zoo/vgg/vgg_c.py
def classifier(self, x, n_classes):
        """ Construct the Classifier
            x         : input to the classifier
            n_classes : number of output classes
        """
        # Save the encoding layer
        self.encoding = x

        # Flatten the feature maps
        x = Flatten()(x)

        # Save the embedding layer
        self.embedding = x
    
        # Two fully connected dense layers
        x = self.Dense(x, 4096, activation=self.ReLU)
        x = self.Dense(x, 4096, activation=self.ReLU)

        outputs = super().classifier(x, n_classes, pooling=None)
        return outputs
 Construct the Classifier
            x         : input to the classifier
            n_classes : number of output classes
        
Construct the Classifier
x         : input to the classifier
n_classes : number of output classes
Construct the Classifier x : input to the classifier n_classes : number of output classes
def classifier(self, x, n_classes):
        self.encoding = x
        x = Flatten()(x)
        self.embedding = x
        x = self.Dense(x, 4096, activation=self.ReLU)
        x = self.Dense(x, 4096, activation=self.ReLU)
        outputs = super().classifier(x, n_classes, pooling=None)
        return outputs
def classifier ( self , x , n_classes ) : self . encoding = x x = Flatten ( ) ( x ) self . embedding = x x = self . Dense ( x , 4096 , activation = self . ReLU ) x = self . Dense ( x , 4096 , activation = self . ReLU ) outputs = super ( ) . classifier ( x , n_classes , pooling = None ) return outputs
Construct the Classifier
x         : input to the classifier
n_classes : number of output classes
Construct the Classifier x : input to the classifier n_classes : number of output classes
""" Construct the Classifier
            x         : input to the classifier
            n_classes : number of output classes
        """ # Save the encoding layer # Flatten the feature maps # Save the embedding layer # Two fully connected dense layers
self
x
n_classes