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input_shape=(32, 32, 3))) will become input_shape=(32, 32, 1))) Channel is the last argument by default "...When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers, does not include the sample axis), e.g. input_shape=(128, 128, 3) for 128x128 RGB pictures in data_format="channels_last...&...


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One choice is to train a neural network model to take these values and output original images. Notice that usually some data is loss in this process so it might be impossible to reconstruct the image with perfection. You could try inverting the functional form but: CNNs usually use ReLu activation which is not bijective. Pooling layers throws information ...


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Try training with more epochs. I've seen this same thing where the output after M epochs is a grayscale image but continued training results in more and more color.


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Hope you are applying the preprocessing steps on the dataset that you are using for predict. I remember getting this kind of prediction log time back and that time I think it was something to do with either not applying the same preprocessing pipeline or incorrectly doing the label map Since you are using image generator label mapping should be easy through ...


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If you have images of cats only, you could create boundary boxes (BB) of your images. Some BB will have cats an others won't. You will label those BB with cats inside as 1 as the others as 0. This way you can set up a dataset with a binary class. It will be much easier if you already have boundary boxes for the cats in each image since this way it will be ...


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In absolute, it is one CNN wich takes 3 inputs images. You could see it as 3 separate features extractors (CNN) which merge their results while trained together. The author obtain 3 2D input from a 3D images by keeping 3 2D images; one in each plane. Each of these images has multiple channel because they slices the input among the respective axis. It is ...


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I do not think there is a special kind of format that needs to be followed as long as the image is clear and readable, which (imho) it is for your case. Regarding the last 2/3 layers, the final layer is the output with 1 unit, so you pictured it correctly, along as the article mentions the output shape (that is not a multi-output situation). Good luck with ...


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The problem is that your ResNet-18 follows the architecture for ImageNet as outlined in the ResNet paper: However, spatial input dimensions of ImageNet are different from CIFAR10 (32x32) so the architecture does not match your input. Instead you can follow the author's description of their CIFAR10 architecture in section 4.2 of the same paper: The plain/...


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