Python* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 1

max_pool2d_layer_dense_batch.py

1 # file: max_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 maximum pooling layers usage
45 # !
46 # !*****************************************************************************
47 
48 #
49 
50 
51 #
52 
53 import os
54 import sys
55 
56 from daal.algorithms.neural_networks import layers
57 from daal.algorithms.neural_networks.layers import maximum_pooling2d
58 
59 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
60 if utils_folder not in sys.path:
61  sys.path.insert(0, utils_folder)
62 from utils import printTensor, readTensorFromCSV
63 
64 # Input data set name
65 datasetFileName = os.path.join("..", "data", "batch", "layer.csv")
66 
67 if __name__ == "__main__":
68 
69  # Read datasetFileName from a file and create a tensor to store input data
70  data = readTensorFromCSV(datasetFileName)
71  nDim = data.getNumberOfDimensions()
72 
73  printTensor(data, "Forward two-dimensional maximum pooling layer input (first 10 rows):", 10)
74 
75  # Create an algorithm to compute forward two-dimensional maximum pooling layer results using default method
76  forwardLayer = maximum_pooling2d.forward.Batch(nDim)
77  forwardLayer.input.setInput(layers.forward.data, data)
78 
79  # Compute forward two-dimensional maximum pooling layer results
80  forwardLayer.compute()
81 
82  # Get the computed forward two-dimensional maximum pooling layer results
83  forwardResult = forwardLayer.getResult()
84 
85  printTensor(forwardResult.getResult(layers.forward.value), "Forward two-dimensional maximum pooling layer result (first 5 rows):", 5)
86  printTensor(forwardResult.getLayerData(layers.maximum_pooling2d.auxSelectedIndices),
87  "Forward two-dimensional maximum pooling layer selected indices (first 10 rows):", 10)
88 
89  # Create an algorithm to compute backward two-dimensional maximum pooling layer results using default method
90  backwardLayer = layers.maximum_pooling2d.backward.Batch(nDim)
91  backwardLayer.input.setInput(layers.backward.inputGradient, forwardResult.getResult(layers.forward.value))
92  backwardLayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
93 
94  # Compute backward two-dimensional maximum pooling layer results
95  backwardLayer.compute()
96 
97  # Get the computed backward two-dimensional maximum pooling layer results
98  backwardResult = backwardLayer.getResult()
99 
100  printTensor(backwardResult.getResult(layers.backward.gradient),
101  "Backward two-dimensional maximum pooling layer result (first 10 rows):", 10)

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