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.algorithms.neural_networks.layers
import loss
32 from daal.algorithms.neural_networks.layers.loss
import logistic_cross
34 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
35 if utils_folder
not in sys.path:
36 sys.path.insert(0, utils_folder)
37 from utils
import printTensor, readTensorFromCSV
40 datasetName = os.path.join(
"..",
"data",
"batch",
"logistic_cross_entropy_layer.csv")
41 datasetGroundTruthName = os.path.join(
"..",
"data",
"batch",
"logistic_cross_entropy_layer_ground_truth.csv")
43 if __name__ ==
"__main__":
46 tensorData = readTensorFromCSV(datasetName)
47 groundTruth = readTensorFromCSV(datasetGroundTruthName)
50 logisticCrossLayerForward = loss.logistic_cross.forward.Batch(method=loss.logistic_cross.defaultDense)
53 logisticCrossLayerForward.input.setInput(layers.forward.data, tensorData)
54 logisticCrossLayerForward.input.setInput(loss.forward.groundTruth, groundTruth)
57 forwardResult = logisticCrossLayerForward.compute()
60 printTensor(forwardResult.getResult(layers.forward.value),
"Forward logistic cross-entropy layer result (first 5 rows):", 5)
61 printTensor(forwardResult.getLayerData(loss.logistic_cross.auxGroundTruth),
"Logistic Cross-Entropy layer ground truth (first 5 rows):", 5)
64 logisticCrossLayerBackward = logistic_cross.backward.Batch(method=loss.logistic_cross.defaultDense)
67 logisticCrossLayerBackward.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
70 backwardResult = logisticCrossLayerBackward.compute()
73 printTensor(backwardResult.getResult(layers.backward.gradient),
"Backward logistic cross-entropy layer result (first 5 rows):", 5)