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

softmax_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: softmax_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 softmax layer usage
19 # !
20 # !*****************************************************************************
21 
22 #
23 ## <a name="DAAL-EXAMPLE-PY-SOFTMAX_LAYER_BATCH"></a>
24 ## \example softmax_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 softmax
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 dimension = 1
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 softmax layer results using default method
49  softmaxLayerForward = softmax.forward.Batch()
50  softmaxLayerForward.parameter.dimension = dimension
51 
52  # Set input objects for the forward softmax layer
53  softmaxLayerForward.input.setInput(layers.forward.data, tensorData)
54 
55  # Compute forward softmax layer results
56  forwardResult = softmaxLayerForward.compute()
57 
58  # Print the results of the forward softmax layer
59  printTensor(forwardResult.getResult(layers.forward.value), "Forward softmax layer result (first 5 rows):", 5)
60 
61  # Get the size of forward softmax layer output
62  gDims = forwardResult.getResult(layers.forward.value).getDimensions()
63  tensorDataBack = HomogenTensor(gDims, TensorIface.doAllocate, 0.01)
64 
65  # Create an algorithm to compute backward softmax layer results using default method
66  softmaxLayerBackward = softmax.backward.Batch()
67  softmaxLayerBackward.parameter.dimension = dimension
68 
69  # Set input objects for the backward softmax layer
70  softmaxLayerBackward.input.setInput(layers.backward.inputGradient, tensorDataBack)
71  softmaxLayerBackward.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
72 
73  # Compute backward softmax layer results
74  backwardResult = softmaxLayerBackward.compute()
75 
76  # Print the results of the backward softmax layer
77  printTensor(backwardResult.getResult(layers.backward.gradient), "Backward softmax layer result (first 5 rows):", 5)

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