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 two-dimensional average pooling layer input (first 10 rows):", 10)
49 forwardLayer = layers.average_pooling2d.forward.Batch(nDim)
50 forwardLayer.input.setInput(layers.forward.data, data)
54 forwardResult = forwardLayer.compute()
56 printTensor(forwardResult.getResult(layers.forward.value),
57 "Forward two-dimensional average pooling layer result (first 5 rows):",
59 printNumericTable(forwardResult.getLayerData(layers.average_pooling2d.auxInputDimensions),
60 "Forward two-dimensional average pooling layer input dimensions:")
63 backwardLayer = layers.average_pooling2d.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))
69 backwardResult = backwardLayer.compute()
71 printTensor(backwardResult.getResult(layers.backward.gradient),
72 "Backward two-dimensional average pooling layer result (first 10 rows):",