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

abs_layer_dense_batch.py

1 # file: abs_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 absolute value (abs) layer usage
19 # !
20 # !
21 # !*****************************************************************************
22 
23 #
24 
25 
26 #
27 
28 import os
29 import sys
30 
31 from daal.algorithms.neural_networks import layers
32 from daal.data_management import HomogenTensor, TensorIface
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 
40 # Input data set parameters
41 datasetName = os.path.join("..", "data", "batch", "layer.csv")
42 
43 if __name__ == "__main__":
44 
45  # Read datasetFileName from a file and create a tensor to store input data
46  tensorData = readTensorFromCSV(datasetName)
47 
48  # Create an algorithm to compute forward abs layer results using default method
49  absLayerForward = layers.abs.forward.Batch()
50 
51  # Set input objects for the forward abs layer
52  absLayerForward.input.setInput(layers.forward.data, tensorData)
53 
54  # Compute forward abs layer results
55  forwardResult = absLayerForward.compute()
56 
57  # Print the results of the forward abs layer
58  printTensor(forwardResult.getResult(layers.forward.value), "Forward abs layer result (first 5 rows):", 5)
59 
60  # Get the size of forward abs layer output
61  gDims = forwardResult.getResult(layers.forward.value).getDimensions()
62  tensorDataBack = HomogenTensor(gDims, TensorIface.doAllocate, 0.01)
63 
64  # Create an algorithm to compute backward abs layer results using default method
65  absLayerBackward = layers.abs.backward.Batch()
66 
67  # Set input objects for the backward abs layer
68  absLayerBackward.input.setInput(layers.backward.inputGradient, tensorDataBack)
69  absLayerBackward.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
70 
71  # Compute backward abs layer results
72  backwardResult = absLayerBackward.compute()
73 
74  # Print the results of the backward abs layer
75  printTensor(backwardResult.getResult(layers.backward.gradient), "Backward abs layer result (first 5 rows):", 5)

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