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

dropout_layer_dense_batch.py

1 # file: dropout_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 dropout 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, readTensorFromCSV
39 
40 # Input data set parameters
41 datasetName = os.path.join("..", "data", "batch", "layer.csv")
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 dropout layer results using default method
49  dropoutLayerForward = layers.dropout.forward.Batch()
50 
51  # Set input objects for the forward dropout layer
52  dropoutLayerForward.input.setInput(layers.forward.data, tensorData)
53 
54  # Compute forward dropout layer results
55  forwardResult = dropoutLayerForward.compute()
56 
57  printTensor(forwardResult.getResult(layers.forward.value), "Forward dropout layer result (first 5 rows):", 5)
58  printTensor(forwardResult.getLayerData(layers.dropout.auxRetainMask), "Dropout layer retain mask (first 5 rows):", 5)
59 
60  # Get the size of forward dropout layer output
61  gDims = forwardResult.getResult(layers.forward.value).getDimensions()
62  tensorDataBack = HomogenTensor(gDims, TensorIface.doAllocate, 0.01)
63 
64  # Create an algorithm to compute backward dropout layer results using default method
65  dropoutLayerBackward = layers.dropout.backward.Batch()
66 
67  # Set input objects for the backward dropout layer
68  dropoutLayerBackward.input.setInput(layers.backward.inputGradient, tensorDataBack)
69  dropoutLayerBackward.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
70 
71  # Compute backward dropout layer results
72  backwardResult = dropoutLayerBackward.compute()
73 
74  # Print the results of the backward dropout layer
75  printTensor(backwardResult.getResult(layers.backward.gradient), "Backward dropout layer result (first 5 rows):", 5)

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