No dropout in last hidden layer?

[j6e]

I've been working with your code lately and I've notice that the last layer of the keras_mlp.py in both models does never apply dropout:

model = Sequential()
model.add( Dense( params['layer_1_size'], init = params['init'], 
activation = params['layer_1_activation'], input_dim = input_dim ))

for i in range( int( params['n_layers'] ) - 1 ):

    extras = 'layer_{}_extras'.format( i + 1 )

    if params[extras]['name'] == 'dropout':
        model.add( Dropout( params[extras]['rate'] ))
    elif params[extras]['name'] == 'batchnorm':
        model.add( BatchNorm())

    model.add( Dense( params['layer_{}_size'.format( i + 2 )], init = params['init'], 
        activation = params['layer_{}_activation'.format( i + 2 )]))

model.add( Dense( 1, init = params['init'], activation = 'linear' ))

As can be seen in the code, the last hidden layer can't have dropout since the dropout is coded before the layer itself. Is this intentional or it's undesired behaviour?

[zygmuntz]

It is probably a bug, yeah. Keras docs show dropout applied before the last layer: https://keras.io/getting-started/sequential-model-guide/