zoo/mobilenet/mobilenet_v3_c.py
def learner(self, x, **metaparameters):
        """ Construct the Learner
            x        : input to the learner
            alpha    : width multiplier
        """
        groups = metaparameters['groups']
        alpha  = metaparameters['alpha']

        last = groups.pop()

        # Add Attention Residual Convolution Groups
        for group in groups:
            x = self.group(x, **group, **metaparameters)

        # Last block is a 1x1 linear convolutional layer,
        # expanding the number of filters to 1280.
        x = self.Conv2D(x, last['n_filters'] * alpha, (1, 1), strides=(1, 1), padding='same', **metaparameters)
        x = self.BatchNormalization(x)
        x = last['activation'](x)
        return x
 Construct the Learner
            x        : input to the learner
            alpha    : width multiplier
        
Construct the Learner
x        : input to the learner
alpha    : width multiplier
Construct the Learner x : input to the learner alpha : width multiplier
def learner(self, x, **metaparameters):
        groups = metaparameters['groups']
        alpha  = metaparameters['alpha']
        last = groups.pop()
        for group in groups:
            x = self.group(x, **group, **metaparameters)
        x = self.Conv2D(x, last['n_filters'] * alpha, (1, 1), strides=(1, 1), padding='same', **metaparameters)
        x = self.BatchNormalization(x)
        x = last['activation'](x)
        return x
def learner ( self , x , ** metaparameters ) : groups = metaparameters [ 'groups' ] alpha = metaparameters [ 'alpha' ] last = groups . pop ( ) for group in groups : x = self . group ( x , ** group , ** metaparameters ) x = self . Conv2D ( x , last [ 'n_filters' ] * alpha , ( 1 , 1 ) , strides = ( 1 , 1 ) , padding = 'same' , ** metaparameters ) x = self . BatchNormalization ( x ) x = last [ 'activation' ] ( x ) return x
Construct the Learner
x        : input to the learner
alpha    : width multiplier
Construct the Learner x : input to the learner alpha : width multiplier
""" Construct the Learner
            x        : input to the learner
            alpha    : width multiplier
        """ # Add Attention Residual Convolution Groups # Last block is a 1x1 linear convolutional layer, # expanding the number of filters to 1280.
self
x