enter image description here

Context: This code is based on a 3 layer fully connected neural network trained on had written numbers 0-9. This back query code will then take in an output value of 0.99,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01 and then its run backward through the network to get the pixel values at the beginning of the network to see what the defention of a 0 is to the network.

So my question is after the inverse sigmoid is applied amd that vector is multiplied by the vector of the transposed weight matrix how is that supposed to give me the activation values from the layer previous because if I do a dot product between two matrices $W*X=Z$ and then transpose $W.T*Z$ that does not give me X ? So then how could back query be useful ? It clearly is useful cause when I run the code it shows me the networks idea of a 0 but cant piece together how it works in my head.

Here is some additional context

enter image description here

  • $\begingroup$ It would be better if you provided the actual code rather than the screenshot, if people needed to refer to it. $\endgroup$
    – nbro
    Feb 25 at 13:52


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