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Compilation of the module Compilation configure the learning process of the module. It is done via compile method. It takes 3 arguments. Optimizer Can use string identifier of an existing optimizer (rmsprop | adagrad) or instance of an existing Optimizer. Loss Function Model always tries to minimize the loss. Can use existing string identifier of an loss function ( mse | categorical_crossentropy)or an objective function. A list of metrics For any classification problem set metrics to accuracy. metrics=['accuracy'] metric could be a string identifier , existing metric or custom metric function. # For a binary classification problem model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy']) # For a mean squared error regression problem model.compile(optimizer='rmsp...

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