zoo/squeezenet/squeezenet_c.py
def fire_block(self, x, **metaparameters):
        ''' Construct a Fire Block
            x        : input to the block
            n_filters: number of filters
            reg      : kernel regularizer
        '''
        n_filters = metaparameters['n_filters']
            
        # squeeze layer
        squeeze = self.Conv2D(x, n_filters, (1, 1), strides=1, padding='same')
        squeeze = self.ReLU(x)

        # branch the squeeze layer into a 1x1 and 3x3 convolution and double the number
        # of filters
        expand1x1 = self.Conv2D(squeeze, n_filters * 4, (1, 1), strides=1, padding='same')
        expand1x1 = self.ReLU(expand1x1)
        expand3x3 = self.Conv2D(squeeze, n_filters * 4, (3, 3), strides=1, padding='same')
        expand3x3 = self.ReLU(expand3x3)

        # concatenate the feature maps from the 1x1 and 3x3 branches
        x = Concatenate()([expand1x1, expand3x3])
        return x
 Construct a Fire Block
            x        : input to the block
            n_filters: number of filters
            reg      : kernel regularizer
        
Construct a Fire Block
x        : input to the block
n_filters: number of filters
reg      : kernel regularizer
Construct a Fire Block x : input to the block n_filters : number of filters reg : kernel regularizer
def fire_block(self, x, **metaparameters):
        n_filters = metaparameters['n_filters']
        squeeze = self.Conv2D(x, n_filters, (1, 1), strides=1, padding='same')
        squeeze = self.ReLU(x)
        expand1x1 = self.Conv2D(squeeze, n_filters * 4, (1, 1), strides=1, padding='same')
        expand1x1 = self.ReLU(expand1x1)
        expand3x3 = self.Conv2D(squeeze, n_filters * 4, (3, 3), strides=1, padding='same')
        expand3x3 = self.ReLU(expand3x3)
        x = Concatenate()([expand1x1, expand3x3])
        return x
def fire_block ( self , x , ** metaparameters ) : n_filters = metaparameters [ 'n_filters' ] squeeze = self . Conv2D ( x , n_filters , ( 1 , 1 ) , strides = 1 , padding = 'same' ) squeeze = self . ReLU ( x ) expand1x1 = self . Conv2D ( squeeze , n_filters * 4 , ( 1 , 1 ) , strides = 1 , padding = 'same' ) expand1x1 = self . ReLU ( expand1x1 ) expand3x3 = self . Conv2D ( squeeze , n_filters * 4 , ( 3 , 3 ) , strides = 1 , padding = 'same' ) expand3x3 = self . ReLU ( expand3x3 ) x = Concatenate ( ) ( [ expand1x1 , expand3x3 ] ) return x
Construct a Fire Block
x        : input to the block
n_filters: number of filters
reg      : kernel regularizer
Construct a Fire Block x : input to the block n_filters : number of filters reg : kernel regularizer
''' Construct a Fire Block
            x        : input to the block
            n_filters: number of filters
            reg      : kernel regularizer
        ''' # squeeze layer # branch the squeeze layer into a 1x1 and 3x3 convolution and double the number # of filters # concatenate the feature maps from the 1x1 and 3x3 branches
self
x