Python* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 2

concat_layer_dense_batch.py

1 # file: concat_layer_dense_batch.py
2 #===============================================================================
3 # Copyright 2014-2018 Intel Corporation
4 # All Rights Reserved.
5 #
6 # If this software was obtained under the Intel Simplified Software License,
7 # the following terms apply:
8 #
9 # The source code, information and material ("Material") contained herein is
10 # owned by Intel Corporation or its suppliers or licensors, and title to such
11 # Material remains with Intel Corporation or its suppliers or licensors. The
12 # Material contains proprietary information of Intel or its suppliers and
13 # licensors. The Material is protected by worldwide copyright laws and treaty
14 # provisions. No part of the Material may be used, copied, reproduced,
15 # modified, published, uploaded, posted, transmitted, distributed or disclosed
16 # in any way without Intel's prior express written permission. No license under
17 # any patent, copyright or other intellectual property rights in the Material
18 # is granted to or conferred upon you, either expressly, by implication,
19 # inducement, estoppel or otherwise. Any license under such intellectual
20 # property rights must be express and approved by Intel in writing.
21 #
22 # Unless otherwise agreed by Intel in writing, you may not remove or alter this
23 # notice or any other notice embedded in Materials by Intel or Intel's
24 # suppliers or licensors in any way.
25 #
26 #
27 # If this software was obtained under the Apache License, Version 2.0 (the
28 # "License"), the following terms apply:
29 #
30 # You may not use this file except in compliance with the License. You may
31 # obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
32 #
33 #
34 # Unless required by applicable law or agreed to in writing, software
35 # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
36 # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
37 #
38 # See the License for the specific language governing permissions and
39 # limitations under the License.
40 #===============================================================================
41 
42 #
43 # ! Content:
44 # ! Python example of forward and backward concatenation (concat) layer usage
45 # !
46 # !*****************************************************************************
47 
48 #
49 
50 
51 #
52 
53 import os
54 import sys
55 
56 from daal.algorithms.neural_networks import layers
57 
58 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
59 if utils_folder not in sys.path:
60  sys.path.insert(0, utils_folder)
61 from utils import printNumericTable, printTensor, readTensorFromCSV
62 
63 # Input data set parameters
64 datasetName = os.path.join("..", "data", "batch", "layer.csv")
65 concatDimension = 1
66 nInputs = 3
67 
68 if __name__ == "__main__":
69 
70  # Retrieve the input data
71  tensorData = readTensorFromCSV(datasetName)
72  tensorDataCollection = layers.LayerData()
73 
74  for i in range(nInputs):
75  tensorDataCollection[i] = tensorData
76 
77  # Create an algorithm to compute forward concatenation layer results using default method
78  concatLayerForward = layers.concat.forward.Batch(concatDimension)
79 
80  # Set input objects for the forward concatenation layer
81  concatLayerForward.input.setInputLayerData(layers.forward.inputLayerData, tensorDataCollection)
82 
83  # Compute forward concatenation layer results
84  forwardResult = concatLayerForward.compute()
85 
86  printTensor(forwardResult.getResult(layers.forward.value), "Forward concatenation layer result value (first 5 rows):", 5)
87 
88  # Create an algorithm to compute backward concatenation layer results using default method
89  concatLayerBackward = layers.concat.backward.Batch(concatDimension)
90 
91  # Set inputs for the backward concatenation layer
92  concatLayerBackward.input.setInput(layers.backward.inputGradient, forwardResult.getResult(layers.forward.value))
93  concatLayerBackward.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
94 
95  printNumericTable(forwardResult.getLayerData(layers.concat.auxInputDimensions), "auxInputDimensions ")
96 
97  # Compute backward concatenation layer results
98  backwardResult = concatLayerBackward.compute()
99 
100  for i in range(tensorDataCollection.size()):
101  printTensor(backwardResult.getResultLayerData(layers.backward.resultLayerData, i),
102  "Backward concatenation layer backward result (first 5 rows):", 5)

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