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As for replacing the layers, the remainder of the levels which aren't frozen are replaced with the exact same framework given that the former model. The weights and biases, even so, are replaced with randomized initialization. The model is additionally tuned at a Understanding charge of 1E-4 for ten epochs. As for unfreezing the frozen layers, the levels Beforehand frozen are unfrozen, building the parameters updatable yet again. The design is more tuned at a fair lessen Mastering rate of 1E-five for 10 epochs, yet the models still undergo considerably from overfitting.
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Performances among the a few models are proven in Table one. The disruption predictor dependant on FFE outperforms other models. The model depending on the SVM with guide aspect extraction also beats the general deep neural network (NN) model by a large margin.
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