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

softmax_layer_dense_batch.py

1 # file: softmax_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 softmax 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 softmax
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 dimension = 1
44 
45 if __name__ == "__main__":
46 
47  # Read datasetFileName from a file and create a tensor to store input data
48  tensorData = readTensorFromCSV(datasetName)
49 
50  # Create an algorithm to compute forward softmax layer results using default method
51  softmaxLayerForward = softmax.forward.Batch()
52  softmaxLayerForward.parameter.dimension = dimension
53 
54  # Set input objects for the forward softmax layer
55  softmaxLayerForward.input.setInput(layers.forward.data, tensorData)
56 
57  # Compute forward softmax layer results
58  forwardResult = softmaxLayerForward.compute()
59 
60  # Print the results of the forward softmax layer
61  printTensor(forwardResult.getResult(layers.forward.value), "Forward softmax layer result (first 5 rows):", 5)
62 
63  # Get the size of forward softmax 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 softmax layer results using default method
68  softmaxLayerBackward = softmax.backward.Batch()
69  softmaxLayerBackward.parameter.dimension = dimension
70 
71  # Set input objects for the backward softmax layer
72  softmaxLayerBackward.input.setInput(layers.backward.inputGradient, tensorDataBack)
73  softmaxLayerBackward.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
74 
75  # Compute backward softmax layer results
76  backwardResult = softmaxLayerBackward.compute()
77 
78  # Print the results of the backward softmax layer
79  printTensor(backwardResult.getResult(layers.backward.gradient), "Backward softmax layer result (first 5 rows):", 5)

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