了解线性层中的 Pytorch 权重和偏差

Question

下面是将权重和偏差合并到一个层中的代码，我无法理解下面的行，为什么我们必须将权重转置矩阵与 bais 相乘。我应该在没有权重的情况下进行偏置，因为我们正在乘以权重以获得最终输出 3

combined_layer.bias.data = layer1.bias @ layer2.weight.t() + layer2.bias

 # Create a single layer to replace the two linear layers
 combined_layer = nn.Linear(input_size, output_size)

 combined_layer.weight.data = layer2.weight @ layer1.weight
 combined_layer.bias.data = layer1.bias @ layer2.weight.t() + layer2.bias //This should be just bias

 outputs3 = inputs @ combined_layer.weight.t() + combined_layer.bias

谁能帮我理解一下？

Answer 1

您只需展开两个 Linear 层的原始方程，即

# out = layer2(layer1(x))
# given (x @ A + B) @ C + D
out = (x @ layer1.weight.t() + layer1.bias) @ layer2.weight.t() + layer2.bias

你可以展开(x @ A + B) @ C + D = (x @ A @ C) + B @ C + D

out = x @ layer1.weight.t() @ layer2.weight.t() + layer1.bias @ layer2.weight.t() + layer2.bias
out = x @ (layer1.weight.t() @ layer2.weight.t()) + (layer1.bias @ layer2.weight.t() + layer2.bias)
# the above equation is x @ (A @ C) + B @ C + D

# now you can assume
combined_layer.weight = layer2.weight @ layer1.weight
combined_layer.bias = layer1.bias @ layer2.weight.t() + layer2.bias 
# final output
out = x @ combined_layer.weight.t() + combined_layer.bias

另请注意，这里也使用了矩阵乘法转置规则，即

transpose(A@B) = transpose(B) @ transpose(A)

这就是 combined_layer.weight.t() 乘以 x 的原因，因为我们没有在 layer2.weight @ layer1.weight 中进行转置。

了解线性层中的 Pytorch 权重和偏差

Understanding Pytorch Weight and Biases in Linear Layer

python

neural-network

gradient-descent

deep-learning

pytorch