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

smoothrelu_layer_dense_batch.py

Deprecation Notice: With the introduction of daal4py, a package that supersedes PyDAAL, Intel is deprecating PyDAAL and will discontinue support starting with Intel® DAAL 2021 and Intel® Distribution for Python 2021. Until then Intel will continue to provide compatible pyDAAL pip and conda packages for newer releases of Intel DAAL and make it available in open source. However, Intel will not add the new features of Intel DAAL to pyDAAL. Intel recommends developers switch to and use daal4py.

Note: To find daal4py examples, refer to daal4py documentation or browse github repository.

1 # file: smoothrelu_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 forward and backward smooth rectified linear unit (smooth relu) layer usage
19 # !
20 # !*****************************************************************************
21 
22 #
23 ## <a name="DAAL-EXAMPLE-PY-SMOOTHRELU_LAYER_BATCH"></a>
24 ## \example smoothrelu_layer_dense_batch.py
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 smoothrelu
32 from daal.data_management import HomogenTensor, TensorIface
33 
34 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
35 if utils_folder not in sys.path:
36  sys.path.insert(0, utils_folder)
37 from utils import printTensor, readTensorFromCSV
38 
39 # Input data set parameters
40 datasetName = os.path.join("..", "data", "batch", "layer.csv")
41 
42 if __name__ == "__main__":
43 
44  # Read datasetFileName from a file and create a tensor to store input data
45  tensorData = readTensorFromCSV(datasetName)
46 
47  # Create an algorithm to compute forward smooth relu layer results using default method
48  smoothreluLayerForward = smoothrelu.forward.Batch()
49 
50  # Set input objects for the forward smooth relu layer
51  smoothreluLayerForward.input.setInput(layers.forward.data, tensorData)
52 
53  # Compute forward smooth relu layer results
54  forwardResult = smoothreluLayerForward.compute()
55 
56  # Print the results of the forward smooth relu layer
57  printTensor(forwardResult.getResult(layers.forward.value), "Forward smooth ReLU layer result (first 5 rows):", 5)
58 
59  # Get the size of forward dropout smooth relu output
60  gDims = forwardResult.getResult(layers.forward.value).getDimensions()
61  tensorDataBack = HomogenTensor(gDims, TensorIface.doAllocate, 0.01)
62 
63  # Create an algorithm to compute backward smooth relu layer results using default method
64  smoothreluLayerBackward = smoothrelu.backward.Batch()
65 
66  # Set input objects for the backward smooth relu layer
67  smoothreluLayerBackward.input.setInput(layers.backward.inputGradient, tensorDataBack)
68  smoothreluLayerBackward.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
69 
70  # Compute backward smooth relu layer results
71  backwardResult = smoothreluLayerBackward.compute()
72 
73  # Print the results of the backward smooth relu layer
74  printTensor(backwardResult.getResult(layers.backward.gradient), "Backward smooth ReLU layer result (first 5 rows):", 5)

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