def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''):
# Print mutation results to evolve.txt (for use with train.py --evolve)
a = '%10s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys
b = '%10.3g' * len(hyp) % tuple(hyp.values()) # hyperparam values
c = '%10.4g' * len(results) % results # results (P, R, [email protected], [email protected]:0.95, val_losses x 3)
print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c))
if bucket:
url = 'gs://%s/evolve.txt' % bucket
if gsutil_getsize(url) > (os.path.getsize('evolve.txt') if os.path.exists('evolve.txt') else 0):
os.system('gsutil cp %s .' % url) # download evolve.txt if larger than local
with open('evolve.txt', 'a') as f: # append result
f.write(c + b + '\n')
x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0) # load unique rows
x = x[np.argsort(-fitness(x))] # sort
np.savetxt('evolve.txt', x, '%10.3g') # save sort by fitness
# Save yaml
for i, k in enumerate(hyp.keys()):
hyp[k] = float(x[0, i + 7])
with open(yaml_file, 'w') as f:
results = tuple(x[0, :7])
c = '%10.4g' * len(results) % results # results (P, R, [email protected], [email protected]:0.95, val_losses x 3)
f.write('# Hyperparameter Evolution Results\n# Generations: %g\n# Metrics: ' % len(x) + c + '\n\n')
yaml.dump(hyp, f, sort_keys=False)
if bucket:
os.system('gsutil cp evolve.txt %s gs://%s' % (yaml_file, bucket)) # upload
def print_mutation(hyp, results, yaml_file="hyp_evolved.yaml", bucket=""):
# Print mutation results to evolve.txt (for use with train.py --evolve)
a = "%10s" * len(hyp) % tuple(hyp.keys()) # hyperparam keys
b = "%10.3g" * len(hyp) % tuple(hyp.values()) # hyperparam values
c = ("%10.4g" * len(results) % results
) # results (P, R, [email protected], [email protected]:0.95, val_losses x 3)
print("\n%s\n%s\nEvolved fitness: %s\n" % (a, b, c))
if bucket:
url = "gs://%s/evolve.txt" % bucket
if gsutil_getsize(url) > (os.path.getsize("evolve.txt")
if os.path.exists("evolve.txt") else 0):
os.system("gsutil cp %s ." %
url) # download evolve.txt if larger than local
with open("evolve.txt", "a") as f: # append result
f.write(c + b + "\n")
x = np.unique(np.loadtxt("evolve.txt", ndmin=2),
axis=0) # load unique rows
x = x[np.argsort(-fitness(x))] # sort
np.savetxt("evolve.txt", x, "%10.3g") # save sort by fitness
# Save yaml
for i, k in enumerate(hyp.keys()):
hyp[k] = float(x[0, i + 7])
with open(yaml_file, "w") as f:
results = tuple(x[0, :7])
c = ("%10.4g" * len(results) % results
) # results (P, R, [email protected], [email protected]:0.95, val_losses x 3)
f.write(
"# Hyperparameter Evolution Results\n# Generations: %g\n# Metrics: "
% len(x) + c + "\n\n")
yaml.dump(hyp, f, sort_keys=False)
if bucket:
os.system("gsutil cp evolve.txt %s gs://%s" %
(yaml_file, bucket)) # upload