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I was training a CNN network on German traffic sign classifier data. The architecture was-

  • 3 convolutional layers with intermediate max pooling
  • concatenated outputs of layer 2 and layer 3 to feed to the input layer of the fully connected classifier.
  • A final layer giving the softmax probabilities of the classes.
  • Used ReLu as the activation function.
  • Dropout for regularization only for the input layer of fully connected(FC) network with a keep_prob of 0.5

I did not scale the outputs of the FC input layer neither at the training time nor at the testing time. And while testing used a keep_prob of 1.0. I achieved an accuracy of 99 on validation set and 94 on the testing set.

Now, my question is- How did I achieve this much accuracy without scaling when using dropout?

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I am pretty much a noobie - but my understanding is that without scaling the input, model takes longer to learn. I scaled my inputs - image = 1/255. I did nothing for the outputs - I got numbers too good to be true for validation accuracy (1.0) and loss (0.0000004). Looking for an explanation I found that unless I scaled my output my validation numbers were bogus. As noobie I could not figure out how to scale the outputs, so I just killed the =1/255. My wonderful model was not normal junk:)

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