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

ave_pool1d_layer_dense_batch.py

1 # file: ave_pool1d_layer_dense_batch.py
2 #===============================================================================
3 # Copyright 2014-2018 Intel Corporation.
4 #
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6 # your use of them is governed by the express license under which they were
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9 # the related documents without Intel's prior written permission.
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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 one-dimensional average 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 
32 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
33 if utils_folder not in sys.path:
34  sys.path.insert(0, utils_folder)
35 from utils import printTensor, readTensorFromCSV,printNumericTable
36 
37 # Input data set name
38 datasetFileName = os.path.join("..", "data", "batch", "layer.csv")
39 
40 if __name__ == "__main__":
41 
42  # Read datasetFileName from a file and create a tensor to store input data
43  data = readTensorFromCSV(datasetFileName)
44  nDim = data.getNumberOfDimensions()
45 
46  printTensor(data, "Forward one-dimensional average pooling layer input (first 10 rows):", 10)
47 
48  # Create an algorithm to compute forward one-dimensional pooling layer results using average method
49  forwardLayer = layers.average_pooling1d.forward.Batch(nDim)
50  forwardLayer.input.setInput(layers.forward.data, data)
51 
52  # Compute forward one-dimensional average pooling layer results
53  # Result class from layers.average_pooling1d.forward
54  forwardResult = forwardLayer.compute()
55 
56  # Print the results of the forward one-dimensional average pooling layer
57  printTensor(forwardResult.getResult(layers.forward.value),
58  "Forward one-dimensional average pooling layer result (first 5 rows):",
59  5)
60  printNumericTable(forwardResult.getLayerData(layers.average_pooling1d.auxInputDimensions),
61  "Forward one-dimensional average pooling layer input dimensions:")
62 
63  # Create an algorithm to compute backward one-dimensional pooling layer results using average method
64  backwardLayer = layers.average_pooling1d.backward.Batch(nDim)
65 
66  # Set input objects for the backward one-dimensional average pooling layer
67  backwardLayer.input.setInput(layers.backward.inputGradient, forwardResult.getResult(layers.forward.value))
68  backwardLayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
69 
70  # Compute backward one-dimensional average pooling layer results
71  # Result class from layers.average_pooling1d.backward
72  backwardResult = backwardLayer.compute()
73 
74  # Print the results of the backward one-dimensional average pooling layer
75  printTensor(backwardResult.getResult(layers.backward.gradient),
76  "Backward one-dimensional average pooling layer result (first 10 rows):",
77  10)

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