37 lines
633 B
Python
37 lines
633 B
Python
import torch
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from pathlib import Path
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import celeste_ai.plotting as plotting
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from multiprocessing import Pool
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m = Path("model_data/current")
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def plot_pred(src_model):
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plotting.predicted_reward(
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src_model,
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m / f"plots/predicted/{src_model.stem}.png",
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device = torch.device("cpu")
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)
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def plot_best(src_model):
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plotting.best_action(
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src_model,
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m / f"plots/best_action/{src_model.stem}.png",
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device = torch.device("cpu")
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)
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for k, v in {
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#"prediction": plot_pred,
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"best_action": plot_best,
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}.items():
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print(f"Making {k} plots...")
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with Pool(5) as p:
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p.map(
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v,
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list((m / "model_archive").iterdir())
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) |