Python* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

loss_softmax_entr_layer_dense_batch.py

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.

1 # file: loss_softmax_entr_layer_dense_batch.py
2 #===============================================================================
3 # Copyright 2014-2019 Intel Corporation.
4 #
5 # This software and the related documents are Intel copyrighted materials, and
6 # your use of them is governed by the express license under which they were
7 # provided to you (License). Unless the License provides otherwise, you may not
8 # use, modify, copy, publish, distribute, disclose or transmit this software or
9 # the related documents without Intel's prior written permission.
10 #
11 # This software and the related documents are provided as is, with no express
12 # or implied warranties, other than those that are expressly stated in the
13 # License.
14 #===============================================================================
15 
16 #
17 # ! Content:
18 # ! Python example of forward and backward softmax cross-entropy layer usage
19 # !
20 # !*****************************************************************************
21 
22 #
23 ## <a name="DAAL-EXAMPLE-PY-LOSS_SOFTMAX_ENTR_LAYER_DENSE_BATCH"></a>
24 ## \example loss_softmax_entr_layer_dense_batch.py
25 #
26 
27 import os
28 import sys
29 from daal.data_management import HomogenTensor
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 softmax_cross
33 
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
38 
39 # Input data set parameters
40 datasetGroundTruth = [[[1, 0, 0, 1]],[[0, 0, 1, 1]],[[1, 0, 0, 1]]];
41 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]]];
42 
43 
44 if __name__ == "__main__":
45 
46  # Retrieve the input data
47  groundTruth = HomogenTensor(datasetGroundTruth)
48  tensorData = HomogenTensor(dataset)
49 
50  printTensor(tensorData, "Forward softmax cross-entropy layer input data:");
51  printTensor(groundTruth, "Forward softmax cross-entropy layer input ground truth:");
52 
53  # Create an algorithm to compute forward softmax cross-entropy layer results using default method
54  softmaxCrossLayerForward = loss.softmax_cross.forward.Batch(method=loss.softmax_cross.defaultDense)
55 
56  # Set input objects for the forward softmax_cross layer
57  softmaxCrossLayerForward.input.setInput(layers.forward.data, tensorData)
58  softmaxCrossLayerForward.input.setInput(loss.forward.groundTruth, groundTruth)
59 
60  # Compute forward softmax_cross layer results
61  forwardResult = softmaxCrossLayerForward.compute()
62 
63  # Print the results of the forward softmax_cross layer
64  printTensor(forwardResult.getResult(layers.forward.value), "Forward softmax cross-entropy layer result (first 5 rows):", 5)
65  printTensor(forwardResult.getLayerData(loss.softmax_cross.auxProbabilities), "Softmax Cross-Entropy layer probabilities estimations (first 5 rows):", 5)
66  printTensor(forwardResult.getLayerData(loss.softmax_cross.auxGroundTruth), "Softmax Cross-Entropy layer ground truth (first 5 rows):", 5)
67 
68  # Create an algorithm to compute backward softmax_cross layer results using default method
69  softmaxCrossLayerBackward = softmax_cross.backward.Batch(method=loss.softmax_cross.defaultDense)
70 
71  # Set input objects for the backward softmax_cross layer
72  softmaxCrossLayerBackward.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
73 
74  # Compute backward softmax_cross layer results
75  backwardResult = softmaxCrossLayerBackward.compute()
76 
77  # Print the results of the backward softmax_cross layer
78  printTensor(backwardResult.getResult(layers.backward.gradient), "Backward softmax cross-entropy layer result (first 5 rows):", 5)

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