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

stoch_pool2d_layer_dense_batch.py

1 # file: stoch_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.
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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 stochastic 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 stochastic_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 non-negative data set name
39 datasetFileName = os.path.join("..", "data", "batch", "layer_non_negative.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  printTensor(data, "Forward two-dimensional stochastic pooling layer input (first 10 rows):", 10)
47 
48  # Create an algorithm to compute forward two-dimensional stochastic pooling layer results using default method
49  forwardLayer = stochastic_pooling2d.forward.Batch(nDim)
50  forwardLayer.input.setInput(layers.forward.data, data)
51 
52  # Compute forward two-dimensional stochastic pooling layer results
53  forwardLayer.compute()
54 
55  # Get the computed forward two-dimensional stochastic pooling layer results
56  forwardResult = forwardLayer.getResult()
57 
58  printTensor(forwardResult.getResult(layers.forward.value), "Forward two-dimensional stochastic pooling layer result (first 5 rows):", 5)
59  printTensor(forwardResult.getLayerData(layers.stochastic_pooling2d.auxSelectedIndices),
60  "Forward two-dimensional stochastic pooling layer selected indices (first 10 rows):", 10)
61 
62  # Create an algorithm to compute backward two-dimensional stochastic pooling layer results using default method
63  backwardLayer = layers.stochastic_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 stochastic pooling layer results
68  backwardLayer.compute()
69 
70  # Get the computed backward two-dimensional stochastic pooling layer results
71  backwardResult = backwardLayer.getResult()
72 
73  printTensor(backwardResult.getResult(layers.backward.gradient),
74  "Backward two-dimensional stochastic pooling layer result (first 10 rows):", 10)

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