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

lcn_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: lcn_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 local contrast normalization layer usage
19 # !
20 # !*****************************************************************************
21 
22 #
23 ## <a name="DAAL-EXAMPLE-PY-LCN_LAYER_BATCH"></a>
24 ## \example lcn_layer_dense_batch.py
25 #
26 
27 import os
28 import sys
29 
30 from daal.algorithms.neural_networks import layers
31 from daal.data_management import HomogenTensor, TensorIface
32 
33 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
34 if utils_folder not in sys.path:
35  sys.path.insert(0, utils_folder)
36 from utils import printTensor
37 
38 # Input data set name
39 datasetFileName = os.path.join("..", "data", "batch", "layer.csv")
40 
41 if __name__ == "__main__":
42 
43  # Create collection of dimension sizes of the input data tensor
44  inDims = [2, 1, 3, 4]
45  tensorData = HomogenTensor(inDims, TensorIface.doAllocate, 1.0)
46 
47  # Create an algorithm to compute forward two-dimensional convolution layer results using default method
48  lcnLayerForward = layers.lcn.forward.Batch()
49  lcnLayerForward.input.setInput(layers.forward.data, tensorData)
50 
51  # Compute forward two-dimensional convolution layer results
52  forwardResult = lcnLayerForward.compute()
53 
54  printTensor(forwardResult.getResult(layers.forward.value), "Forward local contrast normalization layer result:")
55  printTensor(forwardResult.getLayerData(layers.lcn.auxCenteredData), "Centered data tensor:")
56  printTensor(forwardResult.getLayerData(layers.lcn.auxSigma), "Sigma tensor:")
57  printTensor(forwardResult.getLayerData(layers.lcn.auxC), "C tensor:")
58  printTensor(forwardResult.getLayerData(layers.lcn.auxInvMax), "Inverted max(sigma, C):")
59 
60  # Create input gradient tensor for backward two-dimensional convolution layer
61  tensorDataBack = HomogenTensor(inDims, TensorIface.doAllocate, 0.01)
62 
63  # Create an algorithm to compute backward two-dimensional convolution layer results using default method
64  lcnLayerBackward = layers.lcn.backward.Batch()
65  lcnLayerBackward.input.setInput(layers.backward.inputGradient, tensorDataBack)
66  lcnLayerBackward.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
67 
68  # Compute backward two-dimensional convolution layer results
69  backwardResult = lcnLayerBackward.compute()
70 
71  printTensor(backwardResult.getResult(layers.backward.gradient),
72  "Local contrast normalization layer backpropagation gradient result:")

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