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

lrn_layer_dense_batch.py

1 # file: lrn_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 response normalization (lrn) 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.algorithms.neural_networks.layers import lrn
34 from daal.data_management import HomogenTensor, TensorIface
35 
36 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
37 if utils_folder not in sys.path:
38  sys.path.insert(0, utils_folder)
39 from utils import printTensor, readTensorFromCSV
40 
41 # Input data set parameters
42 datasetName = os.path.join("..", "data", "batch", "layer.csv")
43 
44 if __name__ == "__main__":
45 
46  # Read datasetFileName from a file and create a tensor to store input data
47  tensorData = readTensorFromCSV(datasetName)
48 
49  # Create an algorithm to compute forward local response normalization layer results using default method
50  forwardLRNlayer = lrn.forward.Batch()
51 
52  # Set input objects for the forward local response normalization layer
53  forwardLRNlayer.input.setInput(layers.forward.data, tensorData)
54 
55  # Compute forward local response normalization layer results
56  forwardResult = forwardLRNlayer.compute()
57 
58  # Print the results of the forward local response normalization layer
59  printTensor(tensorData, "LRN layer input (first 5 rows):", 5)
60  printTensor(forwardResult.getResult(layers.forward.value), "LRN layer result (first 5 rows):", 5)
61  printTensor(forwardResult.getLayerData(layers.lrn.auxSmBeta), "LRN layer auxSmBeta (first 5 rows):", 5)
62 
63  # Get the size of forward local response normalization layer output
64  gDims = forwardResult.getResult(layers.forward.value).getDimensions()
65  tensorDataBack = HomogenTensor(gDims, TensorIface.doAllocate, 0.01)
66 
67  # Create an algorithm to compute backward local response normalization layer results using default method
68  backwardLRNlayer = lrn.backward.Batch()
69 
70  # Set input objects for the backward local response normalization layer
71  backwardLRNlayer.input.setInput(layers.backward.inputGradient, tensorDataBack)
72  backwardLRNlayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
73 
74  # Compute backward local response normalization layer results
75  backwardResult = backwardLRNlayer.compute()
76 
77  # Print the results of the backward local response normalization layer
78  printTensor(backwardResult.getResult(layers.backward.gradient), "LRN layer backpropagation result (first 5 rows):", 5)

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