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Authors use many different approaches. One approach is to have a different input neuron for each possible category, and then use a "1-hot" encoding. So if you have 10 categories, then you can encode this as 10 binary features. Another is to use some sort of binary encoding. If you have 10 categories, it is sufficient to use 4 neurons to represent all ...


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Yes it should be possible. You may have a bug in your code, or the wrong hyperparameters. Training ResNet-50 will take a long time. Try training on other sets of images and see what accuracy you get to check if your approach is correct. Or, try loading a pretrained model, and training from that.


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A one-hot encoding, as described in John's answer, is probably the most straightforward / simple solution (maybe even the most common?). It is not without its problems though. For example, if you have a large number of such categorical variables, and each has a large number of possible values, the number of binary inputs you need for one-hot encodings may ...


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In short: yes, you must allow "do nothing" decision as a first level result. Your system must decide the action to be taken, including "do nothing" action. This is different to low network outputs, that can be translated as "don't know what to do". In other words, the network can result in: "I don't know what to do now&...


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I know this is not a straight answer to your question, but I couldn't comment on your post so decided to post it (so maybe I will delete it after you received a better answer). I think this playlist by sentdex can be handy as he goes through a lot of details to teach a neural network model that can drive cars in GTA-V by simply looking at each frame of the ...


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What you have is called a classification problem with categorical features. That is, the features can be represented numerically, but the numbers have no relative meaning. Algorithms that rely on smooth function approximation will probably not work well here. These would include classic approaches to regression, and also function approximation via a neural ...


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Transfer Learning allows you to add new categories to be predicted to the output layer without needing to re-train the entire model every time a new category needs to be classified. Rather, the weights of all initial layers up to the last few layers of the network can be frozen and only the last one or two layers can be made trainable for fine-tuning the ...


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When you use sigmoid activation it is applied independently to all outputs so outputs won't sum up to 1. Sigmoid is usually used for binary logistic regression where you only have 2 classes. In your case you should use softmax activation at the output which will squash outputs to range [0,1] and additionally make them sum up to 1.


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@SmallChess's answer is a good start, but there are some additional parts to the question. binary variables or binary data consist of data with the values 0 or 1, and no other values. We usually don't talk about "binary distributions", because it's only data, variables, or outcomes that can be binary. A distribution might produce binary data, but is not ...


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