def main(dataset_name, save_dir, cfg):
## Data
data_dir = os.path.join('/scail/data/group/atlas/kalpit/data',
dataset_name)
dataset = Dataset(data_dir)
## Model
print 'Creating Model...'
model = get_model(dataset_name, cfg)
#model.summary()
## Train
print 'Training Model...'
starttime = time.time()
train_loss_batch, train_acc_batch, train_loss, val_loss, val_acc = train(
model, dataset, cfg)
endtime = time.time()
plot_loss(train_loss, save_dir, 'training_cost', 'training_cost')
plot_loss(val_loss, save_dir, 'validation_cost', 'validation_cost')
## Validate
print ''
print 'Final Validation...'
validate(model, dataset)
## Training Time
print 'Training Time: {:.2f}'.format(endtime - starttime)
return min(train_loss)
def main(dataset_name, network, save_dir, cfg):
## Data
data_dir = os.path.join('/scail/data/group/atlas/kalpit/data', dataset_name)
dataset = Dataset(data_dir)
## Model
print 'Creating Model...'
model = get_model(dataset_name+'_'+network, cfg)
#model.summary()
## Train
print 'Training Model...'
starttime = time.time()
if network=='ff':
if cfg.optimizer=='kalpit':
train_loss, val_loss, val_acc = train_ff_kalpit(model, dataset, cfg, save_dir)
else:
train_loss, val_loss, val_acc = train_ff_vanilla(model, dataset, cfg, save_dir)
elif network=='conv':
dataset.data_reshape((cfg.input_height,cfg.input_width,cfg.input_nchannels)) # for both mnist and cifar10
if cfg.optimizer=='kalpit':
train_loss, val_loss, val_acc = train_conv_kalpit(model, dataset, cfg, save_dir)
else:
train_loss, val_loss, val_acc = train_conv_vanilla(model, dataset, cfg, save_dir)
elif network=='autoencoder':
if cfg.optimizer=='kalpit':
train_loss, val_loss = train_autoencoder_kalpit(model, dataset, cfg, save_dir)
else:
train_loss, val_loss = train_autoencoder_vanilla(model, dataset, cfg, save_dir)
else:
raise NotImplementedError
endtime = time.time()
#plot_loss(train_loss, save_dir, 'training_cost', 'training_cost')
#plot_loss(val_loss, save_dir, 'validation_cost', 'validation_cost')
## Validate
print ''
print 'Final Validation...'
if network=='ff':
validate_ff(model, dataset)
elif network=='conv':
validate_conv(model, dataset)
elif network=='autoencoder':
validate_autoencoder(model, dataset)
## Training Time
print 'Training Time: {:.2f}'.format(endtime - starttime)
return min(train_loss)
if final_run:
sys.stdout = open(os.path.join(save_dir, 'stdout'), 'w')
print run_id
print 'testing'
## Data
data_dir = os.path.join('/scail/data/group/atlas/kalpit/data', dataset_name)
dataset = Dataset(data_dir)
## Config
cfg = Config(save_dir)
## Model
print 'Creating Model...'
print 'DROPOUT NOT IMPLEMENTED CORRECTLY FOR VALIDATION!!!'
model = get_model(dataset_name, cfg)
#model.summary()
## Train
print 'Training Model...'
starttime = time.time()
train_loss_batch, train_acc_batch, train_loss, val_loss, val_acc = train(model, dataset, cfg)
endtime = time.time()
plot_loss(train_loss, save_dir, 'training_cost', 'training_cost')
plot_loss(val_loss, save_dir, 'validation_cost', 'validation_cost')
## Validate
print ''
print 'Final Validation...'
validate(model, dataset)