30 from daal.algorithms.neural_networks
import layers
31 from daal.data_management
import HomogenTensor, TensorIface
33 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
34 if utils_folder
not in sys.path:
35 sys.path.insert(0, utils_folder)
36 from utils
import printTensor, readTensorFromCSV
39 datasetFileName = os.path.join(
"..",
"data",
"batch",
"layer.csv")
42 if __name__ ==
"__main__":
45 data = readTensorFromCSV(datasetFileName)
47 printTensor(data,
"Forward batch normalization layer input (first 5 rows):", 5)
50 dataDims = data.getDimensions()
51 dimensionSize = dataDims[dimension]
54 dimensionSizes = [dimensionSize]
57 weights = HomogenTensor(dimensionSizes, TensorIface.doAllocate, 1.0)
58 biases = HomogenTensor(dimensionSizes, TensorIface.doAllocate, 2.0)
59 populationMean = HomogenTensor(dimensionSizes, TensorIface.doAllocate, 0.0)
60 populationVariance = HomogenTensor(dimensionSizes, TensorIface.doAllocate, 0.0)
63 forwardLayer = layers.batch_normalization.forward.Batch()
64 forwardLayer.parameter.dimension = dimension
65 forwardLayer.input.setInput(layers.forward.data, data)
66 forwardLayer.input.setInput(layers.forward.weights, weights)
67 forwardLayer.input.setInput(layers.forward.biases, biases)
68 forwardLayer.input.setInputLayerData(layers.batch_normalization.forward.populationMean, populationMean)
69 forwardLayer.input.setInputLayerData(layers.batch_normalization.forward.populationVariance, populationVariance)
72 forwardResult = forwardLayer.compute()
74 printTensor(forwardResult.getResult(layers.forward.value),
"Forward batch normalization layer result (first 5 rows):", 5)
75 printTensor(forwardResult.getLayerData(layers.batch_normalization.auxMean),
"Mini-batch mean (first 5 values):", 5)
76 printTensor(forwardResult.getLayerData(layers.batch_normalization.auxStandardDeviation),
"Mini-batch standard deviation (first 5 values):", 5)
77 printTensor(forwardResult.getLayerData(layers.batch_normalization.auxPopulationMean),
"Population mean (first 5 values):", 5)
78 printTensor(forwardResult.getLayerData(layers.batch_normalization.auxPopulationVariance),
"Population variance (first 5 values):", 5)
81 inputGradientTensor = HomogenTensor(dataDims, TensorIface.doAllocate, 10.0)
84 backwardLayer = layers.batch_normalization.backward.Batch()
85 backwardLayer.parameter.dimension = dimension
86 backwardLayer.input.setInput(layers.backward.inputGradient, inputGradientTensor)
87 backwardLayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
90 backwardResult = backwardLayer.compute()
92 printTensor(backwardResult.getResult(layers.backward.gradient),
"Backward batch normalization layer result (first 5 rows):", 5)
93 printTensor(backwardResult.getResult(layers.backward.weightDerivatives),
"Weight derivatives (first 5 values):", 5)
94 printTensor(backwardResult.getResult(layers.backward.biasDerivatives),
"Bias derivatives (first 5 values):", 5)