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

ave_pool3d_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_pool3d_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 three-dimensional average pooling layers usage
19 # !
20 # !*****************************************************************************
21 
22 #
23 ## <a name="DAAL-EXAMPLE-PY-AVERAGE_POOLING3D_LAYER_BATCH"></a>
24 ## \example ave_pool3d_layer_dense_batch.py
25 #
26 
27 import os
28 import sys
29 
30 import numpy as np
31 
32 from daal.algorithms.neural_networks import layers
33 from daal.data_management import HomogenTensor
34 
35 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
36 if utils_folder not in sys.path:
37  sys.path.insert(0, utils_folder)
38 from utils import printTensor3d, printNumericTable
39 
40 nDim = 3
41 dims = [3, 2, 4]
42 dataArray = np.array([[[1, 2, 3, 4],
43  [5, 6, 7, 8]],
44  [[9, 10, 11, 12],
45  [13, 14, 15, 16]],
46  [[17, 18, 19, 20],
47  [21, 22, 23, 24]]],
48  dtype=np.float64)
49 
50 if __name__ == "__main__":
51 
52  dataTensor = HomogenTensor(dataArray)
53 
54  printTensor3d(dataTensor, "Forward average pooling layer input:")
55 
56  # Create an algorithm to compute forward pooling layer results using average method
57  forwardLayer = layers.average_pooling3d.forward.Batch(nDim)
58  forwardLayer.input.setInput(layers.forward.data, dataTensor)
59 
60  # Compute forward pooling layer results
61  # Result class from layers.average_pooling3d.forward
62  forwardResult = forwardLayer.compute()
63 
64  printTensor3d(forwardResult.getResult(layers.forward.value), "Forward average pooling layer result:")
65  printNumericTable(forwardResult.getLayerData(layers.average_pooling3d.auxInputDimensions),
66  "Forward pooling layer input dimensions:")
67 
68  # Create an algorithm to compute backward pooling layer results using average method
69  backwardLayer = layers.average_pooling3d.backward.Batch(nDim)
70  backwardLayer.input.setInput(layers.backward.inputGradient, forwardResult.getResult(layers.forward.value))
71  backwardLayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
72 
73  # Compute backward pooling layer results
74  # Result class from layers.average_pooling3d.backward
75  backwardResult = backwardLayer.compute()
76 
77  printTensor3d(backwardResult.getResult(layers.backward.gradient), "Backward average pooling layer result:")

For more complete information about compiler optimizations, see our Optimization Notice.