30 from daal.algorithms.neural_networks
import layers
32 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
33 if utils_folder
not in sys.path:
34 sys.path.insert(0, utils_folder)
35 from utils
import printTensor, readTensorFromCSV,printNumericTable
38 datasetFileName = os.path.join(
"..",
"data",
"batch",
"layer.csv")
40 if __name__ ==
"__main__":
43 data = readTensorFromCSV(datasetFileName)
44 nDim = data.getNumberOfDimensions()
46 printTensor(data,
"Forward one-dimensional average pooling layer input (first 10 rows):", 10)
49 forwardLayer = layers.average_pooling1d.forward.Batch(nDim)
50 forwardLayer.input.setInput(layers.forward.data, data)
54 forwardResult = forwardLayer.compute()
57 printTensor(forwardResult.getResult(layers.forward.value),
58 "Forward one-dimensional average pooling layer result (first 5 rows):",
60 printNumericTable(forwardResult.getLayerData(layers.average_pooling1d.auxInputDimensions),
61 "Forward one-dimensional average pooling layer input dimensions:")
64 backwardLayer = layers.average_pooling1d.backward.Batch(nDim)
67 backwardLayer.input.setInput(layers.backward.inputGradient, forwardResult.getResult(layers.forward.value))
68 backwardLayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
72 backwardResult = backwardLayer.compute()
75 printTensor(backwardResult.getResult(layers.backward.gradient),
76 "Backward one-dimensional average pooling layer result (first 10 rows):",