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));
# except:
# log.fatal("Unexpected error:", sys.exc_info());
if __name__ == '__main__':
default_config = os.path.join(os.path.dirname(__file__), 'batch.cfg');
config = load_config(default_config=default_config, reset_logging=False);
config = merge_params(load_config_file(default_config), config);
process_jobs(config);
train, yaml_str = load_yaml_file(
os.path.join(os.path.dirname(__file__), 'train_convnet_template.yaml'),
params=config,
);
save_yaml_file(yaml_str, os.path.join(config.experiment_root, 'settings.yaml'));
with log_timing(log, 'training network'):
train.main_loop();
def get_default_config_path():
return os.path.join(os.path.dirname(__file__),'train_convnet.cfg');
if __name__ == '__main__':
config = load_config(default_config=get_default_config_path(), reset_logging=False);
if not config.get('only_extract_results', False):
train_convnet(config);
scan_for_best_performance(config.experiment_root, 'valid_y_misclass');
scan_for_best_performance(config.experiment_root, 'valid_ptrial_misclass_rate')
values = extract_results(config.experiment_root, mode='misclass');
print np.multiply(100, [
# 1 - values['test_y_misclass'],
# 1 - values['test_wseq_misclass_rate'],
# 1 - values['test_wtrial_misclass_rate']]);
1 - values['frame_misclass'],
report = analyze_reconstruction_error(datasets, minibatched_output_fn);
for line in report:
print '{:>6} : mean = {:.3f} std = {:.3f} max = {:.3f}'.format(*line);
# analyze_frames(dataset, output_fn);
if worst_frames:
analyze_worst_frames(datasets['train'], minibatched_output_fn, output_path=output_path);
# analyze_reconstruction(dataset, minibatched_output_fn, hop_size);
analyze_complex(datasets['test'], minibatched_output_fn, config.hop_size, output_path=output_path);
return report;
if __name__ == '__main__':
config = load_config(default_config=
# '/Users/sstober/git/deepbeat/deepbeat/output/gpu/sda/batch_400Hz_80-50-25-10/sda_400Hz_80-50-25-10.cfg'
# '/Users/sstober/git/deepbeat/deepbeat/output/gpu/sda/batch_400Hz_100-50-25-10/sda_400Hz_100-50-25-10.cfg'
'/Users/sstober/git/deepbeat/deepbeat/output/gpu/sda/batch_100Hz_100-50-25-10/sda_100Hz_100-50-25-10.cfg'
);
# os.path.join(os.path.dirname(__file__), 'run', 'train_sda_mlp.cfg'), reset_logging=False);
# config.dataset_suffix = '_channels';
config.dataset_root = '/Users/sstober/work/datasets/Dan/eeg';
config.experiment_root = '/Users/sstober/git/deepbeat/deepbeat/output/gpu/sda/batch_400Hz_80-50-25-10/';
config.experiment_root = '/Users/sstober/git/deepbeat/deepbeat/output/gpu/sda/batch_400Hz_100-50-25-10/';
config.experiment_root = '/Users/sstober/git/deepbeat/deepbeat/output/gpu/sda/batch_100Hz_100-50-25-10/';
# analyze(config, subjects='all', worst_frames=True);
analyze(config, subjects=[1], worst_frames=False);
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))
# except:
# log.fatal("Unexpected error:", sys.exc_info());
if __name__ == '__main__':
default_config = os.path.join(os.path.dirname(__file__), 'batch.cfg')
config = load_config(default_config=default_config, reset_logging=False)
config = merge_params(load_config_file(default_config), config)
process_jobs(config)
1 - values['sequence_misclass'],
1 - values['trial_misclass']]);
accuracy[i] = 100 * (1 - extract_best_result(params.experiment_root, mode='misclass', check_dataset='test')[0]);
print results;
print results.mean(axis=0);
print results.max(axis=1);
print accuracy;
print accuracy.mean();
return results, accuracy;
if __name__ == '__main__':
config = load_config(default_config=
os.path.join(os.path.dirname(__file__),'train_convnet.cfg'), reset_logging=True);
# override settings
# config.experiment_root = os.path.join(config.experiment_root, 'cross-trial');
# config.subjects = None;
# config.subjects = 8; # subj9 0-indexed
# config.max_epochs = 5;
# FIXME: remove manual override
# config.only_extract_results = True;
# config.experiment_root = '/Users/sstober/git/deepbeat/deepbeat/output/gpu/fftcnn/cross-test2/'
# config.experiment_root = '/Users/sstober/git/deepbeat/deepbeat/output/gpu/fftcnn/exp08.a-crosstrial.subj9/';
if config.get('full_cross_trial', False):
full_cross_trial_test(config);
else:
from deepthought.util.config_util import merge_params
from deepthought.experiments.ismir2014.train_convnet import train_convnet
# from deepthought.experiments.ismir2014.cross_trial_test import full_cross_trial_test;
def run(params):
try:
train_convnet(params)
except:
print "Unexpected error:", sys.exc_info()[0]
if __name__ == '__main__':
config = load_config(default_config=os.path.join(os.path.dirname(__file__),
'train_convnet.cfg'),
reset_logging=False)
batch_subjects = config.get('batch_subjects', xrange(13))
# per person
for i in batch_subjects:
hyper_params = {
'experiment_root':
os.path.join(config.experiment_root, 'individual',
'subj' + str(i + 1)),
'subjects': [i]
}
params = merge_params(config, hyper_params)
print confusion_matrix(labels, y_pred);
labels = np.argmax(testset.y, axis=1)
print classification_report(labels, y_pred);
print confusion_matrix(labels, y_pred);
misclass = (labels != y_pred).mean()
print misclass
# # alternative version from KeepBestParams
# minibatch = T.matrix('minibatch')
# output_fn = theano.function(inputs=[minibatch],outputs=T.argmax( model.fprop(minibatch), axis = 1 ));
# it = testset.iterator('sequential', batch_size=batch_size, targets=False);
# y_pred = [output_fn(mbatch) for mbatch in it];
# y_hat = T.argmax(state, axis=1)
# y = T.argmax(target, axis=1)
# misclass = T.neq(y, y_hat).mean()
# misclass = T.cast(misclass, config.floatX)
# rval['misclass'] = misclass
# rval['nll'] = self.cost(Y_hat=state, Y=target)
log.debug('done');
if __name__ == '__main__':
config = load_config(default_config='../train_sda.cfg', reset_logging=False);
analyze(config);
params=params,
);
save_yaml_file(train_yaml_str, os.path.join(params.experiment_root, 'mlp_train.yaml'));
with log_timing(log, 'training MLP'):
train.main_loop();
log.info('done');
def get_default_config_path():
return os.path.join(os.path.dirname(__file__),'train_sda_mlp.cfg');
if __name__ == '__main__':
# config = load_config(default_config='../../train_sda.cfg', reset_logging=False);
config = load_config(default_config=get_default_config_path(), reset_logging=False);
hyper_params = {
};
params = merge_params(config, hyper_params);
if not config.get('only_extract_results', False):
train_mlp(params);
scan_for_best_performance(params.experiment_root, 'valid_y_misclass');
scan_for_best_performance(params.experiment_root, 'valid_ptrial_misclass_rate')
values = extract_results(config.experiment_root, mode='misclass');
print np.multiply(100, [
for col, group, subjects in groups:
hyper_params = {
'experiment_root': os.path.join(config.experiment_root,
str(group)),
'subjects': subjects
# NOTE: layerX_content should still point to global sda/ folder
}
table_row[col:col + 3] = run_experiment(config, hyper_params,
random_seeds)
print table_row
return table_row
if __name__ == '__main__':
config = load_config(default_config=get_default_config_path())
## this will save the raw network output for the best model
# config.save_output = True;
# FIXME: remove manual override
# config.only_extract_results = True;
#
# config.experiment_root = '/Users/sstober/git/deepbeat/deepbeat/output/gpu.old/cnn/bigbatch/';
#
# config.experiment_root = '/Users/sstober/git/deepbeat/deepbeat/output/gpu/fftcnn/exp05.2/'; ## <= BEST so far
# 72.2 & 72.8 & 73.7
row = run_experiments_for_table_row(config)
print config.experiment_root
[11, 'all', 'all']
];
for col, group, subjects in groups:
hyper_params = {
'experiment_root' : os.path.join(config.experiment_root, str(group)),
'subjects' : subjects
# NOTE: layerX_content should still point to global sda/ folder
};
table_row[col:col+3] = run_experiment(config, hyper_params, random_seeds);
print table_row;
return table_row;
if __name__ == '__main__':
config = load_config(default_config=get_default_config_path());
# FIXME: remove manual override
# config.only_extract_results = True;
# config.experiment_root = '/Users/stober/git/deepbeat/deepbeat/output/gpu.old/sda/exp6.14all'
# config.experiment_root = '/Users/sstober/git/deepbeat/deepbeat/output/gpu/sda/batch1/batch_50-25-10.a lr 0.00005 + Momentum 0.5-0.7/'
# config.experiment_root = '/Users/sstober/git/deepbeat/deepbeat/output/gpu/sda/batch2/batch_50-25-10.f lr 0.00005 + AdaDelta/'
# config.experiment_root = '/Users/sstober/git/deepbeat/deepbeat/output/gpu/sda/batch2/batch_50-25-10.d lr0.3 + 0.5 Momentum';
# config.experiment_root = '/Users/sstober/git/deepbeat/deepbeat/output/gpu/sda/batch3/batch_50-25-10.k ';
row = run_experiments_for_table_row(config);
print config.experiment_root;
s = '';
for i,f in enumerate(row):
if worst_frames:
analyze_worst_frames(datasets['train'],
minibatched_output_fn,
output_path=output_path)
# analyze_reconstruction(dataset, minibatched_output_fn, hop_size);
analyze_complex(datasets['test'],
minibatched_output_fn,
config.hop_size,
output_path=output_path)
return report
if __name__ == '__main__':
config = load_config(
default_config=
# '/Users/sstober/git/deepbeat/deepbeat/output/gpu/sda/batch_400Hz_80-50-25-10/sda_400Hz_80-50-25-10.cfg'
# '/Users/sstober/git/deepbeat/deepbeat/output/gpu/sda/batch_400Hz_100-50-25-10/sda_400Hz_100-50-25-10.cfg'
'/Users/sstober/git/deepbeat/deepbeat/output/gpu/sda/batch_100Hz_100-50-25-10/sda_100Hz_100-50-25-10.cfg'
)
# os.path.join(os.path.dirname(__file__), 'run', 'train_sda_mlp.cfg'), reset_logging=False);
# config.dataset_suffix = '_channels';
config.dataset_root = '/Users/sstober/work/datasets/Dan/eeg'
config.experiment_root = '/Users/sstober/git/deepbeat/deepbeat/output/gpu/sda/batch_400Hz_80-50-25-10/'
config.experiment_root = '/Users/sstober/git/deepbeat/deepbeat/output/gpu/sda/batch_400Hz_100-50-25-10/'
config.experiment_root = '/Users/sstober/git/deepbeat/deepbeat/output/gpu/sda/batch_100Hz_100-50-25-10/'
# analyze(config, subjects='all', worst_frames=True);
analyze(config, subjects=[1], worst_frames=False)
def plot2(root):
plot(root+'/mlp.pkl', channels, root+'/plot.pdf');
plot(root+'/mlp.pkl', less_channels, root+'/plot-less.pdf', get_color_variants(2));
def plot_all(root_path):
import fnmatch
# matches = []
for root, dirnames, filenames in os.walk(root_path):
for filename in fnmatch.filter(filenames, 'mlp.pkl'):
# matches.append(os.path.join(root, filename))
plot2(root);
if __name__ == '__main__':
config = load_config(default_config=
# os.path.join(os.path.dirname(__file__), '..', 'run', 'train_convnet.cfg'), reset_logging=True);
os.path.join(os.path.dirname(__file__), '..', 'run', 'train_fftconvnet.cfg'), reset_logging=True);
# os.path.join(os.path.dirname(__file__), '..', 'run', 'train_sda_mlp.cfg'), reset_logging=True);
# FIXME:
# root = '/Users/stober/git/deepbeat/deepbeat/output/gpu/sda/exp6.14all/';
# root = '/Users/stober/git/deepbeat/deepbeat/output/gpu/cnn/bigbatch/individual/';
# plot_batch(root);
# for i in xrange(12):
# root = '/Users/stober/git/deepbeat/deepbeat/output/gpu/cnn/bigbatch/cross-trial/pair'+str(i);
# plot(root+'/mlp.pkl', channels, root+'/plot.pdf');
# plot(root+'/mlp.pkl', less_channels, root+'/plot-less.pdf', get_color_variants(2));
# FIXME
root = '/Users/sstober/git/deepbeat/deepbeat/output/gpu/sda/subj2_50-25-10.f';