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For the stacked LSTM and the LSTM parts of seq-to-seq mannequin, the LSTM layer is adopted by a rectifier activation and dropout layer with dropout price 0.2. We’ll practice each classifier for 200 epochs with early termination conditioned on the lack of developing set. We first split all knowledge into coaching set and check set. Within the coaching set, the reference date is from April 1st to Jun thirtieth, 2020 (91 days in complete). Within the test set, the reference date is from July 1st to August thirtieth, 2020 (61 days in total). We randomly choose 10 days from coaching set as a improvement set for hyper-parameters tuning purposes. We evaluate the held-out take a look at set using accuracy and area under the receiver working characteristic (AUC). We build your complete pipeline utilizing Python 3.6. Keras is used to design deep neural networks. Pandas and Numpy is utilized to pre-course of knowledge.
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