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 printNumericTable, printTensor, readTensorFromCSV
64 datasetName = os.path.join(
"..",
"data",
"batch",
"layer.csv")
68 if __name__ ==
"__main__":
71 tensorData = readTensorFromCSV(datasetName)
72 tensorDataCollection = layers.LayerData()
74 for i
in range(nInputs):
75 tensorDataCollection[i] = tensorData
78 concatLayerForward = layers.concat.forward.Batch(concatDimension)
81 concatLayerForward.input.setInputLayerData(layers.forward.inputLayerData, tensorDataCollection)
84 forwardResult = concatLayerForward.compute()
86 printTensor(forwardResult.getResult(layers.forward.value),
"Forward concatenation layer result value (first 5 rows):", 5)
89 concatLayerBackward = layers.concat.backward.Batch(concatDimension)
92 concatLayerBackward.input.setInput(layers.backward.inputGradient, forwardResult.getResult(layers.forward.value))
93 concatLayerBackward.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
95 printNumericTable(forwardResult.getLayerData(layers.concat.auxInputDimensions),
"auxInputDimensions ")
98 backwardResult = concatLayerBackward.compute()
100 for i
in range(tensorDataCollection.size()):
101 printTensor(backwardResult.getResultLayerData(layers.backward.resultLayerData, i),
102 "Backward concatenation layer backward result (first 5 rows):", 5)