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

concat_layer_dense_batch.py

1 # file: concat_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 concatenation (concat) 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 
34 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
35 if utils_folder not in sys.path:
36  sys.path.insert(0, utils_folder)
37 from utils import printNumericTable, printTensor, readTensorFromCSV
38 
39 # Input data set parameters
40 datasetName = os.path.join("..", "data", "batch", "layer.csv")
41 concatDimension = 1
42 nInputs = 3
43 
44 if __name__ == "__main__":
45 
46  # Retrieve the input data
47  tensorData = readTensorFromCSV(datasetName)
48  tensorDataCollection = layers.LayerData()
49 
50  for i in range(nInputs):
51  tensorDataCollection[i] = tensorData
52 
53  # Create an algorithm to compute forward concatenation layer results using default method
54  concatLayerForward = layers.concat.forward.Batch(concatDimension)
55 
56  # Set input objects for the forward concatenation layer
57  concatLayerForward.input.setInputLayerData(layers.forward.inputLayerData, tensorDataCollection)
58 
59  # Compute forward concatenation layer results
60  forwardResult = concatLayerForward.compute()
61 
62  printTensor(forwardResult.getResult(layers.forward.value), "Forward concatenation layer result value (first 5 rows):", 5)
63 
64  # Create an algorithm to compute backward concatenation layer results using default method
65  concatLayerBackward = layers.concat.backward.Batch(concatDimension)
66 
67  # Set inputs for the backward concatenation layer
68  concatLayerBackward.input.setInput(layers.backward.inputGradient, forwardResult.getResult(layers.forward.value))
69  concatLayerBackward.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
70 
71  printNumericTable(forwardResult.getLayerData(layers.concat.auxInputDimensions), "auxInputDimensions ")
72 
73  # Compute backward concatenation layer results
74  backwardResult = concatLayerBackward.compute()
75 
76  for i in range(tensorDataCollection.size()):
77  printTensor(backwardResult.getResultLayerData(layers.backward.resultLayerData, i),
78  "Backward concatenation layer backward result (first 5 rows):", 5)

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