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

ave_pool1d_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: ave_pool1d_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 one-dimensional average pooling layers usage
19 # !
20 # !*****************************************************************************
21 
22 #
23 ## <a name="DAAL-EXAMPLE-PY-AVERAGE_POOLING1D_LAYER_BATCH"></a>
24 ## \example ave_pool1d_layer_dense_batch.py
25 #
26 
27 import os
28 import sys
29 
30 from daal.algorithms.neural_networks import layers
31 
32 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
33 if utils_folder not in sys.path:
34  sys.path.insert(0, utils_folder)
35 from utils import printTensor, readTensorFromCSV,printNumericTable
36 
37 # Input data set name
38 datasetFileName = os.path.join("..", "data", "batch", "layer.csv")
39 
40 if __name__ == "__main__":
41 
42  # Read datasetFileName from a file and create a tensor to store input data
43  data = readTensorFromCSV(datasetFileName)
44  nDim = data.getNumberOfDimensions()
45 
46  printTensor(data, "Forward one-dimensional average pooling layer input (first 10 rows):", 10)
47 
48  # Create an algorithm to compute forward one-dimensional pooling layer results using average method
49  forwardLayer = layers.average_pooling1d.forward.Batch(nDim)
50  forwardLayer.input.setInput(layers.forward.data, data)
51 
52  # Compute forward one-dimensional average pooling layer results
53  # Result class from layers.average_pooling1d.forward
54  forwardResult = forwardLayer.compute()
55 
56  # Print the results of the forward one-dimensional average pooling layer
57  printTensor(forwardResult.getResult(layers.forward.value),
58  "Forward one-dimensional average pooling layer result (first 5 rows):",
59  5)
60  printNumericTable(forwardResult.getLayerData(layers.average_pooling1d.auxInputDimensions),
61  "Forward one-dimensional average pooling layer input dimensions:")
62 
63  # Create an algorithm to compute backward one-dimensional pooling layer results using average method
64  backwardLayer = layers.average_pooling1d.backward.Batch(nDim)
65 
66  # Set input objects for the backward one-dimensional average pooling layer
67  backwardLayer.input.setInput(layers.backward.inputGradient, forwardResult.getResult(layers.forward.value))
68  backwardLayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
69 
70  # Compute backward one-dimensional average pooling layer results
71  # Result class from layers.average_pooling1d.backward
72  backwardResult = backwardLayer.compute()
73 
74  # Print the results of the backward one-dimensional average pooling layer
75  printTensor(backwardResult.getResult(layers.backward.gradient),
76  "Backward one-dimensional average pooling layer result (first 10 rows):",
77  10)

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