57 from daal.algorithms.neural_networks
import initializers
58 from daal.algorithms.neural_networks
import layers
59 from daal.algorithms
import optimization_solver
60 from daal.algorithms.neural_networks
import training, prediction
61 from daal.data_management
import NumericTable, HomogenNumericTable
63 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
64 if utils_folder
not in sys.path:
65 sys.path.insert(0, utils_folder)
66 from utils
import printTensors, readTensorFromCSV
69 trainDatasetFile = os.path.join(
"..",
"data",
"batch",
"neural_network_train.csv")
70 trainGroundTruthFile = os.path.join(
"..",
"data",
"batch",
"neural_network_train_ground_truth.csv")
71 testDatasetFile = os.path.join(
"..",
"data",
"batch",
"neural_network_test.csv")
72 testGroundTruthFile = os.path.join(
"..",
"data",
"batch",
"neural_network_test_ground_truth.csv")
83 fullyConnectedLayer1 = layers.fullyconnected.Batch(5)
84 fullyConnectedLayer1.parameter.weightsInitializer = initializers.uniform.Batch(-0.001, 0.001)
85 fullyConnectedLayer1.parameter.biasesInitializer = initializers.uniform.Batch(0, 0.5)
88 fullyConnectedLayer2 = layers.fullyconnected.Batch(2)
89 fullyConnectedLayer2.parameter.weightsInitializer = initializers.uniform.Batch(0.5, 1)
90 fullyConnectedLayer2.parameter.biasesInitializer = initializers.uniform.Batch(0.5, 1)
93 softmaxCrossEntropyLayer = layers.loss.softmax_cross.Batch()
96 topology = training.Topology()
99 topology.push_back(fullyConnectedLayer1)
100 topology.push_back(fullyConnectedLayer2)
101 topology.push_back(softmaxCrossEntropyLayer)
102 topology.get(fc1).addNext(fc2)
103 topology.get(fc2).addNext(sm1)
109 trainingData = readTensorFromCSV(trainDatasetFile)
110 trainingGroundTruth = readTensorFromCSV(trainGroundTruthFile,
True)
112 sgdAlgorithm = optimization_solver.sgd.Batch(fptype=np.float32)
116 sgdAlgorithm.parameter.learningRateSequence = HomogenNumericTable(1, 1, NumericTable.doAllocate, learningRate)
118 sgdAlgorithm.parameter.batchSize = batchSize
119 sgdAlgorithm.parameter.nIterations = int(trainingData.getDimensionSize(0) / sgdAlgorithm.parameter.batchSize)
122 net = training.Batch(sgdAlgorithm)
124 sampleSize = trainingData.getDimensions()
125 sampleSize[0] = batchSize
128 topology = configureNet()
129 net.initialize(sampleSize, topology)
132 net.input.setInput(training.data, trainingData)
133 net.input.setInput(training.groundTruth, trainingGroundTruth)
136 trainingModel = net.compute().get(training.model)
138 return trainingModel.getPredictionModel_Float32()
141 def testModel(predictionModel):
143 predictionData = readTensorFromCSV(testDatasetFile)
146 net = prediction.Batch()
148 net.parameter.batchSize = predictionData.getDimensionSize(0)
151 net.input.setModelInput(prediction.model, predictionModel)
152 net.input.setTensorInput(prediction.data, predictionData)
159 def printResults(predictionResult):
161 predictionGroundTruth = readTensorFromCSV(testGroundTruthFile)
163 printTensors(predictionGroundTruth, predictionResult.getResult(prediction.prediction),
164 "Ground truth",
"Neural network predictions: each class probability",
165 "Neural network classification results (first 20 observations):", 20)
169 if __name__ ==
"__main__":
171 predictionModel = trainModel()
173 predictionResult = testModel(predictionModel)
175 printResults(predictionResult)