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

split_layer_dense_batch.py

1 # file: split_layer_dense_batch.py
2 #===============================================================================
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40 #===============================================================================
41 
42 #
43 # ! Content:
44 # ! Python example of forward and backward split layer usage
45 # !
46 # !*****************************************************************************
47 
48 #
49 ## <a name="DAAL-EXAMPLE-PY-SPLIT_LAYER_BATCH"></a>
50 ## \example split_layer_dense_batch.py
51 #
52 
53 import os
54 import sys
55 
56 from daal.algorithms.neural_networks import layers
57 from daal.algorithms.neural_networks.layers import split
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, readTensorFromCSV
63 
64 # Input data set parameters
65 datasetName = os.path.join("..", "data", "batch", "layer.csv")
66 nOutputs = 3
67 nInputs = 3
68 
69 if __name__ == "__main__":
70 
71  # Read datasetFileName from a file and create a tensor to store input data
72  tensorData = readTensorFromCSV(datasetName)
73 
74  # Create an algorithm to compute forward split layer results using default method
75  splitLayerForward = split.forward.Batch()
76 
77  # Set parameters for the forward split layer
78  splitLayerForward.parameter.nOutputs = nOutputs
79  splitLayerForward.parameter.nInputs = nInputs
80 
81  # Set input objects for the forward split layer
82  splitLayerForward.input.setInput(layers.forward.data, tensorData)
83 
84  printTensor(tensorData, "Split layer input (first 5 rows):", 5)
85 
86  # Compute forward split layer results
87  forwardResult = splitLayerForward.compute()
88 
89  # Print the results of the forward split layer
90  for i in range(nOutputs):
91  printTensor(forwardResult.getResultLayerData(split.forward.valueCollection, i),
92  "Forward split layer result (first 5 rows):", 5)
93 
94  # Create an algorithm to compute backward split layer results using default method
95  splitLayerBackward = split.backward.Batch()
96 
97  # Set parameters for the backward split layer
98  splitLayerBackward.parameter.nOutputs = nOutputs
99  splitLayerBackward.parameter.nInputs = nInputs
100 
101  # Set input objects for the backward split layer
102  splitLayerBackward.input.setInputLayerData(split.backward.inputGradientCollection,
103  forwardResult.getResultLayerData(split.forward.valueCollection))
104 
105  # Compute backward split layer results
106  backwardResult = splitLayerBackward.compute()
107 
108  # Print the results of the backward split layer
109  printTensor(backwardResult.getResult(layers.backward.gradient), "Backward split layer result (first 5 rows):", 5)

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