zoo/mobilenet/mobilenet_v1_c.py
def stem(self, inputs, **metaparameters):
        """ Construct the Stem Group
            inputs : input tensor
            alpha  : width multiplier
        """
        alpha = metaparameters['alpha']

        # Convolutional block
        x = ZeroPadding2D(padding=((0, 1), (0, 1)))(inputs)
        x = self.Conv2D(x, 32 * alpha, (3, 3), strides=(2, 2), padding='valid')
        x = self.BatchNormalization(x)
        x = self.ReLU(x)

        # Depthwise Separable Convolution Block
        x = self.depthwise_block(x, (1, 1), n_filters=64, alpha=alpha)
        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']
        x = ZeroPadding2D(padding=((0, 1), (0, 1)))(inputs)
        x = self.Conv2D(x, 32 * alpha, (3, 3), strides=(2, 2), padding='valid')
        x = self.BatchNormalization(x)
        x = self.ReLU(x)
        x = self.depthwise_block(x, (1, 1), n_filters=64, alpha=alpha)
        return x
def stem ( self , inputs , ** metaparameters ) : alpha = metaparameters [ 'alpha' ] x = ZeroPadding2D ( padding = ( ( 0 , 1 ) , ( 0 , 1 ) ) ) ( inputs ) x = self . Conv2D ( x , 32 * alpha , ( 3 , 3 ) , strides = ( 2 , 2 ) , padding = 'valid' ) x = self . BatchNormalization ( x ) x = self . ReLU ( x ) x = self . depthwise_block ( x , ( 1 , 1 ) , n_filters = 64 , alpha = alpha ) 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
        """ # Convolutional block # Depthwise Separable Convolution Block
self
inputs