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

        # Flatten the input image
        x = Flatten()(x)

        # Progressive Unit Pooling
        for layer in layers:
            n_nodes = layer['n_nodes']
            x = self.Dense(x, n_nodes)
            x = self.BatchNormalization(x)
            x = self.ReLU(x)

        # The Encoding
        return x
 Construct the Encoder 
            x     : input to the encoder
            layers: number of nodes per layer
        
Construct the Encoder
x     : input to the encoder
layers: number of nodes per layer
Construct the Encoder x : input to the encoder layers : number of nodes per layer
def encoder(self, x, **metaparameters):
        layers = metaparameters['layers']
        x = Flatten()(x)
        for layer in layers:
            n_nodes = layer['n_nodes']
            x = self.Dense(x, n_nodes)
            x = self.BatchNormalization(x)
            x = self.ReLU(x)
        return x
def encoder ( self , x , ** metaparameters ) : layers = metaparameters [ 'layers' ] x = Flatten ( ) ( x ) for layer in layers : n_nodes = layer [ 'n_nodes' ] x = self . Dense ( x , n_nodes ) x = self . BatchNormalization ( x ) x = self . ReLU ( x ) return x
Construct the Encoder
x     : input to the encoder
layers: number of nodes per layer
Construct the Encoder x : input to the encoder layers : number of nodes per layer
''' Construct the Encoder 
            x     : input to the encoder
            layers: number of nodes per layer
        ''' # Flatten the input image # Progressive Unit Pooling # The Encoding
self
x