19 lines
492 B
Python
19 lines
492 B
Python
from datetime import datetime
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from sys import argv
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import numpy as np
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from config import *
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import model
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import data
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import tard_wrangler
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if len(argv) > 1:
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mymodel = model.load_model(argv[1])
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else:
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dataset = data.get_data("dataset-linux.pkl")
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mymodel = model.make_model(dataset)
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timestamp = datetime.now().strftime("%Y%m%d-%H%M%S")
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mymodel.save(MODEL_DIRECTORY + f"model_-_{timestamp}.keras")
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print(tard_wrangler.full_predict("training_set/xop.c", "training_set/xop.c.norm", mymodel))
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