Python* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 4

reshape_layer_dense_batch.py

1 # file: reshape_layer_dense_batch.py
2 #===============================================================================
3 # Copyright 2014-2019 Intel Corporation.
4 #
5 # This software and the related documents are Intel copyrighted materials, and
6 # your use of them is governed by the express license under which they were
7 # provided to you (License). Unless the License provides otherwise, you may not
8 # use, modify, copy, publish, distribute, disclose or transmit this software or
9 # the related documents without Intel's prior written permission.
10 #
11 # This software and the related documents are provided as is, with no express
12 # or implied warranties, other than those that are expressly stated in the
13 # License.
14 #===============================================================================
15 
16 #
17 # ! Content:
18 # ! Python example of forward and backward reshape layer usage
19 # !
20 # !*****************************************************************************
21 
22 #
23 
24 
25 #
26 
27 import os
28 import sys
29 
30 from daal.algorithms.neural_networks import layers
31 from daal.algorithms.neural_networks.layers import reshape
32 from daal.data_management import HomogenTensor, Tensor
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 printTensor, readTensorFromCSV
38 
39 # Input data set parameters
40 datasetName = os.path.join("..", "data", "batch", "layer.csv")
41 
42 if __name__ == "__main__":
43 
44  # Read datasetFileName from a file and create a tensor to store input data
45  tensorData = readTensorFromCSV(datasetName)
46 
47  reshapeDimensions = [-1, 5]
48 
49  # Create an algorithm to compute forward reshape layer results using default method
50  reshapeLayerForward = reshape.forward.Batch(reshapeDimensions)
51 
52  # Set input objects for the forward reshape layer
53  reshapeLayerForward.input.setInput(layers.forward.data, tensorData)
54 
55  printTensor(tensorData, "Forward reshape layer input (first 5 rows):", 5)
56 
57  # Compute forward reshape layer results
58  forwardResult = reshapeLayerForward.compute()
59 
60  # Print the results of the forward reshape layer
61  printTensor(forwardResult.getResult(layers.forward.value), "Forward reshape layer result (first 5 rows):", 5)
62 
63  # Get the size of forward reshape layer output
64  gDims = forwardResult.getResult(layers.forward.value).getDimensions()
65  tensorDataBack = HomogenTensor(gDims, Tensor.doAllocate, 0.01)
66 
67  # Create an algorithm to compute backward reshape layer results using default method
68  reshapeLayerBackward = reshape.backward.Batch()
69 
70  # Set input objects for the backward reshape layer
71  reshapeLayerBackward.input.setInput(layers.backward.inputGradient, tensorDataBack)
72  reshapeLayerBackward.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
73 
74  # Compute backward reshape layer results
75  backwardResult = reshapeLayerBackward.compute()
76 
77  # Print the results of the backward reshape layer
78  printTensor(backwardResult.getResult(layers.backward.gradient), "Backward reshape layer result (first 5 rows):", 5)

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