Deprecation Notice: With the introduction of daal4py, a package that supersedes PyDAAL, Intel is deprecating PyDAAL and will discontinue support starting with Intel® DAAL 2021 and Intel® Distribution for Python 2021. Until then Intel will continue to provide compatible pyDAAL pip and conda packages for newer releases of Intel DAAL and make it available in open source. However, Intel will not add the new features of Intel DAAL to pyDAAL. Intel recommends developers switch to and use daal4py.

Note: To find daal4py examples, refer to daal4py documentation or browse github repository.

 # file:
 # Copyright 2014-2019 Intel Corporation.
 # This software and the related documents are Intel copyrighted  materials,  and
 # your use of  them is  governed by the  express license  under which  they were
 # provided to you (License).  Unless the License provides otherwise, you may not
 # use, modify, copy, publish, distribute,  disclose or transmit this software or
 # the related documents without Intel's prior written permission.
 # This software and the related documents  are provided as  is,  with no express
 # or implied  warranties,  other  than those  that are  expressly stated  in the
 # License.
 # !  Content:
 # !    Python example of neural network training and scoring
 # !*****************************************************************************
 ## \example
 import os
 import sys
 import numpy as np
 from daal.algorithms.neural_networks import initializers
 from daal.algorithms.neural_networks import layers
 from daal.algorithms import optimization_solver
 from daal.algorithms.neural_networks import training, prediction
 from daal.data_management import NumericTable, HomogenNumericTable
 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
 if utils_folder not in sys.path:
     sys.path.insert(0, utils_folder)
 from utils import printTensors, readTensorFromCSV
 # Input data set parameters
 trainDatasetFile = os.path.join("..", "data", "batch", "neural_network_train.csv")
 trainGroundTruthFile = os.path.join("..", "data", "batch", "neural_network_train_ground_truth.csv")
 testDatasetFile = os.path.join("..", "data", "batch", "neural_network_test.csv")
 testGroundTruthFile = os.path.join("..", "data", "batch", "neural_network_test_ground_truth.csv")
 fc1 = 0
 fc2 = 1
 sm1 = 2
 batchSize = 10
 def configureNet():
     # Create layers of the neural network
     # Create fully-connected layer and initialize layer parameters
     fullyConnectedLayer1 = layers.fullyconnected.Batch(5)
     fullyConnectedLayer1.parameter.weightsInitializer = initializers.uniform.Batch(-0.001, 0.001)
     fullyConnectedLayer1.parameter.biasesInitializer = initializers.uniform.Batch(0, 0.5)
     # Create fully-connected layer and initialize layer parameters
     fullyConnectedLayer2 = layers.fullyconnected.Batch(2)
     fullyConnectedLayer2.parameter.weightsInitializer = initializers.uniform.Batch(0.5, 1)
     fullyConnectedLayer2.parameter.biasesInitializer = initializers.uniform.Batch(0.5, 1)
     # Create softmax layer and initialize layer parameters
     softmaxCrossEntropyLayer = layers.loss.softmax_cross.Batch()
     # Create configuration of the neural network with layers
     topology = training.Topology()
     # Add layers to the topology of the neural network
     return topology
 def trainModel():
     # Read training data set from a .csv file and create a tensor to store input data
     trainingData = readTensorFromCSV(trainDatasetFile)
     trainingGroundTruth = readTensorFromCSV(trainGroundTruthFile, True)
     sgdAlgorithm = optimization_solver.sgd.Batch(fptype=np.float32)
     # Set learning rate for the optimization solver used in the neural network
     learningRate = 0.001
     sgdAlgorithm.parameter.learningRateSequence = HomogenNumericTable(1, 1, NumericTable.doAllocate, learningRate)
     # Set the batch size for the neural network training
     sgdAlgorithm.parameter.batchSize = batchSize
     sgdAlgorithm.parameter.nIterations = int(trainingData.getDimensionSize(0) / sgdAlgorithm.parameter.batchSize)
     # Create an algorithm to train neural network
     net = training.Batch(sgdAlgorithm)
     sampleSize = trainingData.getDimensions()
     sampleSize[0] = batchSize
     # Configure the neural network
     topology = configureNet()
     net.initialize(sampleSize, topology)
     # Pass a training data set and dependent values to the algorithm
     net.input.setInput(, trainingData)
     net.input.setInput(training.groundTruth, trainingGroundTruth)
     # Run the neural network training and retrieve training model
     trainingModel = net.compute().get(training.model)
     # return prediction model
     return trainingModel.getPredictionModel_Float32()
 def testModel(predictionModel):
     # Read testing data set from a .csv file and create a tensor to store input data
     predictionData = readTensorFromCSV(testDatasetFile)
     # Create an algorithm to compute the neural network predictions
     net = prediction.Batch()
     net.parameter.batchSize = predictionData.getDimensionSize(0)
     # Set input objects for the prediction neural network
     net.input.setModelInput(prediction.model, predictionModel)
     net.input.setTensorInput(, predictionData)
     # Run the neural network prediction
     # and return results of the neural network prediction
     return net.compute()
 def printResults(predictionResult):
     # Read testing ground truth from a .csv file and create a tensor to store the data
     predictionGroundTruth = readTensorFromCSV(testGroundTruthFile)
     printTensors(predictionGroundTruth, predictionResult.getResult(prediction.prediction),
                  "Ground truth", "Neural network predictions: each class probability",
                  "Neural network classification results (first 20 observations):", 20)
 topology = ""
 if __name__ == "__main__":
     predictionModel = trainModel()
     predictionResult = testModel(predictionModel)
For more complete information about compiler optimizations, see our Optimization Notice.
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