zoo/jumpnet/jumpnet_c.py
def stem(self, inputs, **metaparameters):
        """ Construct the Stem Convolutional Group 
            inputs : the input vector
            stack  : convolutional filters in two 3x3 stack
        """
        stack = metaparameters['stem']
        n_filters = stack['n_filters']

        pooling = stack['pooling']
        if pooling == 'feature':
           strides = (2, 2)
        else:
           strides = (1, 1)
    
        # Stack of two 3x3 filters
        x = self.Conv2D(inputs, n_filters[0], (3, 3), strides=strides, padding='same')
        x = self.BatchNormalization(x)
        x = self.ReLU(x)

        x = self.Conv2D(x, n_filters[1], (3, 3), strides=(1, 1), padding='same')
        x = self.BatchNormalization(x)
        x = self.ReLU(x)

        return x
 Construct the Stem Convolutional Group 
            inputs : the input vector
            stack  : convolutional filters in two 3x3 stack
        
Construct the Stem Convolutional Group
inputs : the input vector
stack  : convolutional filters in two 3x3 stack
Construct the Stem Convolutional Group inputs : the input vector stack : convolutional filters in two 3x3 stack
def stem(self, inputs, **metaparameters):
        stack = metaparameters['stem']
        n_filters = stack['n_filters']
        pooling = stack['pooling']
        if pooling == 'feature':
           strides = (2, 2)
        else:
           strides = (1, 1)
        x = self.Conv2D(inputs, n_filters[0], (3, 3), strides=strides, padding='same')
        x = self.BatchNormalization(x)
        x = self.ReLU(x)
        x = self.Conv2D(x, n_filters[1], (3, 3), strides=(1, 1), padding='same')
        x = self.BatchNormalization(x)
        x = self.ReLU(x)
        return x
def stem ( self , inputs , ** metaparameters ) : stack = metaparameters [ 'stem' ] n_filters = stack [ 'n_filters' ] pooling = stack [ 'pooling' ] if pooling == 'feature' : strides = ( 2 , 2 ) else : strides = ( 1 , 1 ) x = self . Conv2D ( inputs , n_filters [ 0 ] , ( 3 , 3 ) , strides = strides , padding = 'same' ) x = self . BatchNormalization ( x ) x = self . ReLU ( x ) x = self . Conv2D ( x , n_filters [ 1 ] , ( 3 , 3 ) , strides = ( 1 , 1 ) , padding = 'same' ) x = self . BatchNormalization ( x ) x = self . ReLU ( x ) return x
Construct the Stem Convolutional Group
inputs : the input vector
stack  : convolutional filters in two 3x3 stack
Construct the Stem Convolutional Group inputs : the input vector stack : convolutional filters in two 3x3 stack
""" Construct the Stem Convolutional Group 
            inputs : the input vector
            stack  : convolutional filters in two 3x3 stack
        """ # Stack of two 3x3 filters
self
inputs