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40 lines
No EOL
1.6 KiB
Python
40 lines
No EOL
1.6 KiB
Python
import numpy as np
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import json, math, time, os
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if __name__ == "__main__":
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#Res print
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nb_run=3
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accs = []
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taccs = []
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# aug_accs = []
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# f1_max = []
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# f1_min = []
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# times = []
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# mem = []
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files = ["res/benchmark_NoCeil/log/MyLeNetMatStochBUNoceil-50epochs__k4_%s.json"%(str(run)) for run in range(1, nb_run+1)]
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for idx, file in enumerate(files):
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#legend+=str(idx)+'-'+file+'\n'
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with open(file) as json_file:
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data = json.load(json_file)
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# accs.append(data['Accuracy'])
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accs.append(max([x["test_acc"] for x in data]))
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taccs.append(max([x["train_acc"] for x in data]))
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# aug_accs.append(data['Aug_Accuracy'][1])
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# times.append(data['Time'][0])
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# mem.append(data['Memory'][1])
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# acc_idx = [x['acc'] for x in data['Log']].index(data['Accuracy'])
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# f1_max.append(max(data['Log'][acc_idx]['f1'])*100)
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# f1_min.append(min(data['Log'][acc_idx]['f1'])*100)
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# print(idx, accs[-1])
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print(files[0])
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print("Acc : %.2f ~ %.2f"%(np.mean(accs), np.std(accs)))
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print("Acc train : %.2f ~ %.2f"%(np.mean(taccs), np.std(taccs)))
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# print("Acc : %.2f ~ %.2f / Aug_Acc %d: %.2f ~ %.2f"%(np.mean(accs), np.std(accs), data['Aug_Accuracy'][0], np.mean(aug_accs), np.std(aug_accs)))
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# print("F1 max : %.2f ~ %.2f / F1 min : %.2f ~ %.2f"%(np.mean(f1_max), np.std(f1_max), np.mean(f1_min), np.std(f1_min)))
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# print("Time (h): %.2f ~ %.2f"%(np.mean(times)/3600, np.std(times)/3600))
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# print("Mem (MB): %d ~ %d"%(np.mean(mem), np.std(mem))) |