mirror of
https://github.com/osmarks/meme-search-engine.git
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53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
# claude-3
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import json
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import matplotlib.pyplot as plt
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# Read data from log.jsonl
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data = []
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with open('log.jsonl', 'r') as file:
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for line in file:
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data.append(json.loads(line))
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# Extract steps, loss, and val_loss
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steps = [entry['step'] for entry in data if "loss" in entry]
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loss = [entry['loss'] for entry in data if "loss" in entry]
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val_loss_data = [entry['val_loss'] for entry in data if 'val_loss' in entry]
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val_steps = [entry['step'] for entry in data if 'val_loss' in entry]
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# Extract individual validation loss series
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val_loss_series = {}
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for val_loss in val_loss_data:
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for key, value in val_loss.items():
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if key not in val_loss_series:
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val_loss_series[key] = []
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val_loss_series[key].append(value)
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# Calculate rolling average for loss
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window_size = 50
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rolling_avg = [sum(loss[i:i+window_size])/window_size for i in range(len(loss)-window_size+1)]
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rolling_steps = steps[window_size-1:]
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# Calculate rolling averages for validation loss series
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val_rolling_avgs = {}
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for key, series in val_loss_series.items():
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val_rolling_avgs[key] = [sum(series[i:i+window_size])/window_size for i in range(len(series)-window_size+1)]
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print([(name, min(series)) for name, series in val_loss_series.items()])
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# Create the plot
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plt.figure(figsize=(10, 6))
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#plt.plot(steps, loss, label='Loss')
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plt.plot(rolling_steps, rolling_avg, label='Rolling Average (Loss)')
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for key, series in val_loss_series.items():
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#plt.plot(val_steps, series, marker='o', linestyle='', label=f'Validation Loss ({key})')
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plt.plot(val_steps[window_size-1:], val_rolling_avgs[key], label=f'Rolling Average (Validation Loss {key})')
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plt.xlabel('Steps')
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plt.ylabel('Loss')
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plt.title('Loss and Validation Loss vs. Steps')
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plt.legend()
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plt.grid(True)
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plt.show()
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