32 from daal.algorithms.neural_networks
import layers
34 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
35 if utils_folder
not in sys.path:
36 sys.path.insert(0, utils_folder)
37 from utils
import printTensor, readTensorFromCSV,printNumericTable
40 datasetFileName = os.path.join(
"..",
"data",
"batch",
"layer.csv")
42 if __name__ ==
"__main__":
45 data = readTensorFromCSV(datasetFileName)
46 nDim = data.getNumberOfDimensions()
48 printTensor(data,
"Forward one-dimensional average pooling layer input (first 10 rows):", 10)
51 forwardLayer = layers.average_pooling1d.forward.Batch(nDim)
52 forwardLayer.input.setInput(layers.forward.data, data)
56 forwardResult = forwardLayer.compute()
59 printTensor(forwardResult.getResult(layers.forward.value),
60 "Forward one-dimensional average pooling layer result (first 5 rows):",
62 printNumericTable(forwardResult.getLayerData(layers.average_pooling1d.auxInputDimensions),
63 "Forward one-dimensional average pooling layer input dimensions:")
66 backwardLayer = layers.average_pooling1d.backward.Batch(nDim)
69 backwardLayer.input.setInput(layers.backward.inputGradient, forwardResult.getResult(layers.forward.value))
70 backwardLayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
74 backwardResult = backwardLayer.compute()
77 printTensor(backwardResult.getResult(layers.backward.gradient),
78 "Backward one-dimensional average pooling layer result (first 10 rows):",