Python* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 3

max_pool3d_layer_dense_batch.py

1 # file: max_pool3d_layer_dense_batch.py
2 #===============================================================================
3 # Copyright 2014-2018 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 maximum 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
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 maximum pooling layer input:")
55 
56  # Create an algorithm to compute forward pooling layer results using maximum method
57  forwardLayer = layers.maximum_pooling3d.forward.Batch(nDim)
58  forwardLayer.input.setInput(layers.forward.data, dataTensor)
59 
60  # Compute forward pooling layer results
61  forwardResult = forwardLayer.compute()
62 
63  printTensor3d(forwardResult.getResult(layers.forward.value), "Forward maximum pooling layer result:")
64  printTensor3d(forwardResult.getLayerData(layers.maximum_pooling3d.auxSelectedIndices),
65  "Forward maximum pooling layer selected indices:")
66 
67  # Create an algorithm to compute backward pooling layer results using maximum method
68  backwardLayer = layers.maximum_pooling3d.backward.Batch(nDim)
69  backwardLayer.input.setInput(layers.backward.inputGradient, forwardResult.getResult(layers.forward.value))
70  backwardLayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
71 
72  # Compute backward pooling layer results
73  backwardResult = backwardLayer.compute()
74 
75  # Print the computed backward pooling layer results
76  printTensor3d(backwardResult.getResult(layers.backward.gradient), "Backward maximum pooling layer result:")

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