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

initializers_dense_batch.py

1 # file: initializers_dense_batch.py
2 #===============================================================================
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40 #===============================================================================
41 
42 #
43 # ! Content:
44 # ! Python example of initializers
45 # !
46 # !*****************************************************************************
47 
48 #
49 ## <a name="DAAL-EXAMPLE-PY-INITIALIZERS_DENSE_BATCH"></a>
50 ## \example initializers_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 import initializers
58 from daal.data_management import HomogenTensor, TensorIface
59 
60 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
61 if utils_folder not in sys.path:
62  sys.path.insert(0, utils_folder)
63 from utils import printTensor
64 
65 if __name__ == "__main__":
66  # Create collection of dimension sizes of the input data tensor
67  inDims = [2, 1, 3, 4]
68  tensorData = HomogenTensor(inDims, TensorIface.doAllocate)
69 
70  # Fill tensor data using truncated gaussian initializer
71  # Create an algorithm to initialize data using default method
72  truncatedGaussInitializer = initializers.truncated_gaussian.Batch(0.0, 1.0)
73 
74  # Set input object and parameters for the truncated gaussian initializer
75  truncatedGaussInitializer.input.set(initializers.data, tensorData)
76 
77  # Compute truncated gaussian initializer
78  truncatedGaussInitializer.compute()
79 
80  # Print the results of the truncated gaussian initializer
81  printTensor(tensorData, "Data with truncated gaussian distribution:")
82 
83 
84  # Fill tensor data using gaussian initializer
85  # Create an algorithm to initialize data using default method
86  gaussInitializer = initializers.gaussian.Batch(1.0, 0.5)
87 
88  # Set input object and parameters for the gaussian initializer
89  gaussInitializer.input.set(initializers.data, tensorData)
90 
91  # Compute gaussian initializer
92  gaussInitializer.compute()
93 
94  # Print the results of the gaussian initializer
95  printTensor(tensorData, "Data with gaussian distribution:")
96 
97 
98  # Fill tensor data using uniform initializer
99  # Create an algorithm to initialize data using default method
100  uniformInitializer = initializers.uniform.Batch(-5.0, 5.0)
101 
102  # Set input object and parameters for the uniform initializer
103  uniformInitializer.input.set(initializers.data, tensorData)
104 
105  # Compute uniform initializer
106  uniformInitializer.compute()
107 
108  # Print the results of the uniform initializer
109  printTensor(tensorData, "Data with uniform distribution:")
110 
111 
112  # Fill layer weights using xavier initializer
113  # Create an algorithm to compute forward fully-connected layer results using default method
114  fullyconnectedLayerForward = layers.fullyconnected.forward.Batch(5)
115 
116  # Set input objects and parameter for the forward fully-connected layer
117  fullyconnectedLayerForward.input.setInput(layers.forward.data, tensorData)
118  fullyconnectedLayerForward.parameter.weightsInitializer = initializers.xavier.Batch()
119 
120  # Compute forward fully-connected layer results
121  fullyconnectedLayerForward.compute()
122 
123  # Print the results of the xavier initializer
124  printTensor(fullyconnectedLayerForward.input.getInput(layers.forward.weights), "Weights filled by xavier initializer:")

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