56 from daal.algorithms.neural_networks
import layers
57 from daal.algorithms.neural_networks.layers
import loss
58 from daal.algorithms.neural_networks.layers.loss
import softmax_cross
60 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
61 if utils_folder
not in sys.path:
62 sys.path.insert(0, utils_folder)
63 from utils
import printTensor, readTensorFromCSV
66 datasetName = os.path.join(
"..",
"data",
"batch",
"softmax_cross_entropy_layer.csv")
67 datasetGroundTruthName = os.path.join(
"..",
"data",
"batch",
"softmax_cross_entropy_layer_ground_truth.csv")
69 if __name__ ==
"__main__":
72 tensorData = readTensorFromCSV(datasetName)
73 groundTruth = readTensorFromCSV(datasetGroundTruthName,
True)
76 softmaxCrossLayerForward = loss.softmax_cross.forward.Batch(method=loss.softmax_cross.defaultDense)
79 softmaxCrossLayerForward.input.setInput(layers.forward.data, tensorData)
80 softmaxCrossLayerForward.input.setInput(loss.forward.groundTruth, groundTruth)
83 forwardResult = softmaxCrossLayerForward.compute()
86 printTensor(forwardResult.getResult(layers.forward.value),
"Forward softmax cross-entropy layer result (first 5 rows):", 5)
87 printTensor(forwardResult.getLayerData(loss.softmax_cross.auxProbabilities),
"Softmax Cross-Entropy layer probabilities estimations (first 5 rows):", 5)
88 printTensor(forwardResult.getLayerData(loss.softmax_cross.auxGroundTruth),
"Softmax Cross-Entropy layer ground truth (first 5 rows):", 5)
91 softmaxCrossLayerBackward = softmax_cross.backward.Batch(method=loss.softmax_cross.defaultDense)
94 softmaxCrossLayerBackward.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
97 backwardResult = softmaxCrossLayerBackward.compute()
100 printTensor(backwardResult.getResult(layers.backward.gradient),
"Backward softmax cross-entropy layer result (first 5 rows):", 5)