updates
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10
train.py
10
train.py
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@ -328,7 +328,7 @@ def print_mutation(hyp, results):
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with open('evolve.txt', 'a') as f: # append result
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with open('evolve.txt', 'a') as f: # append result
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f.write(c + b + '\n')
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f.write(c + b + '\n')
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x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0) # load unique rows
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x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0) # load unique rows
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np.savetxt('evolve.txt', x[np.argsort(-fitness(x))] , '%11.3g') # save sort by fitness
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np.savetxt('evolve3.txt', x[np.argsort(-fitness(x))], '%11.3g') # save sort by fitness
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os.system('gsutil cp evolve.txt gs://%s' % opt.bucket) # upload evolve.txt
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os.system('gsutil cp evolve.txt gs://%s' % opt.bucket) # upload evolve.txt
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else:
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else:
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with open('evolve.txt', 'a') as f:
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with open('evolve.txt', 'a') as f:
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@ -410,14 +410,14 @@ if __name__ == '__main__':
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# # Plot results
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# # Plot results
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# import numpy as np
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# import numpy as np
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# import matplotlib.pyplot as plt
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# import matplotlib.pyplot as plt
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# a = np.loadtxt('evolve_1000val.txt')
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# a = np.loadtxt('evolve.txt')
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# x = a[:, 2] * a[:, 3] # metric = mAP * F1
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# x = fitness(a)
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# weights = (x - x.min()) ** 2
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# weights = (x - x.min()) ** 2
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# fig = plt.figure(figsize=(14, 7))
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# fig = plt.figure(figsize=(10, 10))
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# for i in range(len(hyp)):
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# for i in range(len(hyp)):
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# y = a[:, i + 5]
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# y = a[:, i + 5]
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# mu = (y * weights).sum() / weights.sum()
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# mu = (y * weights).sum() / weights.sum()
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# plt.subplot(2, 5, i+1)
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# plt.subplot(4, 5, i + 1)
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# plt.plot(x.max(), mu, 'o')
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# plt.plot(x.max(), mu, 'o')
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# plt.plot(x, y, '.')
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# plt.plot(x, y, '.')
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# print(list(hyp.keys())[i], '%.4g' % mu)
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# print(list(hyp.keys())[i], '%.4g' % mu)
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