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I have a Keras GAN where every layer in the generator has more neurons than the last and also where they all have an activation of LeakyReLU(alpha=0.1). I am trying to map the image back to the noise by solving linear equations in respect to the "output to be", which is the sum of all the weights*last input value then plus a bias going through the activation function. I reverse the LeakyReLU by multiplying all the negative output-to-be-s by 10 (1/alpha) to solve only for a set of linear equations. It gives wrong results though. Here is my code:

```
def inversemodel(model, predict):
current=predict[:]
layerlist=model.layers[:]
layerlist.reverse()
for layer in layerlist:
lastlayerlen=np.array(layer.get_weights()[0]).shape[0]
a=np.transpose(layer.get_weights()[0][:], (1, 0))[:lastlayerlen]
b=np.negative(np.array(layer.get_weights()[1]))
b=b[:lastlayerlen]
#minus biases from both sides
tobe=np.array(current[:lastlayerlen])
for index, i in enumerate(tobe):
if i<0:
tobe[index]=i*10
b=np.subtract(b, tobe)
current = solve(a, b).tolist()
return current
```

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