34 from daal.algorithms.neural_networks
import layers
35 from daal.data_management
import HomogenTensor
37 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
38 if utils_folder
not in sys.path:
39 sys.path.insert(0, utils_folder)
40 from utils
import printTensor3d, printNumericTable
44 dataArray = np.array([[[1, 2, 3, 4],
52 if __name__ ==
"__main__":
54 dataTensor = HomogenTensor(dataArray)
56 printTensor3d(dataTensor,
"Forward average pooling layer input:")
59 forwardLayer = layers.average_pooling3d.forward.Batch(nDim)
60 forwardLayer.input.setInput(layers.forward.data, dataTensor)
64 forwardResult = forwardLayer.compute()
66 printTensor3d(forwardResult.getResult(layers.forward.value),
"Forward average pooling layer result:")
67 printNumericTable(forwardResult.getLayerData(layers.average_pooling3d.auxInputDimensions),
68 "Forward pooling layer input dimensions:")
71 backwardLayer = layers.average_pooling3d.backward.Batch(nDim)
72 backwardLayer.input.setInput(layers.backward.inputGradient, forwardResult.getResult(layers.forward.value))
73 backwardLayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
77 backwardResult = backwardLayer.compute()
79 printTensor3d(backwardResult.getResult(layers.backward.gradient),
"Backward average pooling layer result:")