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

loss_logistic_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_logistic_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 logistic cross-entropy layer usage
19 # !
20 # !*****************************************************************************
21 
22 #
23 ## <a name="DAAL-EXAMPLE-PY-LOSS_LOGISTIC_ENTR_LAYER_DENSE_BATCH"></a>
24 ## \example loss_logistic_entr_layer_dense_batch.py
25 #
26 
27 import os
28 import sys
29 
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
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 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")
42 
43 if __name__ == "__main__":
44 
45  # Retrieve the input data
46  tensorData = readTensorFromCSV(datasetName)
47  groundTruth = readTensorFromCSV(datasetGroundTruthName)
48 
49  # Create an algorithm to compute forward logistic cross-entropy layer results using default method
50  logisticCrossLayerForward = loss.logistic_cross.forward.Batch(method=loss.logistic_cross.defaultDense)
51 
52  # Set input objects for the forward logistic_cross layer
53  logisticCrossLayerForward.input.setInput(layers.forward.data, tensorData)
54  logisticCrossLayerForward.input.setInput(loss.forward.groundTruth, groundTruth)
55 
56  # Compute forward logistic_cross layer results
57  forwardResult = logisticCrossLayerForward.compute()
58 
59  # Print the results of the forward logistic_cross layer
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)
62 
63  # Create an algorithm to compute backward logistic_cross layer results using default method
64  logisticCrossLayerBackward = logistic_cross.backward.Batch(method=loss.logistic_cross.defaultDense)
65 
66  # Set input objects for the backward logistic_cross layer
67  logisticCrossLayerBackward.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
68 
69  # Compute backward logistic_cross layer results
70  backwardResult = logisticCrossLayerBackward.compute()
71 
72  # Print the results of the backward logistic_cross layer
73  printTensor(backwardResult.getResult(layers.backward.gradient), "Backward logistic cross-entropy layer result (first 5 rows):", 5)

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