zoo/xception/xception_c.py
def entryFlow(self, inputs, init_weights=None, **metaparameters):
        """ Create the entry flow section
            inputs : input tensor to neural network
            blocks : number of filters per block
        """

        def stem(inputs):
            """ Create the stem entry into the neural network
                inputs : input tensor to neural network
            """
            # Strided convolution - dimensionality reduction
            # Reduce feature maps by 75%
            x = self.Conv2D(inputs, 32, (3, 3), strides=(2, 2), **metaparameters)
            x = self.BatchNormalization(x)
            x = self.ReLU(x)

            # Convolution - dimensionality expansion
            # Double the number of filters
            x = self.Conv2D(x, 64, (3, 3), strides=(1, 1), **metaparameters)
            x = self.BatchNormalization(x)
            x = self.ReLU(x)
            return x

        blocks = metaparameters['blocks']

        # Create the stem to the neural network
        x = stem(inputs)

        # Create residual blocks using linear projection
        for block in blocks:
            x = self.projection_block(x, **block)

        return x
 Create the entry flow section
            inputs : input tensor to neural network
            blocks : number of filters per block
        
Create the entry flow section
inputs : input tensor to neural network
blocks : number of filters per block
Create the entry flow section inputs : input tensor to neural network blocks : number of filters per block
def entryFlow(self, inputs, init_weights=None, **metaparameters):
        def stem(inputs):
            x = self.Conv2D(inputs, 32, (3, 3), strides=(2, 2), **metaparameters)
            x = self.BatchNormalization(x)
            x = self.ReLU(x)
            x = self.Conv2D(x, 64, (3, 3), strides=(1, 1), **metaparameters)
            x = self.BatchNormalization(x)
            x = self.ReLU(x)
            return x
        blocks = metaparameters['blocks']
        x = stem(inputs)
        for block in blocks:
            x = self.projection_block(x, **block)
        return x
def entryFlow ( self , inputs , init_weights = None , ** metaparameters ) : def stem ( inputs ) : """ Create the stem entry into the neural network
                inputs : input tensor to neural network
            """ x = self . Conv2D ( inputs , 32 , ( 3 , 3 ) , strides = ( 2 , 2 ) , ** metaparameters ) x = self . BatchNormalization ( x ) x = self . ReLU ( x ) x = self . Conv2D ( x , 64 , ( 3 , 3 ) , strides = ( 1 , 1 ) , ** metaparameters ) x = self . BatchNormalization ( x ) x = self . ReLU ( x ) return x blocks = metaparameters [ 'blocks' ] x = stem ( inputs ) for block in blocks : x = self . projection_block ( x , ** block ) return x
Create the entry flow section
inputs : input tensor to neural network
blocks : number of filters per block
Create the entry flow section inputs : input tensor to neural network blocks : number of filters per block
""" Create the entry flow section
            inputs : input tensor to neural network
            blocks : number of filters per block
        """ """ Create the stem entry into the neural network
                inputs : input tensor to neural network
            """ # Strided convolution - dimensionality reduction # Reduce feature maps by 75% # Convolution - dimensionality expansion # Double the number of filters # Create the stem to the neural network # Create residual blocks using linear projection
self
inputs
init_weights