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Q1: I do not really understand how the situation in your Q1 is possible - I would expect an error to be thrown about as a mismatch in shape. For example, when I change the number of classes in the final dense layer, I do indeed get an error. model = Sequential() model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', ...


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Yes, testing data should follow the same preprocessing as the training data. Otherwise, testing data will have nothing comparable with what the algorithm learned, leading to (very) bad performances. note: In Sklearn, the Pipeline class helps you to respect the fundamentals of ML modeling like data leakage and applying the same transformations to train and ...


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When we say the filter size if 5x5 it is for an image with 1 input channel, for three the filter size is 5x5x3 (but at a lot of places this additional info is skipped to make things easy to understand). When you apply a kernel of 5x5x3 to an image the output is just one channel. To get an output of 8 channels you need 8 such kernels. In that case, the number ...


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