models/dense_net.py
def process_layer_kernels(self, b, l, epoch):
        """
        Process a given layer's kernels. Return the connection strenght value
        for each kernel in the layer.

        Args:
            b: `int`, identifier number for the block;
            l: `int`, identifier number for the layer inside the block;
            epoch: `int`, current training epoch (or batch).
        """
        cs_table_kernels = []
        # get the conection strength for each kernel in layer l of block b
        for k in range(len(self.kernels_ref_list[b][l])):
            cs_table_kernels.append(
                self.process_kernel(self.kernels_ref_list[b][l][k]))

        return cs_table_kernels

        Process a given layer's kernels. Return the connection strenght value
        for each kernel in the layer.

        Args:
            b: `int`, identifier number for the block;
            l: `int`, identifier number for the layer inside the block;
            epoch: `int`, current training epoch (or batch).
        
Process a given layer's kernels. Return the connection strenght value
for each kernel in the layer.
Process a given layer ' s kernels . Return the connection strenght value for each kernel in the layer .
def process_layer_kernels(self, b, l, epoch):
        cs_table_kernels = []
        for k in range(len(self.kernels_ref_list[b][l])):
            cs_table_kernels.append(
                self.process_kernel(self.kernels_ref_list[b][l][k]))
        return cs_table_kernels
def process_layer_kernels ( self , b , l , epoch ) : cs_table_kernels = [ ] for k in range ( len ( self . kernels_ref_list [ b ] [ l ] ) ) : cs_table_kernels . append ( self . process_kernel ( self . kernels_ref_list [ b ] [ l ] [ k ] ) ) return cs_table_kernels
Process a given layer's kernels.
Process a given layer ' s kernels .
"""
        Process a given layer's kernels. Return the connection strenght value
        for each kernel in the layer.

        Args:
            b: `int`, identifier number for the block;
            l: `int`, identifier number for the layer inside the block;
            epoch: `int`, current training epoch (or batch).
        """ # get the conection strength for each kernel in layer l of block b
self
b
`int`, identifier number for the block.
` int ` identifier number for the block .
l
`int`, identifier number for the layer inside the block.
` int ` identifier number for the layer inside the block .
epoch
`int`, current training epoch (or batch).
` int ` current training epoch ( or batch ) .