Python* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 1

trans_conv2d_layer_dense_batch.py

1 # file: trans_conv2d_layer_dense_batch.py
2 #===============================================================================
3 # Copyright 2014-2017 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 two-dimensional transposed convolution layer usage
45 # !
46 # !*****************************************************************************
47 
48 #
49 ## <a name="DAAL-EXAMPLE-PY-TRANS_CONV2D_LAYER_DENSE_BATCH"></a>
50 ## \example trans_conv2d_layer_dense_batch.py
51 #
52 
53 import os
54 import sys
55 
56 from daal.algorithms.neural_networks import layers
57 from daal.data_management import HomogenTensor, TensorIface
58 
59 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
60 if utils_folder not in sys.path:
61  sys.path.insert(0, utils_folder)
62 from utils import printTensor
63 
64 # Input data set name
65 datasetFileName = os.path.join("..", "data", "batch", "layer.csv")
66 
67 if __name__ == "__main__":
68 
69  # Create collection of dimension sizes of the input data tensor
70  inDims = [1, 2, 4, 4]
71  tensorData = HomogenTensor(inDims, TensorIface.doAllocate, 1.0)
72 
73  # Create an algorithm to compute forward two-dimensional transposed convolution layer results using default method
74  transposedConv2dLayerForward = layers.transposed_conv2d.forward.Batch()
75  transposedConv2dLayerForward.input.setInput(layers.forward.data, tensorData)
76 
77  # Compute forward two-dimensional transposed convolution layer results
78  forwardResult = transposedConv2dLayerForward.compute()
79 
80  printTensor(forwardResult.getResult(layers.forward.value), "Two-dimensional transposed convolution layer result (first 5 rows):", 5, 15)
81  printTensor(forwardResult.getLayerData(layers.transposed_conv2d.auxWeights),
82  "Two-dimensional transposed convolution layer weights (first 5 rows):", 5, 15)
83 
84  gDims = forwardResult.getResult(layers.forward.value).getDimensions()
85 
86  # Create input gradient tensor for backward two-dimensional transposed convolution layer
87  tensorDataBack = HomogenTensor(gDims, TensorIface.doAllocate, 0.01)
88 
89  # Create an algorithm to compute backward two-dimensional transposed convolution layer results using default method
90  transposedConv2dLayerBackward = layers.transposed_conv2d.backward.Batch()
91  transposedConv2dLayerBackward.input.setInput(layers.backward.inputGradient, tensorDataBack)
92  transposedConv2dLayerBackward.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
93 
94  # Compute backward two-dimensional transposed convolution layer results
95  backwardResult = transposedConv2dLayerBackward.compute()
96 
97  printTensor(backwardResult.getResult(layers.backward.gradient),
98  "Two-dimensional transposed convolution layer backpropagation gradient result (first 5 rows):", 5, 15)
99  printTensor(backwardResult.getResult(layers.backward.weightDerivatives),
100  "Two-dimensional transposed convolution layer backpropagation weightDerivative result (first 5 rows):", 5, 15)
101  printTensor(backwardResult.getResult(layers.backward.biasDerivatives),
102  "Two-dimensional transposed convolution layer backpropagation biasDerivative result (first 5 rows):", 5, 15)

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