zoo/vgg/vgg_c.py
def group(self, x, **metaparameters):
        """ Construct a Convolutional Group
            x        : input to the group
            n_layers : number of convolutional layers
            n_filters: number of filters
        """
        n_filters = metaparameters['n_filters']
        n_layers  = metaparameters['n_layers']
        del metaparameters['n_filters']

        # Block of convolutional layers
        for n in range(n_layers):
            x = self.Conv2D(x, n_filters, (3, 3), strides=(1, 1), padding="same",
                            activation=self.ReLU, **metaparameters)
        
        # Max pooling at the end of the block
        x = MaxPooling2D(2, strides=(2, 2))(x)
        return x
 Construct a Convolutional Group
            x        : input to the group
            n_layers : number of convolutional layers
            n_filters: number of filters
        
Construct a Convolutional Group
x        : input to the group
n_layers : number of convolutional layers
n_filters: number of filters
Construct a Convolutional Group x : input to the group n_layers : number of convolutional layers n_filters : number of filters
def group(self, x, **metaparameters):
        n_filters = metaparameters['n_filters']
        n_layers  = metaparameters['n_layers']
        del metaparameters['n_filters']
        for n in range(n_layers):
            x = self.Conv2D(x, n_filters, (3, 3), strides=(1, 1), padding="same",
                            activation=self.ReLU, **metaparameters)
        x = MaxPooling2D(2, strides=(2, 2))(x)
        return x
def group ( self , x , ** metaparameters ) : n_filters = metaparameters [ 'n_filters' ] n_layers = metaparameters [ 'n_layers' ] del metaparameters [ 'n_filters' ] for n in range ( n_layers ) : x = self . Conv2D ( x , n_filters , ( 3 , 3 ) , strides = ( 1 , 1 ) , padding = "same" , activation = self . ReLU , ** metaparameters ) x = MaxPooling2D ( 2 , strides = ( 2 , 2 ) ) ( x ) return x
Construct a Convolutional Group
x        : input to the group
n_layers : number of convolutional layers
n_filters: number of filters
Construct a Convolutional Group x : input to the group n_layers : number of convolutional layers n_filters : number of filters
""" Construct a Convolutional Group
            x        : input to the group
            n_layers : number of convolutional layers
            n_filters: number of filters
        """ # Block of convolutional layers # Max pooling at the end of the block
self
x