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

ave_pool3d_layer_dense_batch.py

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 
24 
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:")

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