def process_jobs(config):
common_config = config.common;
for job in config.jobs:
log.info('Processing job {} with base {}'.format(job.name, job.base));
job_config = merge_params(common_config, config[job.base]);
log.debug('job overrides: {}'.format(job.overrides));
job_config = merge_params(job_config, job.overrides);
job_config.experiment_root = os.path.join(
config.output_root,
job_config.type,
job.name
);
log.debug('experiment root: {}'.format(job_config.experiment_root));
print job_config;
# try:
if job_config.type == 'cnn':
train_convnet(job_config);
elif job_config.type == 'fftcnn':
train_convnet(job_config);
elif job_config.type == 'sda':
train_mlp(job_config);
else:
log.error('unsupported job type {}'.format(job_config.type));
def run_experiment(config, hyper_params, random_seeds):
if config.global_sda == False:
hyper_params = fix_local_sda_config(hyper_params)
experiment_root = hyper_params['experiment_root']
best_acc = -1
best_results = [np.NAN, np.NAN, np.NAN]
for seed in random_seeds:
hyper_params['random_seed'] = seed
hyper_params['experiment_root'] = experiment_root + '.' + str(seed)
params = merge_params(config, hyper_params)
if os.path.exists(os.path.join(params.experiment_root, 'mlp.pkl')):
print 'found existing mlp.pkl: {}'.format(params.experiment_root)
else:
print 'no mlp.pkl found at: {}'.format(params.experiment_root)
if not config.get('only_extract_results', False):
train_mlp(params)
try:
values = extract_results(params.experiment_root, mode='misclass')
results = np.multiply(
100,
[
# 1 - values['test_y_misclass'],
# 1 - values['test_wseq_misclass_rate'],
# 1 - values['test_wtrial_misclass_rate']]);
1 - values['frame_misclass'],
1 - values['sequence_misclass'],
1 - values['trial_misclass']
])
# save the best results
if np.max(results[2]) > best_acc:
best_results = results
best_acc = np.max(results[2])
except:
print traceback.format_exc()
results = [np.NAN, np.NAN, np.NAN]
print 'results for seed {}: {}'.format(seed, results)
print 'best results: {}'.format(best_results)
return best_results
def run_experiment(config, hyper_params, random_seeds):
if config.global_sda == False:
hyper_params = fix_local_sda_config(hyper_params);
experiment_root = hyper_params['experiment_root'];
best_acc = -1;
best_results = [np.NAN, np.NAN, np.NAN];
for seed in random_seeds:
hyper_params['random_seed'] = seed;
hyper_params['experiment_root'] = experiment_root + '.' + str(seed);
params = merge_params(config, hyper_params);
if os.path.exists(os.path.join(params.experiment_root, 'mlp.pkl')):
print 'found existing mlp.pkl: {}'.format(params.experiment_root);
else:
print 'no mlp.pkl found at: {}'.format(params.experiment_root);
if not config.get('only_extract_results', False):
train_mlp(params);
try:
values = extract_results(params.experiment_root, mode='misclass');
results = np.multiply(100, [
# 1 - values['test_y_misclass'],
# 1 - values['test_wseq_misclass_rate'],
# 1 - values['test_wtrial_misclass_rate']]);
1 - values['frame_misclass'],
1 - values['sequence_misclass'],
1 - values['trial_misclass']]);
# save the best results
if np.max(results[2]) > best_acc:
best_results = results;
best_acc = np.max(results[2]);
except:
print traceback.format_exc();
results = [np.NAN, np.NAN, np.NAN];
print 'results for seed {}: {}'.format(seed, results);
print 'best results: {}'.format(best_results);
return best_results;
def process_jobs(config):
common_config = config.common
for job in config.jobs:
log.info('Processing job {} with base {}'.format(job.name, job.base))
job_config = merge_params(common_config, config[job.base])
log.debug('job overrides: {}'.format(job.overrides))
job_config = merge_params(job_config, job.overrides)
job_config.experiment_root = os.path.join(config.output_root,
job_config.type, job.name)
log.debug('experiment root: {}'.format(job_config.experiment_root))
print job_config
# try:
if job_config.type == 'cnn':
train_convnet(job_config)
elif job_config.type == 'fftcnn':
train_convnet(job_config)
elif job_config.type == 'sda':
train_mlp(job_config)
else:
log.error('unsupported job type {}'.format(job_config.type))
