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: loss_softmax_entr_layer_dense_batch.py
 # 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 softmax cross-entropy layer usage
 # !
 # !*****************************************************************************
 ## \example loss_softmax_entr_layer_dense_batch.py
 import os
 import sys
 from daal.data_management import HomogenTensor
 from daal.algorithms.neural_networks import layers
 from daal.algorithms.neural_networks.layers import loss
 from daal.algorithms.neural_networks.layers.loss import softmax_cross
 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 parameters
 datasetGroundTruth = [[[1, 0, 0, 1]],[[0, 0, 1, 1]],[[1, 0, 0, 1]]];
 dataset = [[[ 1,  2,  3,  4],[ 5,  6,  7,  8]],[[9, 10, 11, 12],[13, 14, 15, 16]],[[17, 18, 19, 20],[21, 22, 23, 24]]];
 if __name__ == "__main__":
     # Retrieve the input data
     groundTruth = HomogenTensor(datasetGroundTruth)
     tensorData = HomogenTensor(dataset)
     printTensor(tensorData, "Forward softmax cross-entropy layer input data:");
     printTensor(groundTruth, "Forward softmax cross-entropy layer input ground truth:");
     # Create an algorithm to compute forward softmax cross-entropy layer results using default method
     softmaxCrossLayerForward = loss.softmax_cross.forward.Batch(method=loss.softmax_cross.defaultDense)
     # Set input objects for the forward softmax_cross layer
     softmaxCrossLayerForward.input.setInput(layers.forward.data, tensorData)
     softmaxCrossLayerForward.input.setInput(loss.forward.groundTruth, groundTruth)
     # Compute forward softmax_cross layer results
     forwardResult = softmaxCrossLayerForward.compute()
     # Print the results of the forward softmax_cross layer
     printTensor(forwardResult.getResult(layers.forward.value), "Forward softmax cross-entropy layer result (first 5 rows):", 5)
     printTensor(forwardResult.getLayerData(loss.softmax_cross.auxProbabilities), "Softmax Cross-Entropy layer probabilities estimations (first 5 rows):", 5)
     printTensor(forwardResult.getLayerData(loss.softmax_cross.auxGroundTruth), "Softmax Cross-Entropy layer ground truth (first 5 rows):", 5)
     # Create an algorithm to compute backward softmax_cross layer results using default method
     softmaxCrossLayerBackward = softmax_cross.backward.Batch(method=loss.softmax_cross.defaultDense)
     # Set input objects for the backward softmax_cross layer
     softmaxCrossLayerBackward.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
     # Compute backward softmax_cross layer results
     backwardResult = softmaxCrossLayerBackward.compute()
     # Print the results of the backward softmax_cross layer
     printTensor(backwardResult.getResult(layers.backward.gradient), "Backward softmax cross-entropy layer result (first 5 rows):", 5)
For more complete information about compiler optimizations, see our Optimization Notice.
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