How to compute backpropagation gradient according chain rule for using vector/matrix differential?
I have some problems for computing derivative for sum of squares error in backprop neural network. For example, we have a neural network as in picture. For drawing simplicity, i've dropped the sample indexes.
Сonventions:
- x - data_set input.
- W - is a weigth matrix.
- v - vector of product: W*x.
- F - activation function vector.
- y - vector of activated data
- D - vector of answers
- e - error signal
- lower index is a variable(NxN) - dimenstionality
- higher [index] - is a layer number.
Jacobian is defined as:
\begin{pmatrix}\frac{\partial f_1(x_1)}{\partial x_1}\dots\frac{\partial f_1(x_N)}{\partial x_N}\\\vdots\ddots\vdots\\\frac{\partial f_M(x_1)}{\partial x_1}\dots\frac{\partial f_M(x_N)}{\partial x_N}\end{pmatrix}Let's have a look at the second layer and let's find derivative according chain rule:
Optimization error is sum of squared errors:
The chain rule for second layer is :
I'm going to derivate according rules founded here: https://fmin.xyz/docs/theory/Matrix_calculus/
- 1xN - dimension
So the differential is:
(You can verify equations according this service http://www.matrixcalculus.org/ or yourself using 2x2 dimension)
Let' look at dimenstions: (1xN) x (NxN) x (NxN) x (Nx2N) = (1x2N)
When we try to use gradient descent
- The dimenstions will not be the same: (NxN) = (NxN) - (1x2N)
Therefore, we cannot update the coefficients according this.
Where did i make a mistake? Any comments are welcomed. Thanks!
Topic derivation matrix backpropagation neural-network machine-learning
Category Data Science