What Shape Does nn.Linear(512, 10) Output?

How Linear Layers Work

A Linear layer performs output = input @ weight.T + bias. The weight matrix has shape (out_features, in_features) = (10, 512), and the bias has shape (10,). Only the last dimension of the input changes:

# Input shapes and corresponding outputs:
(32, 512)    -> (32, 10)    # batch of 32
(8, 16, 512)  -> (8, 16, 10)   # 3D input
(512,)     -> (10,)      # single sample

Parameter Count

Parameters = in_features * out_features + out_features
      = 512 * 10 + 10
      = 5,120 + 10
      = 5,130

PyTorch Code

import torch
import torch.nn as nn

fc = nn.Linear(512, 10)
print(sum(p.numel() for p in fc.parameters())) # 5130

x = torch.randn(32, 512)
output = fc(x)
print(output.shape) # torch.Size([32, 10])

Common Use Case

This is a typical final classification head. The 512-dimensional feature vector (from a CNN backbone or other encoder) is mapped to 10 class logits. For CIFAR-10, ImageNet subsets, or digit classification, this is the standard pattern: features in, class scores out.