Deprecation Notice: With the introduction of daal4py, a package that supersedes PyDAAL, Intel is deprecating PyDAAL and will discontinue support starting with Intel® DAAL 2021 and Intel® Distribution for Python 2021. Until then Intel will continue to provide compatible pyDAAL pip and conda packages for newer releases of Intel DAAL and make it available in open source. However, Intel will not add the new features of Intel DAAL to pyDAAL. Intel recommends developers switch to and use daal4py.
Note: To find daal4py examples, refer to daal4py documentation or browse github repository.
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 printTensor, printNumericTable
42 dataArray = np.array([[[[2, 4, 6, 8],
49 [-10, -12, -14, -16]],
50 [[-18, -20, -22, -24],
51 [-26, -28, -30, -32]],
52 [[-34, -36, -38, -40],
53 [-42, -44, -46, -48]]]],
56 if __name__ ==
"__main__":
57 data = HomogenTensor(dataArray)
60 printTensor(data,
"Forward two-dimensional spatial pyramid average pooling layer input (first 10 rows):", 10)
63 forwardLayer = layers.spatial_average_pooling2d.forward.Batch(2, nDim)
64 forwardLayer.input.setInput(layers.forward.data, data)
68 forwardResult = forwardLayer.compute()
70 printTensor(forwardResult.getResult(layers.forward.value),
71 "Forward two-dimensional spatial pyramid average pooling layer result (first 5 rows):",
73 printNumericTable(forwardResult.getLayerData(layers.spatial_average_pooling2d.auxInputDimensions),
74 "Forward two-dimensional spatial pyramid average pooling layer input dimensions:")
77 backwardLayer = layers.spatial_average_pooling2d.backward.Batch(2, nDim)
78 backwardLayer.input.setInput(layers.backward.inputGradient, forwardResult.getResult(layers.forward.value))
79 backwardLayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
83 backwardResult = backwardLayer.compute()
85 printTensor(backwardResult.getResult(layers.backward.gradient),
86 "Backward two-dimensional spatial pyramid average pooling layer result (first 10 rows):",