32 from daal.algorithms.neural_networks
import layers
33 from daal.data_management
import HomogenTensor
35 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
36 if utils_folder
not in sys.path:
37 sys.path.insert(0, utils_folder)
38 from utils
import printTensor3d, printNumericTable
42 dataArray = np.array([[[1, 2, 3, 4],
50 if __name__ ==
"__main__":
52 dataTensor = HomogenTensor(dataArray)
54 printTensor3d(dataTensor,
"Forward average pooling layer input:")
57 forwardLayer = layers.average_pooling3d.forward.Batch(nDim)
58 forwardLayer.input.setInput(layers.forward.data, dataTensor)
62 forwardResult = forwardLayer.compute()
64 printTensor3d(forwardResult.getResult(layers.forward.value),
"Forward average pooling layer result:")
65 printNumericTable(forwardResult.getLayerData(layers.average_pooling3d.auxInputDimensions),
66 "Forward pooling layer input dimensions:")
69 backwardLayer = layers.average_pooling3d.backward.Batch(nDim)
70 backwardLayer.input.setInput(layers.backward.inputGradient, forwardResult.getResult(layers.forward.value))
71 backwardLayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
75 backwardResult = backwardLayer.compute()
77 printTensor3d(backwardResult.getResult(layers.backward.gradient),
"Backward average pooling layer result:")