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You can get the output of your models with model.output or get_layer and combine them with tf.keras.layers.concatenate


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i wrote up some simple examples to illustrate how convolution and transpose convolution is done, and as implemented by software libraries like PyTorch https://makeyourownneuralnetwork.blogspot.com/2020/02/calculating-output-size-of-convolutions.html an example of the visual explanations:


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In many cases in deep learning it works well to start off with a model which has a very high capacity and potentially overfits. From thereon you can reduce the model capacity to narrow the gap between train and validation error. In this chapter of the Deep Learning Book by Goodwell you find a good description of manual hyperparameter selection and how they ...


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Using a CNN in an autoencoder (mentioned by S van Balen), is the most established way of doing anomaly detection. You can possibly use a pre-trained network as a base for this. And it should be possible to train only the decoder, keeping the encoder frozen. There are some works that show using regularization constraints to make the decoder layers the inverse ...


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Receptive field refers to the number of input pixels that a convolutional filter will operate on. There's a nice distill article about how to calculate receptive field size for your filters (with a nice visualization of receptive field size) and an interactive calculator here if you're only curious about how receptive field size grows with changes to depth ...


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