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.
30 from daal.algorithms.neural_networks
import layers
31 from daal.data_management
import HomogenTensor, TensorIface
33 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
34 if utils_folder
not in sys.path:
35 sys.path.insert(0, utils_folder)
36 from utils
import printTensor, readTensorFromCSV
39 datasetFileName = os.path.join(
"..",
"data",
"batch",
"layer.csv")
42 if __name__ ==
"__main__":
45 data = readTensorFromCSV(datasetFileName)
47 printTensor(data,
"Forward batch normalization layer input (first 5 rows):", 5)
50 dataDims = data.getDimensions()
51 dimensionSize = dataDims[dimension]
54 dimensionSizes = [dimensionSize]
57 weights = HomogenTensor(dimensionSizes, TensorIface.doAllocate, 1.0)
58 biases = HomogenTensor(dimensionSizes, TensorIface.doAllocate, 2.0)
59 populationMean = HomogenTensor(dimensionSizes, TensorIface.doAllocate, 0.0)
60 populationVariance = HomogenTensor(dimensionSizes, TensorIface.doAllocate, 0.0)
63 forwardLayer = layers.batch_normalization.forward.Batch()
64 forwardLayer.parameter.dimension = dimension
65 forwardLayer.input.setInput(layers.forward.data, data)
66 forwardLayer.input.setInput(layers.forward.weights, weights)
67 forwardLayer.input.setInput(layers.forward.biases, biases)
68 forwardLayer.input.setInputLayerData(layers.batch_normalization.forward.populationMean, populationMean)
69 forwardLayer.input.setInputLayerData(layers.batch_normalization.forward.populationVariance, populationVariance)
72 forwardResult = forwardLayer.compute()
74 printTensor(forwardResult.getResult(layers.forward.value),
"Forward batch normalization layer result (first 5 rows):", 5)
75 printTensor(forwardResult.getLayerData(layers.batch_normalization.auxMean),
"Mini-batch mean (first 5 values):", 5)
76 printTensor(forwardResult.getLayerData(layers.batch_normalization.auxStandardDeviation),
"Mini-batch standard deviation (first 5 values):", 5)
77 printTensor(forwardResult.getLayerData(layers.batch_normalization.auxPopulationMean),
"Population mean (first 5 values):", 5)
78 printTensor(forwardResult.getLayerData(layers.batch_normalization.auxPopulationVariance),
"Population variance (first 5 values):", 5)
81 inputGradientTensor = HomogenTensor(dataDims, TensorIface.doAllocate, 10.0)
84 backwardLayer = layers.batch_normalization.backward.Batch()
85 backwardLayer.parameter.dimension = dimension
86 backwardLayer.input.setInput(layers.backward.inputGradient, inputGradientTensor)
87 backwardLayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
90 backwardResult = backwardLayer.compute()
92 printTensor(backwardResult.getResult(layers.backward.gradient),
"Backward batch normalization layer result (first 5 rows):", 5)
93 printTensor(backwardResult.getResult(layers.backward.weightDerivatives),
"Weight derivatives (first 5 values):", 5)
94 printTensor(backwardResult.getResult(layers.backward.biasDerivatives),
"Bias derivatives (first 5 values):", 5)