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

split_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: split_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 split layer usage
19 # !
20 # !*****************************************************************************
21 
22 #
23 ## <a name="DAAL-EXAMPLE-PY-SPLIT_LAYER_BATCH"></a>
24 ## \example split_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 split
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 data set parameters
39 datasetName = os.path.join("..", "data", "batch", "layer.csv")
40 nOutputs = 3
41 nInputs = 3
42 
43 if __name__ == "__main__":
44 
45  # Read datasetFileName from a file and create a tensor to store input data
46  tensorData = readTensorFromCSV(datasetName)
47 
48  # Create an algorithm to compute forward split layer results using default method
49  splitLayerForward = split.forward.Batch()
50 
51  # Set parameters for the forward split layer
52  splitLayerForward.parameter.nOutputs = nOutputs
53  splitLayerForward.parameter.nInputs = nInputs
54 
55  # Set input objects for the forward split layer
56  splitLayerForward.input.setInput(layers.forward.data, tensorData)
57 
58  printTensor(tensorData, "Split layer input (first 5 rows):", 5)
59 
60  # Compute forward split layer results
61  forwardResult = splitLayerForward.compute()
62 
63  # Print the results of the forward split layer
64  for i in range(nOutputs):
65  printTensor(forwardResult.getResultLayerData(split.forward.valueCollection, i),
66  "Forward split layer result (first 5 rows):", 5)
67 
68  # Create an algorithm to compute backward split layer results using default method
69  splitLayerBackward = split.backward.Batch()
70 
71  # Set parameters for the backward split layer
72  splitLayerBackward.parameter.nOutputs = nOutputs
73  splitLayerBackward.parameter.nInputs = nInputs
74 
75  # Set input objects for the backward split layer
76  splitLayerBackward.input.setInputLayerData(split.backward.inputGradientCollection,
77  forwardResult.getResultLayerData(split.forward.valueCollection))
78 
79  # Compute backward split layer results
80  backwardResult = splitLayerBackward.compute()
81 
82  # Print the results of the backward split layer
83  printTensor(backwardResult.getResult(layers.backward.gradient), "Backward split layer result (first 5 rows):", 5)

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