So, we have to configure the model for training. To do this, we will use the compile method, as follows:
AutoencoderModel.compile(optimizer='adadelta', loss='binary_crossentropy')
This method configures the model for training. Only two arguments are used:
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optimizer: String (name of optimizer) or optimizer instance.
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loss: String (name of objective function) or objective function. If the model has multiple outputs, you can use a different loss on each output by passing a dictionary or a list of losses. The loss value that will be minimized by the model will then be the sum of all individual losses.
We have used adadelta optimizer. This method dynamically adapts over time, using only first-order information, and ...
