32 from daal.algorithms.neural_networks
import layers
33 from daal.data_management
import HomogenTensor, TensorIface
35 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
36 if utils_folder
not in sys.path:
37 sys.path.insert(0, utils_folder)
38 from utils
import printTensor, readTensorFromCSV
41 datasetFileName = os.path.join(
"..",
"data",
"batch",
"layer.csv")
44 if __name__ ==
"__main__":
47 data = readTensorFromCSV(datasetFileName)
49 printTensor(data,
"Forward batch normalization layer input (first 5 rows):", 5)
52 dataDims = data.getDimensions()
53 dimensionSize = dataDims[dimension]
56 dimensionSizes = [dimensionSize]
59 weights = HomogenTensor(dimensionSizes, TensorIface.doAllocate, 1.0)
60 biases = HomogenTensor(dimensionSizes, TensorIface.doAllocate, 2.0)
61 populationMean = HomogenTensor(dimensionSizes, TensorIface.doAllocate, 0.0)
62 populationVariance = HomogenTensor(dimensionSizes, TensorIface.doAllocate, 0.0)
65 forwardLayer = layers.batch_normalization.forward.Batch()
66 forwardLayer.parameter.dimension = dimension
67 forwardLayer.input.setInput(layers.forward.data, data)
68 forwardLayer.input.setInput(layers.forward.weights, weights)
69 forwardLayer.input.setInput(layers.forward.biases, biases)
70 forwardLayer.input.setInputLayerData(layers.batch_normalization.forward.populationMean, populationMean)
71 forwardLayer.input.setInputLayerData(layers.batch_normalization.forward.populationVariance, populationVariance)
74 forwardResult = forwardLayer.compute()
76 printTensor(forwardResult.getResult(layers.forward.value),
"Forward batch normalization layer result (first 5 rows):", 5)
77 printTensor(forwardResult.getLayerData(layers.batch_normalization.auxMean),
"Mini-batch mean (first 5 values):", 5)
78 printTensor(forwardResult.getLayerData(layers.batch_normalization.auxStandardDeviation),
"Mini-batch standard deviation (first 5 values):", 5)
79 printTensor(forwardResult.getLayerData(layers.batch_normalization.auxPopulationMean),
"Population mean (first 5 values):", 5)
80 printTensor(forwardResult.getLayerData(layers.batch_normalization.auxPopulationVariance),
"Population variance (first 5 values):", 5)
83 inputGradientTensor = HomogenTensor(dataDims, TensorIface.doAllocate, 10.0)
86 backwardLayer = layers.batch_normalization.backward.Batch()
87 backwardLayer.parameter.dimension = dimension
88 backwardLayer.input.setInput(layers.backward.inputGradient, inputGradientTensor)
89 backwardLayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
92 backwardResult = backwardLayer.compute()
94 printTensor(backwardResult.getResult(layers.backward.gradient),
"Backward batch normalization layer result (first 5 rows):", 5)
95 printTensor(backwardResult.getResult(layers.backward.weightDerivatives),
"Weight derivatives (first 5 values):", 5)
96 printTensor(backwardResult.getResult(layers.backward.biasDerivatives),
"Bias derivatives (first 5 values):", 5)