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.

 # file:
 # Copyright 2014-2019 Intel Corporation.
 # This software and the related documents are Intel copyrighted  materials,  and
 # your use of  them is  governed by the  express license  under which  they were
 # provided to you (License).  Unless the License provides otherwise, you may not
 # use, modify, copy, publish, distribute,  disclose or transmit this software or
 # the related documents without Intel's prior written permission.
 # This software and the related documents  are provided as  is,  with no express
 # or implied  warranties,  other  than those  that are  expressly stated  in the
 # License.
 # !  Content:
 # !    Python example of forward and backward batch normalization layer usage
 # !
 # !*****************************************************************************
 ## \example
 import os
 import sys
 from daal.algorithms.neural_networks import layers
 from daal.data_management import HomogenTensor, TensorIface
 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
 if utils_folder not in sys.path:
     sys.path.insert(0, utils_folder)
 from utils import printTensor, readTensorFromCSV
 # Input data set name
 datasetFileName = os.path.join("..", "data", "batch", "layer.csv")
 dimension = 1
 if __name__ == "__main__":
     # Read datasetFileName from a file and create a tensor to store input data
     data = readTensorFromCSV(datasetFileName)
     printTensor(data, "Forward batch normalization layer input (first 5 rows):", 5)
     # Get collection of dimension sizes of the input data tensor
     dataDims = data.getDimensions()
     dimensionSize = dataDims[dimension]
     # Create a collection of dimension sizes of input weights, biases, population mean and variance tensors
     dimensionSizes = [dimensionSize]
     # Create input weights, biases, population mean and population variance tensors
     weights = HomogenTensor(dimensionSizes, TensorIface.doAllocate, 1.0)
     biases = HomogenTensor(dimensionSizes, TensorIface.doAllocate, 2.0)
     populationMean = HomogenTensor(dimensionSizes, TensorIface.doAllocate, 0.0)
     populationVariance = HomogenTensor(dimensionSizes, TensorIface.doAllocate, 0.0)
     # Create an algorithm to compute forward batch normalization layer results using default method
     forwardLayer = layers.batch_normalization.forward.Batch()
     forwardLayer.parameter.dimension = dimension
     forwardLayer.input.setInput(, data)
     forwardLayer.input.setInput(layers.forward.weights, weights)
     forwardLayer.input.setInput(layers.forward.biases, biases)
     forwardLayer.input.setInputLayerData(layers.batch_normalization.forward.populationMean, populationMean)
     forwardLayer.input.setInputLayerData(layers.batch_normalization.forward.populationVariance, populationVariance)
     # Compute forward batch normalization layer results
     forwardResult = forwardLayer.compute()
     printTensor(forwardResult.getResult(layers.forward.value), "Forward batch normalization layer result (first 5 rows):", 5)
     printTensor(forwardResult.getLayerData(layers.batch_normalization.auxMean), "Mini-batch mean (first 5 values):", 5)
     printTensor(forwardResult.getLayerData(layers.batch_normalization.auxStandardDeviation), "Mini-batch standard deviation (first 5 values):", 5)
     printTensor(forwardResult.getLayerData(layers.batch_normalization.auxPopulationMean), "Population mean (first 5 values):", 5)
     printTensor(forwardResult.getLayerData(layers.batch_normalization.auxPopulationVariance), "Population variance (first 5 values):", 5)
     # Create input gradient tensor for backward batch normalization layer
     inputGradientTensor = HomogenTensor(dataDims, TensorIface.doAllocate, 10.0)
     # Create an algorithm to compute backward batch normalization layer results using default method
     backwardLayer = layers.batch_normalization.backward.Batch()
     backwardLayer.parameter.dimension = dimension
     backwardLayer.input.setInput(layers.backward.inputGradient, inputGradientTensor)
     backwardLayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
     # Compute backward batch normalization layer results
     backwardResult = backwardLayer.compute()
     printTensor(backwardResult.getResult(layers.backward.gradient), "Backward batch normalization layer result (first 5 rows):", 5)
     printTensor(backwardResult.getResult(layers.backward.weightDerivatives), "Weight derivatives (first 5 values):", 5)
     printTensor(backwardResult.getResult(layers.backward.biasDerivatives), "Bias derivatives (first 5 values):", 5)
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