fix: it can put examples where features are expected. Likewise, changing in_features to silence the mismatch is not appropriate if the input’s axes are arranged incorrectly. Community troubleshooting examples illustrate both kinds of mistakes; use the actual failing layer and input shape to choose the correction.
What should you do after a CNN or image layer?
A convolutional or pooling pipeline can produce a per-example activation with multiple feature and spatial dimensions. Before feeding it to a fully connected layer, determine the size of the intended per-example feature representation after those operations, then flatten those dimensions while preserving the batch dimension. Configure the linear layer’s in_features to that resulting feature count.
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The key check remains the same regardless of how the feature count was obtained: the tensor passed into the linear layer must end in exactly the configured in_features. Do not copy a dimension from another model or forum example; layer sizes depend on the actual pipeline and input layout.
Is a dtype mismatch the same problem?
No. The multiply error discussed here is a shape mismatch: the matrix dimensions do not align for the operation. An incompatible dtype between the input and parameters is a separate issue. Changing in_features does not resolve a dtype mismatch, so distinguish the error message and traceback before changing the model.
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Quick checks before changing the model
- Identify the exact linear-layer call shown by the traceback.
- Read the shape of the tensor immediately before that call.
- Compare its final dimension—not its batch size—with the layer’s
in_features. - Verify that batch, sequence, channel, spatial, and feature axes still have the intended meanings after any reshape or permutation.
- For image pipelines, calculate the flattened per-example feature count after convolution and pooling, and keep the batch dimension separate.
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