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1 question from Neural Network Basics

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Question 1: Short answer

mediumAI-checkedNeural Network Basics

A layer gg maps x∈R2x \in \mathbb{R}^2 to y∈R3y \in \mathbb{R}^3. At the current point its Jacobian (rows = outputs, Jji=∂yj/∂xiJ_{ji} = \partial y_j/\partial x_i) and the upstream gradient are

J=[120−131],∇yL=[12−1].J = \begin{bmatrix} 1 & 2 \\ 0 & -1 \\ 3 & 1 \end{bmatrix},\qquad \nabla_y L = \begin{bmatrix} 1 & 2 & -1 \end{bmatrix}.

The vector–Jacobian product gives ∂L/∂x1=\partial L/\partial x_1 = and ∂L/∂x2=\partial L/\partial x_2 = .

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