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

max_pool2d_layer_dense_batch.py

1 # file: max_pool2d_layer_dense_batch.py
2 #===============================================================================
3 # Copyright 2014-2019 Intel Corporation.
4 #
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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.
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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 maximum pooling layers usage
19 # !
20 # !*****************************************************************************
21 
22 #
23 
24 
25 #
26 
27 import os
28 import sys
29 
30 from daal.algorithms.neural_networks import layers
31 from daal.algorithms.neural_networks.layers import maximum_pooling2d
32 
33 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
34 if utils_folder not in sys.path:
35  sys.path.insert(0, utils_folder)
36 from utils import printTensor, readTensorFromCSV
37 
38 # Input data set name
39 datasetFileName = os.path.join("..", "data", "batch", "layer.csv")
40 
41 if __name__ == "__main__":
42 
43  # Read datasetFileName from a file and create a tensor to store input data
44  data = readTensorFromCSV(datasetFileName)
45  nDim = data.getNumberOfDimensions()
46 
47  printTensor(data, "Forward two-dimensional maximum pooling layer input (first 10 rows):", 10)
48 
49  # Create an algorithm to compute forward two-dimensional maximum pooling layer results using default method
50  forwardLayer = maximum_pooling2d.forward.Batch(nDim)
51  forwardLayer.input.setInput(layers.forward.data, data)
52 
53  # Compute forward two-dimensional maximum pooling layer results
54  forwardLayer.compute()
55 
56  # Get the computed forward two-dimensional maximum pooling layer results
57  forwardResult = forwardLayer.getResult()
58 
59  printTensor(forwardResult.getResult(layers.forward.value), "Forward two-dimensional maximum pooling layer result (first 5 rows):", 5)
60  printTensor(forwardResult.getLayerData(layers.maximum_pooling2d.auxSelectedIndices),
61  "Forward two-dimensional maximum pooling layer selected indices (first 10 rows):", 10)
62 
63  # Create an algorithm to compute backward two-dimensional maximum pooling layer results using default method
64  backwardLayer = layers.maximum_pooling2d.backward.Batch(nDim)
65  backwardLayer.input.setInput(layers.backward.inputGradient, forwardResult.getResult(layers.forward.value))
66  backwardLayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
67 
68  # Compute backward two-dimensional maximum pooling layer results
69  backwardLayer.compute()
70 
71  # Get the computed backward two-dimensional maximum pooling layer results
72  backwardResult = backwardLayer.getResult()
73 
74  printTensor(backwardResult.getResult(layers.backward.gradient),
75  "Backward two-dimensional maximum pooling layer result (first 10 rows):", 10)

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