zoo/unet/unet_c.py
def classifier(self, x, n_classes, **metaparameters):
        """ Construct the classifier
            x        : input to the classifier
            n_classes: number of classes
            dropout  : percentage of dropout
        """
        dropout = metaparameters['dropout']

        if dropout > 0.0:
            x = Dropout(dropout)(x)

        x = self.Conv2D(x, n_classes, (1, 1), strides=1, padding='valid', activation='sigmoid') 
        return x
 Construct the classifier
            x        : input to the classifier
            n_classes: number of classes
            dropout  : percentage of dropout
        
Construct the classifier
x        : input to the classifier
n_classes: number of classes
dropout  : percentage of dropout
Construct the classifier x : input to the classifier n_classes : number of classes dropout : percentage of dropout
def classifier(self, x, n_classes, **metaparameters):
        dropout = metaparameters['dropout']
        if dropout > 0.0:
            x = Dropout(dropout)(x)
        x = self.Conv2D(x, n_classes, (1, 1), strides=1, padding='valid', activation='sigmoid') 
        return x
def classifier ( self , x , n_classes , ** metaparameters ) : dropout = metaparameters [ 'dropout' ] if dropout > 0.0 : x = Dropout ( dropout ) ( x ) x = self . Conv2D ( x , n_classes , ( 1 , 1 ) , strides = 1 , padding = 'valid' , activation = 'sigmoid' ) return x
Construct the classifier
x        : input to the classifier
n_classes: number of classes
dropout  : percentage of dropout
Construct the classifier x : input to the classifier n_classes : number of classes dropout : percentage of dropout
""" Construct the classifier
            x        : input to the classifier
            n_classes: number of classes
            dropout  : percentage of dropout
        """
self
x
n_classes