Python* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 2

spat_ave_pool2d_layer_dense_batch.py

1 # file: spat_ave_pool2d_layer_dense_batch.py
2 #===============================================================================
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40 #===============================================================================
41 
42 #
43 # ! Content:
44 # ! Python example of neural network forward and backward two-dimensional spatial pyramid average pooling layers usage
45 # !
46 # !*****************************************************************************
47 
48 #
49 
50 
51 #
52 
53 import os
54 import sys
55 
56 import numpy as np
57 
58 from daal.algorithms.neural_networks import layers
59 from daal.data_management import HomogenTensor
60 
61 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
62 if utils_folder not in sys.path:
63  sys.path.insert(0, utils_folder)
64 from utils import printTensor, printNumericTable
65 
66 nDim = 4
67 dims = [2, 3, 2, 4]
68 dataArray = np.array([[[[2, 4, 6, 8],
69  [10, 12, 14, 16]],
70  [[18, 20, 22, 24],
71  [26, 28, 30, 32]],
72  [[34, 36, 38, 40],
73  [42, 44, 46, 48]]],
74  [[[-2, -4, -6, -8],
75  [-10, -12, -14, -16]],
76  [[-18, -20, -22, -24],
77  [-26, -28, -30, -32]],
78  [[-34, -36, -38, -40],
79  [-42, -44, -46, -48]]]],
80  dtype=np.float64)
81 
82 if __name__ == "__main__":
83  data = HomogenTensor(dataArray)
84 
85  # Read datasetFileName from a file and create a tensor to store input data
86  printTensor(data, "Forward two-dimensional spatial pyramid average pooling layer input (first 10 rows):", 10)
87 
88  # Create an algorithm to compute forward two-dimensional maximum pooling layer results using default method
89  forwardLayer = layers.spatial_average_pooling2d.forward.Batch(2, nDim)
90  forwardLayer.input.setInput(layers.forward.data, data)
91 
92  # Compute forward two-dimensional spatial pyramid average pooling layer results and return them
93  # Result class from layers.spatial_average_pooling2d.forward
94  forwardResult = forwardLayer.compute()
95 
96  printTensor(forwardResult.getResult(layers.forward.value),
97  "Forward two-dimensional spatial pyramid average pooling layer result (first 5 rows):",
98  5)
99  printNumericTable(forwardResult.getLayerData(layers.spatial_average_pooling2d.auxInputDimensions),
100  "Forward two-dimensional spatial pyramid average pooling layer input dimensions:")
101 
102  # Create an algorithm to compute backward two-dimensional spatial pyramid average pooling layer results using default method
103  backwardLayer = layers.spatial_average_pooling2d.backward.Batch(2, nDim)
104  backwardLayer.input.setInput(layers.backward.inputGradient, forwardResult.getResult(layers.forward.value))
105  backwardLayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
106 
107  # Compute backward two-dimensional spatial pyramid average pooling layer results
108  # Result class from layers.spatial_average_pooling2d.backward
109  backwardResult = backwardLayer.compute()
110 
111  printTensor(backwardResult.getResult(layers.backward.gradient),
112  "Backward two-dimensional spatial pyramid average pooling layer result (first 10 rows):",
113  10)

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