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
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 maximum pooling layer input (first 10 rows):", 10)
49 forwardLayer = layers.maximum_pooling1d.forward.Batch(nDim)
50 forwardLayer.input.setInput(layers.forward.data, data)
53 forwardResult = forwardLayer.compute()
56 printTensor(forwardResult.getResult(layers.forward.value),
"Forward one-dimensional maximum pooling layer result (first 5 rows):", 5)
57 printTensor(forwardResult.getLayerData(layers.maximum_pooling1d.auxSelectedIndices),
58 "Forward one-dimensional maximum pooling layer selected indices (first 5 rows):", 5)
61 backwardLayer = layers.maximum_pooling1d.backward.Batch(nDim)
64 backwardLayer.input.setInput(layers.backward.inputGradient, forwardResult.getResult(layers.forward.value))
65 backwardLayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
68 backwardResult = backwardLayer.compute()
71 printTensor(backwardResult.getResult(layers.backward.gradient),
72 "Backward one-dimensional maximum pooling layer result (first 10 rows):", 10)