zoo/inception/inception_v1_c.py
def auxiliary(self, x, n_classes, **metaparameters):
        """ Construct the auxiliary classier
            x        : input to the auxiliary classifier
            n_classes: number of output classes
        """
        x = AveragePooling2D((5, 5), strides=(3, 3))(x)
        x = self.Conv2D(x, 128, (1, 1), strides=(1, 1), padding='same', **metaparameters)
        x = self.ReLU(x)
        x = Flatten()(x)
        x = self.Dense(x, 1024, activation=self.ReLU, **metaparameters)
        x = self.ReLU(x)
        x = Dropout(0.7)(x)
        output = self.Dense(x, n_classes, activation='softmax', **metaparameters)
        return output
 Construct the auxiliary classier
            x        : input to the auxiliary classifier
            n_classes: number of output classes
        
Construct the auxiliary classier
x        : input to the auxiliary classifier
n_classes: number of output classes
Construct the auxiliary classier x : input to the auxiliary classifier n_classes : number of output classes
def auxiliary(self, x, n_classes, **metaparameters):
        x = AveragePooling2D((5, 5), strides=(3, 3))(x)
        x = self.Conv2D(x, 128, (1, 1), strides=(1, 1), padding='same', **metaparameters)
        x = self.ReLU(x)
        x = Flatten()(x)
        x = self.Dense(x, 1024, activation=self.ReLU, **metaparameters)
        x = self.ReLU(x)
        x = Dropout(0.7)(x)
        output = self.Dense(x, n_classes, activation='softmax', **metaparameters)
        return output
def auxiliary ( self , x , n_classes , ** metaparameters ) : x = AveragePooling2D ( ( 5 , 5 ) , strides = ( 3 , 3 ) ) ( x ) x = self . Conv2D ( x , 128 , ( 1 , 1 ) , strides = ( 1 , 1 ) , padding = 'same' , ** metaparameters ) x = self . ReLU ( x ) x = Flatten ( ) ( x ) x = self . Dense ( x , 1024 , activation = self . ReLU , ** metaparameters ) x = self . ReLU ( x ) x = Dropout ( 0.7 ) ( x ) output = self . Dense ( x , n_classes , activation = 'softmax' , ** metaparameters ) return output
Construct the auxiliary classier
x        : input to the auxiliary classifier
n_classes: number of output classes
Construct the auxiliary classier x : input to the auxiliary classifier n_classes : number of output classes
""" Construct the auxiliary classier
            x        : input to the auxiliary classifier
            n_classes: number of output classes
        """
self
x
n_classes