zoo/autoencoder/dc_autoencoder_c.py
def encoder(self, x, **metaparameters):
        ''' Construct the Encoder 
            x     : input to the encoder
            layers: number of filters per layer
        '''
        layers = metaparameters['layers']

        # Progressive Feature Pooling
        for layer in layers:
            n_filters = layer['n_filters']
            x = self.Conv2D(x, n_filters, (3, 3), strides=2, padding='same')
            x = self.BatchNormalization(x)
            x = self.ReLU(x)

        # The Encoding
        return x
 Construct the Encoder 
            x     : input to the encoder
            layers: number of filters per layer
        
Construct the Encoder
x     : input to the encoder
layers: number of filters per layer
Construct the Encoder x : input to the encoder layers : number of filters per layer
def encoder(self, x, **metaparameters):
        layers = metaparameters['layers']
        for layer in layers:
            n_filters = layer['n_filters']
            x = self.Conv2D(x, n_filters, (3, 3), strides=2, padding='same')
            x = self.BatchNormalization(x)
            x = self.ReLU(x)
        return x
def encoder ( self , x , ** metaparameters ) : layers = metaparameters [ 'layers' ] for layer in layers : n_filters = layer [ 'n_filters' ] x = self . Conv2D ( x , n_filters , ( 3 , 3 ) , strides = 2 , padding = 'same' ) x = self . BatchNormalization ( x ) x = self . ReLU ( x ) return x
Construct the Encoder
x     : input to the encoder
layers: number of filters per layer
Construct the Encoder x : input to the encoder layers : number of filters per layer
''' Construct the Encoder 
            x     : input to the encoder
            layers: number of filters per layer
        ''' # Progressive Feature Pooling # The Encoding
self
x