<s> import numpy as np <EOL> from neon . backends . nervanagpu import NervanaGPU <EOL> from neon . backends . nervanacpu import NervanaCPU <EOL> def slicable ( dim , pad = <NUM_LIT:0> ) : <EOL> dim0 = np . prod ( dim [ : - <NUM_LIT:1> ] ) + pad <EOL> return ( dim0 , dim [ - <NUM_LIT:1> ] ) <EOL> def test_pooling ( device_id ) : <EOL> ng = NervanaGPU ( stochastic_round = False , bench = True , device_id = device_id ) <EOL> nc = NervanaCPU ( ) <EOL> layer_args = dict ( dtype = np . float32 , N = <NUM_LIT> , C = <NUM_LIT:16> , D = <NUM_LIT:1> , H = <NUM_LIT:32> , W = <NUM_LIT:32> , J = <NUM_LIT:5> ) <EOL> pool_test_args = dict ( ones = <NUM_LIT:0> , cpu = <NUM_LIT:1> , ng = ng , nc = nc , <EOL> alpha = <NUM_LIT:1.0> , <EOL> ascale = <NUM_LIT> , <EOL> beta = <NUM_LIT:0.0> , <EOL> bpower = <NUM_LIT:0.5> , <EOL> layer_g = ng . lrn_layer ( ** layer_args ) , <EOL> layer_c = nc . lrn_layer ( ** layer_args ) , <EOL> ** layer_args ) <EOL> lrn_helper ( ** pool_test_args ) <EOL> def lrn_helper ( dtype , ones , cpu , alpha , beta , ascale , bpower , <EOL> ng , nc , layer_g , layer_c , N , C , D , H , W , J ) : <EOL> dimI = layer_g . dimI <EOL> dimO = layer_g . dimO <EOL> if ones : <EOL> cpuI = np . ones ( slicable ( dimI ) , dtype = np . float32 ) <EOL> cpuB = np . ones ( slicable ( dimI ) , dtype = np . float32 ) <EOL> cpuE = np . ones ( dimO , dtype = np . float32 ) <EOL> cpuO = np . ones ( dimO , dtype = np . float32 ) <EOL> else : <EOL> cpuI = np . random . uniform ( - <NUM_LIT:1.0> , <NUM_LIT:1.0> , slicable ( dimI ) ) . astype ( np . float16 ) . astype ( np . float32 ) <EOL> cpuB = np . random . uniform ( - <NUM_LIT:1.0> , <NUM_LIT:1.0> , slicable ( dimI ) ) . astype ( np . float16 ) . astype ( np . float32 ) <EOL> cpuE = np . random . uniform ( - <NUM_LIT:1.0> , <NUM_LIT:1.0> , dimO ) . astype ( np . float16 ) . astype ( np . float32 ) <EOL> cpuO = np . random . uniform ( - <NUM_LIT:1.0> , <NUM_LIT:1.0> , dimO ) . astype ( np . float16 ) . astype ( np . float32 ) <EOL> devI = ng . array ( cpuI . reshape ( dimI ) , dtype = dtype ) <EOL> devB = ng . array ( cpuB . reshape ( dimI ) , dtype = dtype ) <EOL> devE = ng . array ( cpuE , dtype = dtype ) <EOL> devO = ng . array ( cpuO , dtype = dtype ) <EOL> devD = ng . empty ( dimO , dtype = dtype ) <EOL> cccI = nc . array ( cpuI . reshape ( dimI ) , dtype = dtype ) <EOL> cccB = nc . array ( cpuB . reshape ( dimI ) , dtype = dtype ) <EOL> cccE = nc . array ( cpuE , dtype = dtype ) <EOL> cccO = nc . array ( cpuO , dtype = dtype ) <EOL> cccD = nc . empty ( dimO , dtype = dtype ) <EOL> ng . fprop_lrn ( layer_g , devI , devO , devD , alpha , beta , ascale , bpower ) <EOL> nc . fprop_lrn ( layer_c , cccI , cccO , cccD , None , None , ascale , bpower ) <EOL> print "<STR_LIT>" <EOL> print "<STR_LIT>" <EOL> print cccD . get ( ) . reshape ( C * D * H * W , N ) [ <NUM_LIT:0> : <NUM_LIT:4> , <NUM_LIT:0> : <NUM_LIT:4> ] <EOL> print "<STR_LIT>" <EOL> print devD . get ( ) . reshape ( C * D * H * W , N ) [ <NUM_LIT:0> : <NUM_LIT:4> , <NUM_LIT:0> : <NUM_LIT:4> ] <EOL> print "<STR_LIT>" <EOL> print "<STR_LIT>" <EOL> print cccO . get ( ) . reshape ( C * D * H * W , N ) [ <NUM_LIT:0> : <NUM_LIT:4> , <NUM_LIT:0> : <NUM_LIT:4> ] <EOL> print "<STR_LIT>" <EOL> print devO . get ( ) . reshape ( C * D * H * W , N ) [ <NUM_LIT:0> : <NUM_LIT:4> , <NUM_LIT:0> : <NUM_LIT:4> ] <EOL> ng . bprop_lrn ( layer_g , devI , devO , devE , devB , devD , alpha , beta , ascale , bpower ) <EOL> nc . bprop_lrn ( layer_c , cccI , cccO , cccE , cccB , cccD , None , None , ascale , bpower ) <EOL> print "<STR_LIT>" <EOL> print "<STR_LIT>" <EOL> print cccB . get ( ) . reshape ( C * D * H * W , N ) [ <NUM_LIT:0> : <NUM_LIT:4> , <NUM_LIT:0> : <NUM_LIT:4> ] <EOL> print "<STR_LIT>" <EOL> print devB . get ( ) . reshape ( C * D * H * W , N ) [ <NUM_LIT:0> : <NUM_LIT:4> , <NUM_LIT:0> : <NUM_LIT:4> ] <EOL> if __name__ == '<STR_LIT:__main__>' : <EOL> test_pooling ( <NUM_LIT:0> ) </s>
