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I think your notations are unclear, but I can give an answer based on what you probably meant. For example, $\frac{\partial{L}}{\partial{W^x}}$ should be replaced by $(\nabla_{W^x_{j:}}L)_{j=1, ...,n}$ (assuming everything stays in $\mathbb{R}^n$). Also your expression for $\frac{\partial{L}}{\partial{W^x}}$ is wrong, even accounting for the notation. Since $...


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What may be more informative in terms of whether it is learning or not, is to track gradients. Through gradients you will be able to understand better whether activations are receiving error terms to adjust weights accordingly or not. In the latter case, this would be characteristic of vanishing gradients problem. You are developing with tf.keras, in which ...


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