56 from daal.algorithms.neural_networks
import layers
57 from daal.data_management
import HomogenTensor, TensorIface
59 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
60 if utils_folder
not in sys.path:
61 sys.path.insert(0, utils_folder)
62 from utils
import printTensor
65 datasetFileName = os.path.join(
"..",
"data",
"batch",
"layer.csv")
67 if __name__ ==
"__main__":
70 inDims = [2, 1, 16, 16]
71 tensorData = HomogenTensor(inDims, TensorIface.doAllocate, 1.0)
74 convolution2dLayerForward = layers.convolution2d.forward.Batch()
75 convolution2dLayerForward.input.setInput(layers.forward.data, tensorData)
78 forwardResult = convolution2dLayerForward.compute()
80 printTensor(forwardResult.getResult(layers.forward.value),
"Two-dimensional convolution layer result (first 5 rows):", 5, 15)
81 printTensor(forwardResult.getLayerData(layers.convolution2d.auxWeights),
82 "Two-dimensional convolution layer weights (first 5 rows):", 5, 15)
84 gDims = forwardResult.getResult(layers.forward.value).getDimensions()
87 tensorDataBack = HomogenTensor(gDims, TensorIface.doAllocate, 0.01)
90 convolution2dLayerBackward = layers.convolution2d.backward.Batch()
91 convolution2dLayerBackward.input.setInput(layers.backward.inputGradient, tensorDataBack)
92 convolution2dLayerBackward.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
95 backwardResult = convolution2dLayerBackward.compute()
97 printTensor(backwardResult.getResult(layers.backward.gradient),
98 "Two-dimensional convolution layer backpropagation gradient result (first 5 rows):", 5, 15)
99 printTensor(backwardResult.getResult(layers.backward.weightDerivatives),
100 "Two-dimensional convolution layer backpropagation weightDerivative result (first 5 rows):", 5, 15)
101 printTensor(backwardResult.getResult(layers.backward.biasDerivatives),
102 "Two-dimensional convolution layer backpropagation biasDerivative result (first 5 rows):", 5, 15)