Python* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 4

stoch_pool2d_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: stoch_pool2d_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 neural network forward and backward two-dimensional stochastic pooling layers usage
19 # !
20 # !*****************************************************************************
21 
22 #
23 ## <a name="DAAL-EXAMPLE-PY-STOCHASTIC_POOLING2D_LAYER_BATCH"></a>
24 ## \example stoch_pool2d_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 stochastic_pooling2d
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, readTensorFromCSV
37 
38 # Input non-negative data set name
39 datasetFileName = os.path.join("..", "data", "batch", "layer_non_negative.csv")
40 
41 if __name__ == "__main__":
42 
43  # Read datasetFileName from a file and create a tensor to store input data
44  data = readTensorFromCSV(datasetFileName)
45  nDim = data.getNumberOfDimensions()
46  printTensor(data, "Forward two-dimensional stochastic pooling layer input (first 10 rows):", 10)
47 
48  # Create an algorithm to compute forward two-dimensional stochastic pooling layer results using default method
49  forwardLayer = stochastic_pooling2d.forward.Batch(nDim)
50  forwardLayer.input.setInput(layers.forward.data, data)
51 
52  # Compute forward two-dimensional stochastic pooling layer results
53  forwardLayer.compute()
54 
55  # Get the computed forward two-dimensional stochastic pooling layer results
56  forwardResult = forwardLayer.getResult()
57 
58  printTensor(forwardResult.getResult(layers.forward.value), "Forward two-dimensional stochastic pooling layer result (first 5 rows):", 5)
59  printTensor(forwardResult.getLayerData(layers.stochastic_pooling2d.auxSelectedIndices),
60  "Forward two-dimensional stochastic pooling layer selected indices (first 10 rows):", 10)
61 
62  # Create an algorithm to compute backward two-dimensional stochastic pooling layer results using default method
63  backwardLayer = layers.stochastic_pooling2d.backward.Batch(nDim)
64  backwardLayer.input.setInput(layers.backward.inputGradient, forwardResult.getResult(layers.forward.value))
65  backwardLayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
66 
67  # Compute backward two-dimensional stochastic pooling layer results
68  backwardLayer.compute()
69 
70  # Get the computed backward two-dimensional stochastic pooling layer results
71  backwardResult = backwardLayer.getResult()
72 
73  printTensor(backwardResult.getResult(layers.backward.gradient),
74  "Backward two-dimensional stochastic pooling layer result (first 10 rows):", 10)

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