32 from daal.algorithms.neural_networks
import layers
33 from daal.data_management
import HomogenTensor, TensorIface
35 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
36 if utils_folder
not in sys.path:
37 sys.path.insert(0, utils_folder)
38 from utils
import printTensor
41 datasetFileName = os.path.join(
"..",
"data",
"batch",
"layer.csv")
43 if __name__ ==
"__main__":
46 inDims = [2, 1, 16, 16]
47 tensorData = HomogenTensor(inDims, TensorIface.doAllocate, 1.0)
50 convolution2dLayerForward = layers.convolution2d.forward.Batch()
51 convolution2dLayerForward.input.setInput(layers.forward.data, tensorData)
54 forwardResult = convolution2dLayerForward.compute()
56 printTensor(forwardResult.getResult(layers.forward.value),
"Two-dimensional convolution layer result (first 5 rows):", 5, 15)
57 printTensor(forwardResult.getLayerData(layers.convolution2d.auxWeights),
58 "Two-dimensional convolution layer weights (first 5 rows):", 5, 15)
60 gDims = forwardResult.getResult(layers.forward.value).getDimensions()
63 tensorDataBack = HomogenTensor(gDims, TensorIface.doAllocate, 0.01)
66 convolution2dLayerBackward = layers.convolution2d.backward.Batch()
67 convolution2dLayerBackward.input.setInput(layers.backward.inputGradient, tensorDataBack)
68 convolution2dLayerBackward.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
71 backwardResult = convolution2dLayerBackward.compute()
73 printTensor(backwardResult.getResult(layers.backward.gradient),
74 "Two-dimensional convolution layer backpropagation gradient result (first 5 rows):", 5, 15)
75 printTensor(backwardResult.getResult(layers.backward.weightDerivatives),
76 "Two-dimensional convolution layer backpropagation weightDerivative result (first 5 rows):", 5, 15)
77 printTensor(backwardResult.getResult(layers.backward.biasDerivatives),
78 "Two-dimensional convolution layer backpropagation biasDerivative result (first 5 rows):", 5, 15)