Python* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 3

loss_softmax_entr_layer_dense_batch.py

1 # file: loss_softmax_entr_layer_dense_batch.py
2 #===============================================================================
3 # Copyright 2014-2018 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 
24 
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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