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

max_pool1d_layer_dense_batch.py

1 # file: max_pool1d_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 one-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 
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
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 maximum pooling layer input (first 10 rows):", 10)
47 
48  # Create an algorithm to compute forward one-dimensional pooling layer results using maximum method
49  forwardLayer = layers.maximum_pooling1d.forward.Batch(nDim)
50  forwardLayer.input.setInput(layers.forward.data, data)
51 
52  # Compute forward one-dimensional maximum pooling layer results
53  forwardResult = forwardLayer.compute()
54 
55  # Print the results of the forward one-dimensional maximum pooling layer
56  printTensor(forwardResult.getResult(layers.forward.value), "Forward one-dimensional maximum pooling layer result (first 5 rows):", 5)
57  printTensor(forwardResult.getLayerData(layers.maximum_pooling1d.auxSelectedIndices),
58  "Forward one-dimensional maximum pooling layer selected indices (first 5 rows):", 5)
59 
60  # Create an algorithm to compute backward one-dimensional maximum pooling layer results using default method
61  backwardLayer = layers.maximum_pooling1d.backward.Batch(nDim)
62 
63  # Set input objects for the backward one-dimensional maximum pooling layer
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 one-dimensional maximum pooling layer results
68  backwardResult = backwardLayer.compute()
69 
70  # Print the results of the backward one-dimensional maximum pooling layer
71  printTensor(backwardResult.getResult(layers.backward.gradient),
72  "Backward one-dimensional maximum pooling layer result (first 10 rows):", 10)

For more complete information about compiler optimizations, see our Optimization Notice.