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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-07-21 03:39:01 +02:00
parent e92a8afae1
commit 7d43ee0d02
1 changed files with 8 additions and 3 deletions
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11
train.py
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@ -319,7 +319,7 @@ def train(cfg,
def print_mutation(hyp, results): def print_mutation(hyp, results):
# Write mutation results # Write mutation results
a = '%11s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys a = '%11s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys
b = '%11.4g' * len(hyp) % tuple(hyp.values()) # hyperparam values b = '%11.3g' * len(hyp) % tuple(hyp.values()) # hyperparam values
c = '%11.3g' * len(results) % results # results (P, R, mAP, F1, test_loss) c = '%11.3g' * len(results) % results # results (P, R, mAP, F1, test_loss)
print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c)) print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c))
@ -327,12 +327,18 @@ def print_mutation(hyp, results):
os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt
with open('evolve.txt', 'a') as f: # append result with open('evolve.txt', 'a') as f: # append result
f.write(c + b + '\n') f.write(c + b + '\n')
x = np.loadtxt('evolve.txt', ndmin=2)
np.savetxt('evolve.txt', x[np.argsort(-fitness(x))], '%11.3g') # save sorted by fitness
os.system('gsutil cp evolve.txt gs://%s' % opt.bucket) # upload evolve.txt os.system('gsutil cp evolve.txt gs://%s' % opt.bucket) # upload evolve.txt
else: else:
with open('evolve.txt', 'a') as f: with open('evolve.txt', 'a') as f:
f.write(c + b + '\n') f.write(c + b + '\n')
def fitness(x): # returns fitness of hyp evolution vectors
return x[:, 2] * 0.5 + x[:, 3] * 0.5 # fitness = weighted combination of mAP and F1
if __name__ == '__main__': if __name__ == '__main__':
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=100, help='number of epochs') parser.add_argument('--epochs', type=int, default=100, help='number of epochs')
@ -373,8 +379,7 @@ if __name__ == '__main__':
for _ in range(1000): # generations to evolve for _ in range(1000): # generations to evolve
# Get best hyperparameters # Get best hyperparameters
x = np.loadtxt('evolve.txt', ndmin=2) x = np.loadtxt('evolve.txt', ndmin=2)
fitness = x[:, 2] * 0.5 + x[:, 3] * 0.5 # fitness as weighted combination of mAP and F1 x = x[fitness(x).argmax()] # select best fitness hyps
x = x[fitness.argmax()] # select best fitness hyps
for i, k in enumerate(hyp.keys()): for i, k in enumerate(hyp.keys()):
hyp[k] = x[i + 5] hyp[k] = x[i + 5]