Multi-label model support for image classification

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It would be great to have the option for multi-label problems in VisualML image classification.

In this use case the target is usually an array of one hot encoded classes and an image can belong to one or more classes. An image can be classified not as a single class (the class with the highest probability), but as multiple classes.

This includes the model top end to not be of type

Dense(n_classes, activation='softmax')

but of type

Dense(n_classes, activation='sigmoid')

Loss function should be 'binary_crossentropy'