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

fullycon_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: fullycon_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 fully-connected layer usage
19 # !
20 # !*****************************************************************************
21 
22 #
23 ## <a name="DAAL-EXAMPLE-PY-FULLYCONNECTED_LAYER_BATCH"></a>
24 ## \example fullycon_layer_dense_batch.py
25 #
26 
27 import os
28 import sys
29 
30 from daal.algorithms.neural_networks import layers
31 from daal.data_management import HomogenTensor, TensorIface
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 
41 if __name__ == "__main__":
42 
43  k = 0
44  m = 5
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 fully-connected layer results using default method
49  fullyconnectedLayerForward = layers.fullyconnected.forward.Batch(m)
50  fullyconnectedLayerForward.parameter.dim = k
51 
52  # Set input objects for the forward fully-connected layer
53  fullyconnectedLayerForward.input.setInput(layers.forward.data, tensorData)
54 
55  # Compute forward fully-connected layer results
56  forwardResult = fullyconnectedLayerForward.compute()
57 
58  # Print the results of the forward fully-connected layer
59  printTensor(forwardResult.getResult(layers.forward.value),
60  "Forward fully-connected layer result (first 5 rows):", 5)
61  printTensor(forwardResult.getLayerData(layers.fullyconnected.auxWeights),
62  "Forward fully-connected layer weights (first 5 rows):", 5)
63 
64  # Get the size of forward fully-connected layer output
65  gDims = forwardResult.getResult(layers.forward.value).getDimensions()
66  tensorDataBack = HomogenTensor(gDims, TensorIface.doAllocate, 0.01)
67 
68  # Create an algorithm to compute backward fully-connected layer results using default method
69  fullyconnectedLayerBackward = layers.fullyconnected.backward.Batch(m)
70 
71  # Set input objects for the backward fully-connected layer
72  fullyconnectedLayerBackward.input.setInput(layers.backward.inputGradient, tensorDataBack)
73  fullyconnectedLayerBackward.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
74 
75  # Compute backward fully-connected layer results
76  backwardResult = fullyconnectedLayerBackward.compute()
77 
78  # Print the results of the backward fully-connected layer
79  printTensor(backwardResult.getResult(layers.backward.gradient),
80  "Backward fully-connected layer gradient result (first 5 rows):", 5)
81  printTensor(backwardResult.getResult(layers.backward.weightDerivatives),
82  "Backward fully-connected layer weightDerivative result (first 5 rows):", 5)
83  printTensor(backwardResult.getResult(layers.backward.biasDerivatives),
84  "Backward fully-connected layer biasDerivative result (first 5 rows):", 5)

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