Python* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

ave_pool2d_layer_dense_batch.py

1 # file: ave_pool2d_layer_dense_batch.py
2 #===============================================================================
3 # Copyright 2014-2019 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 two-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 two-dimensional average pooling layer input (first 10 rows):", 10)
47 
48  # Create an algorithm to compute forward two-dimensional maximum pooling layer results using default method
49  forwardLayer = layers.average_pooling2d.forward.Batch(nDim)
50  forwardLayer.input.setInput(layers.forward.data, data)
51 
52  # Compute forward two-dimensional average pooling layer results and return them
53  # Result class from layers.average_pooling2d.forward
54  forwardResult = forwardLayer.compute()
55 
56  printTensor(forwardResult.getResult(layers.forward.value),
57  "Forward two-dimensional average pooling layer result (first 5 rows):",
58  5)
59  printNumericTable(forwardResult.getLayerData(layers.average_pooling2d.auxInputDimensions),
60  "Forward two-dimensional average pooling layer input dimensions:")
61 
62  # Create an algorithm to compute backward two-dimensional average pooling layer results using default method
63  backwardLayer = layers.average_pooling2d.backward.Batch(nDim)
64  backwardLayer.input.setInput(layers.backward.inputGradient, forwardResult.getResult(layers.forward.value))
65  backwardLayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
66 
67  # Compute backward two-dimensional average pooling layer results
68  # Result class from layers.average_pooling2d.backward
69  backwardResult = backwardLayer.compute()
70 
71  printTensor(backwardResult.getResult(layers.backward.gradient),
72  "Backward two-dimensional average pooling layer result (first 10 rows):",
73  10)

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