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

loss_logistic_entr_layer_dense_batch.py

1 # file: loss_logistic_entr_layer_dense_batch.py
2 #===============================================================================
3 # Copyright 2014-2019 Intel Corporation.
4 #
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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.
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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 
24 
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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