def main():
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
# capture the config path from the run arguments
# then process the json configuration file
# try:
split = 10
training_size = []
accuracy = []
precision = []
recall = []
f1 = []
main_dir = ''
args = get_args()
for i in range(split):
config = process_config_VisOverfit(args.config, i)
# except:
# print("missing or invalid arguments")
# exit(0)
# create the experiments dirs
create_dirs([
config.callbacks.tensorboard_log_dir,
config.callbacks.checkpoint_dir, config.log_dir, config.result_dir
])
print('Create the data generator.')
data_loader = UtsClassificationDataLoader(config)
total_train_size = data_loader.get_train_size()
total_test_size = data_loader.get_test_size()
print("total_train_size: " + str(total_train_size))
print("total_test_size: " + str(total_test_size))
print('Create the model.')
model = UtsClassificationModel(config, data_loader.get_inputshape(),
data_loader.get_nbclasses())
print('Create the trainer')
train_size = int(total_train_size / split) * (i + 1)
if i == split - 1:
print("train_size: " + str(total_train_size))
trainer = UtsClassificationTrainer(model.model,
data_loader.get_train_data(),
config)
main_dir = config.main_dir
else:
print("train_size: " + str(train_size))
train_data = data_loader.get_train_data()
X_train = train_data[0][:train_size, :, :]
y_train = train_data[1][:train_size, :]
trainer = UtsClassificationTrainer(model.model, [X_train, y_train],
config)
print('Start training the model.')
trainer.train()
print('Create the evaluater.')
evaluater = UtsClassificationEvaluater(trainer.best_model,
data_loader.get_test_data(),
data_loader.get_nbclasses(),
config)
print('Start evaluating the model.')
evaluater.evluate()
training_size.append(train_size)
accuracy.append(evaluater.acc)
precision.append(evaluater.precision)
recall.append(evaluater.recall)
f1.append(evaluater.f1)
print("ss")
metrics = {
"accuracy": accuracy,
"precision": precision,
"recall": recall,
"f1": f1,
"training_size": training_size
}
plot_trainingsize_metric(metrics,
main_dir + 'vis_overfit_trainingsize.png')
def main():
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
# capture the config path from the run arguments
# then process the json configuration file
# try:
split = 10
training_size = []
best_model_train_loss = []
best_model_val_loss = []
best_model_train_acc = []
best_model_val_acc = []
best_model_train_precision = []
best_model_val_precision = []
best_model_train_recall = []
best_model_val_recall = []
best_model_train_f1 = []
best_model_val_f1 = []
best_model_learning_rate = []
best_model_nb_epoch = []
best_model_train_err = []
best_model_val_err = []
main_dir = ''
args = get_args()
for i in range(split):
config = process_config_VisTrainingSize(args.config, i)
# except:
# print("missing or invalid arguments")
# exit(0)
# create the experiments dirs
create_dirs([
config.callbacks.tensorboard_log_dir,
config.callbacks.checkpoint_dir, config.log_dir, config.result_dir
])
print('Create the data generator.')
data_loader = UtsClassificationDataLoader(config)
total_train_size = data_loader.get_train_size()
total_test_size = data_loader.get_test_size()
print("total_train_size: " + str(total_train_size))
print("total_test_size: " + str(total_test_size))
print('Create the model.')
model = UtsClassificationModel(config, data_loader.get_inputshape(),
data_loader.get_nbclasses())
print('Create the trainer')
train_size = int(total_train_size / split) * (i + 1)
if i == split - 1:
print("train_size: " + str(total_train_size))
trainer = UtsClassificationTrainer(model.model,
data_loader.get_train_data(),
config)
main_dir = config.main_dir
else:
print("train_size: " + str(train_size))
train_data = data_loader.get_train_data()
X_train = train_data[0][:train_size, :, :]
y_train = train_data[1][:train_size, :]
trainer = UtsClassificationTrainer(model.model, [X_train, y_train],
config)
print('Start training the model.')
trainer.train()
best_model_train_loss.append(trainer.best_model_train_loss)
best_model_val_loss.append(trainer.best_model_val_loss)
best_model_train_acc.append(trainer.best_model_train_acc)
best_model_val_acc.append(trainer.best_model_val_acc)
best_model_train_precision.append(trainer.best_model_train_precision)
best_model_val_precision.append(trainer.best_model_val_precision)
best_model_train_recall.append(trainer.best_model_train_recall)
best_model_val_recall.append(trainer.best_model_val_recall)
best_model_train_f1.append(trainer.best_model_train_f1)
best_model_val_f1.append(trainer.best_model_val_f1)
best_model_learning_rate.append(trainer.best_model_learning_rate)
best_model_nb_epoch.append(trainer.best_model_nb_epoch)
best_model_train_err.append(1 - trainer.best_model_train_acc)
best_model_val_err.append(1 - trainer.best_model_val_acc)
training_size.append(train_size)
print("ss")
metrics = {
"training_size": training_size,
"train_err": best_model_train_err,
"val_err": best_model_val_err
}
plot_trainingsize_metric(metrics,
main_dir + '/vis_overfit_trainingsize.png')