def test_local_abstractconv_gemm():
 image = tensor.ftensor4()
 W = tensor.ftensor4()
 conv = tensor.nnet.conv2d(image, W, input_shape=(1, 32, 32, 32), filter_shape=(32, 32, 3, 3), border_mode='half')
 f = theano.function([image, W], [conv], mode=mode_with_gpu)
 f(numpy.random.rand(1, 32, 32, 32).astype('float32'), numpy.random.rand(32, 32, 3, 3).astype('float32'))

def test_local_abstractconv_gemm ( ) : image = tensor . ftensor4 ( ) W = tensor . ftensor4 ( ) conv = tensor . nnet . conv2d ( image , W , input_shape = ( 1 , 32 , 32 , 32 ) , filter_shape = ( 32 , 32 , 3 , 3 ) , border_mode = 'half' ) f = theano . function ( [ image , W ] , [ conv ] , mode = mode_with_gpu ) f ( numpy . random . rand ( 1 , 32 , 32 , 32 ) . astype ( 'float32' ) , numpy . random . rand ( 32 , 32 , 3 , 3 ) . astype ( 'float32' ) )
we test it here as this is the optimization only that we test .