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 printNumericTable, printTensor, readTensorFromCSV
40 datasetName = os.path.join(
"..",
"data",
"batch",
"layer.csv")
44 if __name__ ==
"__main__":
47 tensorData = readTensorFromCSV(datasetName)
48 tensorDataCollection = layers.LayerData()
50 for i
in range(nInputs):
51 tensorDataCollection[i] = tensorData
54 concatLayerForward = layers.concat.forward.Batch(concatDimension)
57 concatLayerForward.input.setInputLayerData(layers.forward.inputLayerData, tensorDataCollection)
60 forwardResult = concatLayerForward.compute()
62 printTensor(forwardResult.getResult(layers.forward.value),
"Forward concatenation layer result value (first 5 rows):", 5)
65 concatLayerBackward = layers.concat.backward.Batch(concatDimension)
68 concatLayerBackward.input.setInput(layers.backward.inputGradient, forwardResult.getResult(layers.forward.value))
69 concatLayerBackward.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
71 printNumericTable(forwardResult.getLayerData(layers.concat.auxInputDimensions),
"auxInputDimensions ")
74 backwardResult = concatLayerBackward.compute()
76 for i
in range(tensorDataCollection.size()):
77 printTensor(backwardResult.getResultLayerData(layers.backward.resultLayerData, i),
78 "Backward concatenation layer backward result (first 5 rows):", 5)