zoo/squeezenet/squeezenet_complex_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
        
        # set the number of filters equal to number of classes
        x = self.Conv2D(x, n_classes, (1, 1), strides=1,  padding='same')
        x = self.ReLU(x)
        # reduce each filter (class) to a single value
        x = GlobalAveragePooling2D()(x)

        # Save the pre-activation probabilities
        self.probabilities = x
        outputs = Activation('softmax')(x)
        # Save the post-activations probabilities
        self.softmax = outputs
        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 = self.Conv2D(x, n_classes, (1, 1), strides=1,  padding='same')
        x = self.ReLU(x)
        x = GlobalAveragePooling2D()(x)
        self.probabilities = x
        outputs = Activation('softmax')(x)
        self.softmax = outputs
        return outputs
def classifier ( self , x , n_classes ) : self . encoding = x x = self . Conv2D ( x , n_classes , ( 1 , 1 ) , strides = 1 , padding = 'same' ) x = self . ReLU ( x ) x = GlobalAveragePooling2D ( ) ( x ) self . probabilities = x outputs = Activation ( 'softmax' ) ( x ) self . softmax = outputs 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 # set the number of filters equal to number of classes # reduce each filter (class) to a single value # Save the pre-activation probabilities # Save the post-activations probabilities
self
x
n_classes