zoo/xception/xception_c.py
def exitFlow(self, x, n_classes, include_top, **metaparameters):
        """ Create the exit flow section
            x          : input to the exit flow section
            n_classes  : number of output classes
            include_top: whether to include classifier
        """     
        # Remember the input
        shortcut = x

        # Strided convolution to double number of filters in identity link to
        # match output of residual block for the add operation (projection shortcut)
        shortcut = self.Conv2D(x, 1024, (1, 1), strides=(2, 2), padding='same', **metaparameters)
        shortcut = self.BatchNormalization(shortcut)

        # First Depthwise Separable Convolution
        # Dimensionality reduction - reduce number of filters
        x = self.SeparableConv2D(x, 728, (3, 3), padding='same', **metaparameters)
        x = self.BatchNormalization(x)
        x = self.ReLU(x)

        # Second Depthwise Separable Convolution
        # Dimensionality restoration
        x = self.SeparableConv2D(x, 1024, (3, 3), padding='same', **metaparameters)
        x = self.BatchNormalization(x)
        x = self.ReLU(x)

        # Create pooled feature maps, reduce size by 75%
        x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)

        # Add the projection shortcut to the output of the pooling layer
        x = Add()([x, shortcut])

        # Third Depthwise Separable Convolution
        x = self.SeparableConv2D(x, 1556, (3, 3), padding='same', **metaparameters)
        x = self.BatchNormalization(x)
        x = self.ReLU(x)

        # Fourth Depthwise Separable Convolution
        x = self.SeparableConv2D(x, 2048, (3, 3), padding='same', **metaparameters)
        x = self.BatchNormalization(x)
        x = self.ReLU(x)

        # Create classifier section
        if include_top:
            x = self.classifier(x, n_classes, **metaparameters)

        return x
 Create the exit flow section
            x          : input to the exit flow section
            n_classes  : number of output classes
            include_top: whether to include classifier
        
Create the exit flow section
x          : input to the exit flow section
n_classes  : number of output classes
include_top: whether to include classifier
Create the exit flow section x : input to the exit flow section n_classes : number of output classes include_top : whether to include classifier
def exitFlow(self, x, n_classes, include_top, **metaparameters):
        shortcut = x
        shortcut = self.Conv2D(x, 1024, (1, 1), strides=(2, 2), padding='same', **metaparameters)
        shortcut = self.BatchNormalization(shortcut)
        x = self.SeparableConv2D(x, 728, (3, 3), padding='same', **metaparameters)
        x = self.BatchNormalization(x)
        x = self.ReLU(x)
        x = self.SeparableConv2D(x, 1024, (3, 3), padding='same', **metaparameters)
        x = self.BatchNormalization(x)
        x = self.ReLU(x)
        x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
        x = Add()([x, shortcut])
        x = self.SeparableConv2D(x, 1556, (3, 3), padding='same', **metaparameters)
        x = self.BatchNormalization(x)
        x = self.ReLU(x)
        x = self.SeparableConv2D(x, 2048, (3, 3), padding='same', **metaparameters)
        x = self.BatchNormalization(x)
        x = self.ReLU(x)
        if include_top:
            x = self.classifier(x, n_classes, **metaparameters)
        return x
def exitFlow ( self , x , n_classes , include_top , ** metaparameters ) : shortcut = x shortcut = self . Conv2D ( x , 1024 , ( 1 , 1 ) , strides = ( 2 , 2 ) , padding = 'same' , ** metaparameters ) shortcut = self . BatchNormalization ( shortcut ) x = self . SeparableConv2D ( x , 728 , ( 3 , 3 ) , padding = 'same' , ** metaparameters ) x = self . BatchNormalization ( x ) x = self . ReLU ( x ) x = self . SeparableConv2D ( x , 1024 , ( 3 , 3 ) , padding = 'same' , ** metaparameters ) x = self . BatchNormalization ( x ) x = self . ReLU ( x ) x = MaxPooling2D ( ( 3 , 3 ) , strides = ( 2 , 2 ) , padding = 'same' ) ( x ) x = Add ( ) ( [ x , shortcut ] ) x = self . SeparableConv2D ( x , 1556 , ( 3 , 3 ) , padding = 'same' , ** metaparameters ) x = self . BatchNormalization ( x ) x = self . ReLU ( x ) x = self . SeparableConv2D ( x , 2048 , ( 3 , 3 ) , padding = 'same' , ** metaparameters ) x = self . BatchNormalization ( x ) x = self . ReLU ( x ) if include_top : x = self . classifier ( x , n_classes , ** metaparameters ) return x
Create the exit flow section
x          : input to the exit flow section
n_classes  : number of output classes
include_top: whether to include classifier
Create the exit flow section x : input to the exit flow section n_classes : number of output classes include_top : whether to include classifier
""" Create the exit flow section
            x          : input to the exit flow section
            n_classes  : number of output classes
            include_top: whether to include classifier
        """ # Remember the input # Strided convolution to double number of filters in identity link to # match output of residual block for the add operation (projection shortcut) # First Depthwise Separable Convolution # Dimensionality reduction - reduce number of filters # Second Depthwise Separable Convolution # Dimensionality restoration # Create pooled feature maps, reduce size by 75% # Add the projection shortcut to the output of the pooling layer # Third Depthwise Separable Convolution # Fourth Depthwise Separable Convolution # Create classifier section
self
x
n_classes
include_top