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
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 maximum pooling layer input (first 10 rows):", 10)
51 forwardLayer = layers.maximum_pooling1d.forward.Batch(nDim)
52 forwardLayer.input.setInput(layers.forward.data, data)
55 forwardResult = forwardLayer.compute()
58 printTensor(forwardResult.getResult(layers.forward.value),
"Forward one-dimensional maximum pooling layer result (first 5 rows):", 5)
59 printTensor(forwardResult.getLayerData(layers.maximum_pooling1d.auxSelectedIndices),
60 "Forward one-dimensional maximum pooling layer selected indices (first 5 rows):", 5)
63 backwardLayer = layers.maximum_pooling1d.backward.Batch(nDim)
66 backwardLayer.input.setInput(layers.backward.inputGradient, forwardResult.getResult(layers.forward.value))
67 backwardLayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
70 backwardResult = backwardLayer.compute()
73 printTensor(backwardResult.getResult(layers.backward.gradient),
74 "Backward one-dimensional maximum pooling layer result (first 10 rows):", 10)