PyTorch's nn.Linear () detailed explanation

yTorch's nn.Linear() is used to set the fully connected layer in the network. It should be noted that the input and output of the fully connected layer are two-dimensional tensors, the general shape is [batch_size, size], which is different from convolution The layer requires input and output to be four-dimensional tensors. Its usage and formal parameters are explained as follows:

in_features refers to the size of the input two-dimensional tensor, that is, the size in the input [batch_size, size].
  out_features refers to the size of the output two-dimensional tensor, that is, the shape of the output two-dimensional tensor is [batch_size, output_size], of course, it also represents the number of neurons in the fully connected layer.
  From the perspective of the shape of the input and output tensors, it is equivalent to a tensor whose input is [batch_size, in_features] is transformed into an output tensor of [batch_size, out_features].
Example usage:

import torch as t
from torch import nn

# in_features由输入张量的形状决定，out_features则决定了输出张量的形状 
connected_layer = nn.Linear(in_features = 64*64*3, out_features = 1)

# 假定输入的图像形状为[64,64,3]
input = t.randn(1,64,64,3)

# 将四维张量转换为二维张量之后，才能作为全连接层的输入
input = input.view(1,64*64*3)
print(input.shape)
output = connected_layer(input) # 调用全连接层
print(output.shape)

The result of running this code is:

input shape is %s torch.Size([1, 12288])
output shape is %s torch.Size([1, 1])

Reprinted from: https://blog.csdn.net/qq_42079689/article/details/102873766