zoo/mobilenet/mobilenet_v2_c.py
def learner(self, x, **metaparameters):
        """ Construct the Learner
            x        : input to the learner
            alpha    : width multiplier
            expansion: multipler to expand number of filters
        """
        groups = metaparameters['groups']
        alpha  = metaparameters['alpha']
        expansion = metaparameters['expansion']

        last = groups.pop()

        # First Inverted Residual Convolution Group
        group = groups.pop(0)
        x = self.group(x, **group, alpha=alpha, expansion=1)

        # Add remaining Inverted Residual Convolution Groups
        for group in groups:
            x = self.group(x, **group, alpha=alpha, expansion=expansion)

        # Last block is a 1x1 linear convolutional layer,
        # expanding the number of filters to 1280.
        x = self.Conv2D(x, 1280, (1, 1), **metaparameters)
        x = self.BatchNormalization(x)
        x = self.ReLU(x)
        return x
 Construct the Learner
            x        : input to the learner
            alpha    : width multiplier
            expansion: multipler to expand number of filters
        
Construct the Learner
x        : input to the learner
alpha    : width multiplier
expansion: multipler to expand number of filters
Construct the Learner x : input to the learner alpha : width multiplier expansion : multipler to expand number of filters
def learner(self, x, **metaparameters):
        groups = metaparameters['groups']
        alpha  = metaparameters['alpha']
        expansion = metaparameters['expansion']
        last = groups.pop()
        group = groups.pop(0)
        x = self.group(x, **group, alpha=alpha, expansion=1)
        for group in groups:
            x = self.group(x, **group, alpha=alpha, expansion=expansion)
        x = self.Conv2D(x, 1280, (1, 1), **metaparameters)
        x = self.BatchNormalization(x)
        x = self.ReLU(x)
        return x
def learner ( self , x , ** metaparameters ) : groups = metaparameters [ 'groups' ] alpha = metaparameters [ 'alpha' ] expansion = metaparameters [ 'expansion' ] last = groups . pop ( ) group = groups . pop ( 0 ) x = self . group ( x , ** group , alpha = alpha , expansion = 1 ) for group in groups : x = self . group ( x , ** group , alpha = alpha , expansion = expansion ) x = self . Conv2D ( x , 1280 , ( 1 , 1 ) , ** metaparameters ) x = self . BatchNormalization ( x ) x = self . ReLU ( x ) return x
Construct the Learner
x        : input to the learner
alpha    : width multiplier
expansion: multipler to expand number of filters
Construct the Learner x : input to the learner alpha : width multiplier expansion : multipler to expand number of filters
""" Construct the Learner
            x        : input to the learner
            alpha    : width multiplier
            expansion: multipler to expand number of filters
        """ # First Inverted Residual Convolution Group # Add remaining Inverted Residual Convolution Groups # Last block is a 1x1 linear convolutional layer, # expanding the number of filters to 1280.
self
x