Python* API Reference for Intel® Data Analytics Acceleration Library 2020 Update 1

lcn_layer_dense_batch.py

1 # file: lcn_layer_dense_batch.py
2 #===============================================================================
3 # Copyright 2014-2020 Intel Corporation
4 #
5 # Licensed under the Apache License, Version 2.0 (the "License");
6 # you may not use this file except in compliance with the License.
7 # You may obtain a copy of the License at
8 #
9 # http://www.apache.org/licenses/LICENSE-2.0
10 #
11 # Unless required by applicable law or agreed to in writing, software
12 # distributed under the License is distributed on an "AS IS" BASIS,
13 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 # See the License for the specific language governing permissions and
15 # limitations under the License.
16 #===============================================================================
17 
18 #
19 # ! Content:
20 # ! Python example of forward and backward local contrast normalization layer usage
21 # !
22 # !*****************************************************************************
23 
24 #
25 
26 
27 #
28 
29 import os
30 import sys
31 
32 from daal.algorithms.neural_networks import layers
33 from daal.data_management import HomogenTensor, TensorIface
34 
35 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
36 if utils_folder not in sys.path:
37  sys.path.insert(0, utils_folder)
38 from utils import printTensor
39 
40 # Input data set name
41 datasetFileName = os.path.join("..", "data", "batch", "layer.csv")
42 
43 if __name__ == "__main__":
44 
45  # Create collection of dimension sizes of the input data tensor
46  inDims = [2, 1, 3, 4]
47  tensorData = HomogenTensor(inDims, TensorIface.doAllocate, 1.0)
48 
49  # Create an algorithm to compute forward two-dimensional convolution layer results using default method
50  lcnLayerForward = layers.lcn.forward.Batch()
51  lcnLayerForward.input.setInput(layers.forward.data, tensorData)
52 
53  # Compute forward two-dimensional convolution layer results
54  forwardResult = lcnLayerForward.compute()
55 
56  printTensor(forwardResult.getResult(layers.forward.value), "Forward local contrast normalization layer result:")
57  printTensor(forwardResult.getLayerData(layers.lcn.auxCenteredData), "Centered data tensor:")
58  printTensor(forwardResult.getLayerData(layers.lcn.auxSigma), "Sigma tensor:")
59  printTensor(forwardResult.getLayerData(layers.lcn.auxC), "C tensor:")
60  printTensor(forwardResult.getLayerData(layers.lcn.auxInvMax), "Inverted max(sigma, C):")
61 
62  # Create input gradient tensor for backward two-dimensional convolution layer
63  tensorDataBack = HomogenTensor(inDims, TensorIface.doAllocate, 0.01)
64 
65  # Create an algorithm to compute backward two-dimensional convolution layer results using default method
66  lcnLayerBackward = layers.lcn.backward.Batch()
67  lcnLayerBackward.input.setInput(layers.backward.inputGradient, tensorDataBack)
68  lcnLayerBackward.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
69 
70  # Compute backward two-dimensional convolution layer results
71  backwardResult = lcnLayerBackward.compute()
72 
73  printTensor(backwardResult.getResult(layers.backward.gradient),
74  "Local contrast normalization layer backpropagation gradient result:")

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