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

sgd_moment_opt_res_dense_batch.py

1 # file: sgd_moment_opt_res_dense_batch.py
2 #===============================================================================
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40 #===============================================================================
41 
42 #
43 # ! Content:
44 # ! Python example of the SGD algorithm
45 # !*****************************************************************************
46 
47 #
48 ## <a name="DAAL-EXAMPLE-PY-SGD_MOMENT_OPT_RES_DENSE_BATCH"></a>
49 ## \example sgd_moment_opt_res_dense_batch.py
50 #
51 
52 import os
53 import sys
54 
55 import numpy as np
56 
57 import daal.algorithms.optimization_solver as optimization_solver
58 import daal.algorithms.optimization_solver.mse
59 import daal.algorithms.optimization_solver.sgd
60 import daal.algorithms.optimization_solver.iterative_solver
61 
62 from daal.data_management import (
63  DataSourceIface, FileDataSource, HomogenNumericTable, MergedNumericTable, NumericTableIface
64 )
65 
66 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
67 if utils_folder not in sys.path:
68  sys.path.insert(0, utils_folder)
69 from utils import printNumericTable
70 
71 datasetFileName = os.path.join('..', 'data', 'batch', 'mse.csv')
72 
73 nFeatures = 3
74 accuracyThreshold = 0.0000001
75 halfNIterations = 200
76 nIterations = halfNIterations * 2
77 batchSize = 4
78 learningRate = 0.5
79 
80 startPoint = np.array([[8], [2], [1], [4]], dtype=np.float64)
81 
82 if __name__ == "__main__":
83 
84  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
85  dataSource = FileDataSource(datasetFileName,
86  DataSourceIface.notAllocateNumericTable,
87  DataSourceIface.doDictionaryFromContext)
88 
89  # Create Numeric Tables for data and values for dependent variable
90  data = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
91  dependentVariables = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
92  mergedData = MergedNumericTable(data, dependentVariables)
93 
94  # Retrieve the data from the input file
95  dataSource.loadDataBlock(mergedData)
96 
97  nVectors = data.getNumberOfRows()
98 
99  mseObjectiveFunction = optimization_solver.mse.Batch(nVectors)
100  mseObjectiveFunction.input.set(optimization_solver.mse.data, data)
101  mseObjectiveFunction.input.set(optimization_solver.mse.dependentVariables, dependentVariables)
102 
103  # Create objects to compute the SGD result using the default method
104  sgdAlgorithm = optimization_solver.sgd.Batch(mseObjectiveFunction, method=optimization_solver.sgd.momentum)
105 
106  # Set input objects for the the SGD algorithm
107  sgdAlgorithm.input.setInput(optimization_solver.iterative_solver.inputArgument, HomogenNumericTable(startPoint))
108  sgdAlgorithm.parameter.learningRate = HomogenNumericTable(1, 1, NumericTableIface.doAllocate, learningRate)
109  sgdAlgorithm.parameter.nIterations = halfNIterations
110  sgdAlgorithm.parameter.accuracyThreshold = accuracyThreshold
111  sgdAlgorithm.parameter.batchSize = batchSize
112  sgdAlgorithm.parameter.optionalResultRequired = True
113 
114  # Compute the SGD result
115  # Result class from daal.algorithms.optimization_solver.iterative_solver
116  res = sgdAlgorithm.compute()
117 
118  # Print computed the SGD result
119  printNumericTable(res.getResult(optimization_solver.iterative_solver.minimum), "Minimum after first compute():")
120  printNumericTable(res.getResult(optimization_solver.iterative_solver.nIterations), "Number of iterations performed:")
121 
122  sgdAlgorithm.input.setInput(optimization_solver.iterative_solver.inputArgument, res.getResult(optimization_solver.iterative_solver.minimum))
123  sgdAlgorithm.input.setInput(optimization_solver.iterative_solver.optionalArgument, res.getResult(optimization_solver.iterative_solver.optionalResult))
124 
125  res = sgdAlgorithm.compute()
126 
127  printNumericTable(res.getResult(optimization_solver.iterative_solver.minimum), "Minimum after second compute():")
128  printNumericTable(res.getResult(optimization_solver.iterative_solver.nIterations), "Number of iterations performed:")

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