zoo/mobilenet/mobilenet_v3_c.py
def stem(self, inputs, **metaparameters):
        """ Construct the Stem Group
            inputs : input tensor
            alpha  : width multiplier
        """
        alpha = metaparameters['alpha']

        # Calculate the number of filters for the stem convolution
        # Must be divisible by 8
        n_filters = max(8, (int(32 * alpha) + 4) // 8 * 8)
    
        # Convolutional block
        x = self.Conv2D(inputs, n_filters, (3, 3), strides=(2, 2), padding='same', **metaparameters)
        x = self.BatchNormalization(x)
        x = self.HS(x)
        return x
 Construct the Stem Group
            inputs : input tensor
            alpha  : width multiplier
        
Construct the Stem Group
inputs : input tensor
alpha  : width multiplier
Construct the Stem Group inputs : input tensor alpha : width multiplier
def stem(self, inputs, **metaparameters):
        alpha = metaparameters['alpha']
        n_filters = max(8, (int(32 * alpha) + 4) // 8 * 8)
        x = self.Conv2D(inputs, n_filters, (3, 3), strides=(2, 2), padding='same', **metaparameters)
        x = self.BatchNormalization(x)
        x = self.HS(x)
        return x
def stem ( self , inputs , ** metaparameters ) : alpha = metaparameters [ 'alpha' ] n_filters = max ( 8 , ( int ( 32 * alpha ) + 4 ) // 8 * 8 ) x = self . Conv2D ( inputs , n_filters , ( 3 , 3 ) , strides = ( 2 , 2 ) , padding = 'same' , ** metaparameters ) x = self . BatchNormalization ( x ) x = self . HS ( x ) return x
Construct the Stem Group
inputs : input tensor
alpha  : width multiplier
Construct the Stem Group inputs : input tensor alpha : width multiplier
""" Construct the Stem Group
            inputs : input tensor
            alpha  : width multiplier
        """ # Calculate the number of filters for the stem convolution # Must be divisible by 8 # Convolutional block
self
inputs