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 in the distributed processing mode
 # !*****************************************************************************
 ## \example
 import os
 import sys
 import numpy as np
 from daal import step1Local, step2Master
 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 prediction, training
 from daal.data_management import NumericTable, HomogenNumericTable, readOnly, SubtensorDescriptor, HomogenTensor
 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
 trainDatasetFileNames = [
     os.path.join("..", "data", "distributed", "neural_network_train_dense_1.csv"),
     os.path.join("..", "data", "distributed", "neural_network_train_dense_2.csv"),
     os.path.join("..", "data", "distributed", "neural_network_train_dense_3.csv"),
     os.path.join("..", "data", "distributed", "neural_network_train_dense_4.csv")
 trainGroundTruthFileNames = [
     os.path.join("..", "data", "distributed", "neural_network_train_ground_truth_1.csv"),
     os.path.join("..", "data", "distributed", "neural_network_train_ground_truth_2.csv"),
     os.path.join("..", "data", "distributed", "neural_network_train_ground_truth_3.csv"),
     os.path.join("..", "data", "distributed", "neural_network_train_ground_truth_4.csv")
 testDatasetFile     = os.path.join("..", "data", "batch", "neural_network_test.csv")
 testGroundTruthFile = os.path.join("..", "data", "batch", "neural_network_test_ground_truth.csv")
 nNodes = 4
 batchSize = 100
 batchSizeLocal = int(batchSize / nNodes)
 def configureNet():
     m2 = 40
     # Create layers of the neural network
     # Create fully-connected layer and initialize layer parameters
     fullyConnectedLayer1 = layers.fullyconnected.Batch(20)
     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(m2)
     fullyConnectedLayer2.parameter.weightsInitializer = initializers.uniform.Batch(0.5, 1)
     fullyConnectedLayer2.parameter.biasesInitializer = initializers.uniform.Batch(0.5, 1)
     # Create fully-connected layer and initialize layer parameters
     fullyConnectedLayer3 = layers.fullyconnected.Batch(2)
     fullyConnectedLayer3.parameter.weightsInitializer = initializers.uniform.Batch(-0.005, 0.005)
     fullyConnectedLayer3.parameter.biasesInitializer = initializers.uniform.Batch(0, 1)
     # Create softmax layer and initialize layer parameters
     softmaxCrossEntropyLayer =  layers.loss.softmax_cross.Batch()
     # Create topology of the neural network
     topology = training.Topology()
     # Add layers to the topology of the neural network
     fc1 = topology.add(fullyConnectedLayer1)
     fc2 = topology.add(fullyConnectedLayer2)
     fc3 = topology.add(fullyConnectedLayer3)
     sm  = topology.add(softmaxCrossEntropyLayer)
     return topology
 def getNextSubtensor(inputTensor, startPos, nElements):
     dims = inputTensor.getDimensions()
     dims[0] = nElements
     subtensorBlock = SubtensorDescriptor(ntype=np.float32)
     inputTensor.getSubtensor([], startPos, nElements, readOnly, subtensorBlock)
     subtensorData = np.array(subtensorBlock.getArray(), dtype=np.float32)
     return HomogenTensor(subtensorData, ntype=np.float32)
 def initializeNetwork():
     trainingData = [None] * nNodes
     trainingGroundTruth = [None] * nNodes
     # Read training data set from a .csv file and create tensors to store input data
     for node in range(nNodes):
         trainingData[node] = readTensorFromCSV(trainDatasetFileNames[node])
         trainingGroundTruth[node] = readTensorFromCSV(trainGroundTruthFileNames[node], True)
     sampleSize = trainingData[0].getDimensions()
     sampleSize[0] = batchSizeLocal
     # Create stochastic gradient descent (SGD) optimization solver algorithm
     sgdAlgorithm = optimization_solver.sgd.Batch(fptype=np.float32)
     sgdAlgorithm.parameter.batchSize = batchSizeLocal
     # Configure the neural network
     topologyMaster = configureNet()
     net = training.Distributed(step2Master, sgdAlgorithm)
     net.parameter.batchSize = batchSizeLocal
     # Initialize the neural network on master node
     net.initialize(sampleSize, topologyMaster)
     topology = [None] * nNodes
     netLocal = [None] * nNodes
     for node in range(nNodes):
         # Configure the neural network
         topology[node] = configureNet()
         # Pass a model from master node to the algorithms on local nodes
         trainingModel = training.Model()
         trainingModel.initialize_Float32(sampleSize, topology[node])
         netLocal[node] = training.Distributed(step1Local)
         netLocal[node].input.setStep1LocalInput(training.inputModel, trainingModel)
         # Set the batch size for the neural network training
         netLocal[node].parameter.batchSize = batchSizeLocal
     return (net, netLocal, trainingData, trainingGroundTruth)
 def trainModel(net, netLocal, trainingData, trainingGroundTruth):
     # Create stochastic gradient descent (SGD) optimization solver algorithm
     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 optimization solver for the neural network training
     net.parameter.optimizationSolver = sgdAlgorithm
     # Run the neural network training
     nSamples = trainingData[0].getDimensions()[0]
     for i in range(0, nSamples - batchSizeLocal + 1, batchSizeLocal):
         # Compute weights and biases for the batch of inputs on local nodes
         for node in range(nNodes):
             # Pass a training data set and dependent values to the algorithm
             netLocal[node].input.setInput(, getNextSubtensor(trainingData[node], i, batchSizeLocal))
             netLocal[node].input.setInput(training.groundTruth, getNextSubtensor(trainingGroundTruth[node], i, batchSizeLocal))
             # Compute weights and biases on local node
             pres = netLocal[node].compute()
             # Pass computed weights and biases to the master algorithm
             net.input.add(training.partialResults, node, pres)
         # Update weights and biases on master node
         wb = net.getPartialResult().get(training.resultFromMaster).get(training.model).getWeightsAndBiases()
         # Update weights and biases on local nodes
         for node in range(nNodes):
     # Finalize neural network training on the master node
     res = net.finalizeCompute()
     # Retrieve training and prediction models of the neural network
     return res.get(training.model).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()
     # Set the batch size for the neural network prediction
     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 result
     return net.compute()
 def printResults(testGroundTruthFile, 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)
 def main():
     init = initializeNetwork()
     predictionModel = trainModel(*init)
     predictionResult = testModel(predictionModel)
     printResults(testGroundTruthFile, predictionResult)
 if __name__ == "__main__":
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