About bihaoxyz
About bihaoxyz
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Consequently, it is the greatest observe to freeze all layers during the ParallelConv1D blocks and only fantastic-tune the LSTM levels plus the classifier without the need of unfreezing the frozen layers (scenario 2-a, as well as the metrics are demonstrated just in case two in Desk two). The levels frozen are viewed as able to extract basic characteristics throughout tokamaks, even though the rest are considered tokamak specific.
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บันทึกชื่อ, อีเมล และชื่อเว็บไซต์ของฉันบนเบราว์เซอร์นี�?สำหรับการแสดงความเห็นครั้งถัดไป
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We suppose which the ParallelConv1D layers are speculated to extract the characteristic in just a frame, which happens to be a time slice of 1 ms, even though the LSTM levels target extra on extracting the capabilities in a longer time scale, that's tokamak dependent.
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Within our situation, the FFE skilled on J-TEXT is expected to be able to extract low-level features across diverse tokamaks, for example All those related to MHD instabilities along with other functions that are common throughout various tokamaks. The top layers (layers nearer on the output) with the pre-qualified model, generally the classifier, in addition to the top on the characteristic extractor, are used for extracting superior-stage functions specific into the supply tasks. The best levels from the model tend to be good-tuned or changed to produce them more applicable with the concentrate on undertaking.
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