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

initializers_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: initializers_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 initializers
19 # !
20 # !*****************************************************************************
21 
22 #
23 ## <a name="DAAL-EXAMPLE-PY-INITIALIZERS_DENSE_BATCH"></a>
24 ## \example initializers_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 import initializers
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
38 
39 if __name__ == "__main__":
40  # Create collection of dimension sizes of the input data tensor
41  inDims = [2, 1, 3, 4]
42  tensorData = HomogenTensor(inDims, TensorIface.doAllocate)
43 
44  # Fill tensor data using truncated gaussian initializer
45  # Create an algorithm to initialize data using default method
46  truncatedGaussInitializer = initializers.truncated_gaussian.Batch(0.0, 1.0)
47 
48  # Set input object and parameters for the truncated gaussian initializer
49  truncatedGaussInitializer.input.set(initializers.data, tensorData)
50 
51  # Compute truncated gaussian initializer
52  truncatedGaussInitializer.compute()
53 
54  # Print the results of the truncated gaussian initializer
55  printTensor(tensorData, "Data with truncated gaussian distribution:")
56 
57 
58  # Fill tensor data using gaussian initializer
59  # Create an algorithm to initialize data using default method
60  gaussInitializer = initializers.gaussian.Batch(1.0, 0.5)
61 
62  # Set input object and parameters for the gaussian initializer
63  gaussInitializer.input.set(initializers.data, tensorData)
64 
65  # Compute gaussian initializer
66  gaussInitializer.compute()
67 
68  # Print the results of the gaussian initializer
69  printTensor(tensorData, "Data with gaussian distribution:")
70 
71 
72  # Fill tensor data using uniform initializer
73  # Create an algorithm to initialize data using default method
74  uniformInitializer = initializers.uniform.Batch(-5.0, 5.0)
75 
76  # Set input object and parameters for the uniform initializer
77  uniformInitializer.input.set(initializers.data, tensorData)
78 
79  # Compute uniform initializer
80  uniformInitializer.compute()
81 
82  # Print the results of the uniform initializer
83  printTensor(tensorData, "Data with uniform distribution:")
84 
85 
86  # Fill layer weights using xavier initializer
87  # Create an algorithm to compute forward fully-connected layer results using default method
88  fullyconnectedLayerForward = layers.fullyconnected.forward.Batch(5)
89 
90  # Set input objects and parameter for the forward fully-connected layer
91  fullyconnectedLayerForward.input.setInput(layers.forward.data, tensorData)
92  fullyconnectedLayerForward.parameter.weightsInitializer = initializers.xavier.Batch()
93 
94  # Compute forward fully-connected layer results
95  fullyconnectedLayerForward.compute()
96 
97  # Print the results of the xavier initializer
98  printTensor(fullyconnectedLayerForward.input.getInput(layers.forward.weights), "Weights filled by xavier initializer:")

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