56 from daal.algorithms.neural_networks
import layers
58 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
59 if utils_folder
not in sys.path:
60 sys.path.insert(0, utils_folder)
61 from utils
import printTensor, readTensorFromCSV
64 datasetFileName = os.path.join(
"..",
"data",
"batch",
"layer.csv")
66 if __name__ ==
"__main__":
69 data = readTensorFromCSV(datasetFileName)
70 nDim = data.getNumberOfDimensions()
72 printTensor(data,
"Forward one-dimensional maximum pooling layer input (first 10 rows):", 10)
75 forwardLayer = layers.maximum_pooling1d.forward.Batch(nDim)
76 forwardLayer.input.setInput(layers.forward.data, data)
79 forwardResult = forwardLayer.compute()
82 printTensor(forwardResult.getResult(layers.forward.value),
"Forward one-dimensional maximum pooling layer result (first 5 rows):", 5)
83 printTensor(forwardResult.getLayerData(layers.maximum_pooling1d.auxSelectedIndices),
84 "Forward one-dimensional maximum pooling layer selected indices (first 5 rows):", 5)
87 backwardLayer = layers.maximum_pooling1d.backward.Batch(nDim)
90 backwardLayer.input.setInput(layers.backward.inputGradient, forwardResult.getResult(layers.forward.value))
91 backwardLayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
94 backwardResult = backwardLayer.compute()
97 printTensor(backwardResult.getResult(layers.backward.gradient),
98 "Backward one-dimensional maximum pooling layer result (first 10 rows):", 10)