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

spat_max_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: spat_max_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 spatial pyramid maximum pooling layers usage
19 # !
20 # !*****************************************************************************
21 
22 #
23 ## <a name="DAAL-EXAMPLE-PY-SPAT_MAX_POOL2D_LAYER_DENSE_BATCH"></a>
24 ## \example spat_max_pool2d_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.algorithms.neural_networks.layers import spatial_maximum_pooling2d
34 from daal.data_management import HomogenTensor
35 
36 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
37 if utils_folder not in sys.path:
38  sys.path.insert(0, utils_folder)
39 from utils import printTensor
40 
41 nDim = 4
42 dims = [2, 3, 2, 4]
43 dataArray = np.array([[[[1, 2, 3, 4],
44  [5, 6, 7, 8]],
45  [[9, 10, 11, 12],
46  [13, 14, 15, 16]],
47  [[17, 18, 19, 20],
48  [21, 22, 23, 24]]],
49  [[[-1, -2, -3, -4],
50  [-5, -6, -7, -8]],
51  [[-9, -10, -11, -12],
52  [-13, -14, -15, -16]],
53  [[-17, -18, -19, -20],
54  [-21, -22, -23, -24]]]],
55  dtype=np.float64)
56 
57 if __name__ == "__main__":
58  data = HomogenTensor(dataArray)
59 
60 
61  printTensor(data, "Forward two-dimensional spatial pyramid maximum pooling layer input (first 10 rows):", 10)
62 
63  # Create an algorithm to compute forward two-dimensional spatial pyramid maximum pooling layer results using default method
64  forwardLayer = spatial_maximum_pooling2d.forward.Batch(2, nDim)
65  forwardLayer.input.setInput(layers.forward.data, data)
66 
67  # Compute forward two-dimensional spatial pyramid maximum pooling layer results
68  forwardLayer.compute()
69 
70  # Get the computed forward two-dimensional spatial pyramid maximum pooling layer results
71  forwardResult = forwardLayer.getResult()
72 
73  printTensor(forwardResult.getResult(layers.forward.value), "Forward two-dimensional spatial pyramid maximum pooling layer result (first 5 rows):", 5)
74  printTensor(forwardResult.getLayerData(layers.spatial_maximum_pooling2d.auxSelectedIndices),
75  "Forward two-dimensional spatial pyramid maximum pooling layer selected indices (first 10 rows):", 10)
76 
77  # Create an algorithm to compute backward two-dimensional spatial pyramid maximum pooling layer results using default method
78  backwardLayer = layers.spatial_maximum_pooling2d.backward.Batch(2, nDim)
79  backwardLayer.input.setInput(layers.backward.inputGradient, forwardResult.getResult(layers.forward.value))
80  backwardLayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
81 
82  # Compute backward two-dimensional spatial pyramid maximum pooling layer results
83  backwardLayer.compute()
84 
85  # Get the computed backward two-dimensional spatial pyramid maximum pooling layer results
86  backwardResult = backwardLayer.getResult()
87 
88  printTensor(backwardResult.getResult(layers.backward.gradient),
89  "Backward two-dimensional spatial pyramid maximum pooling layer result (first 10 rows):", 10)

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