def main():
# capture the config path from the run arguments
# then process the json configuration file
try:
args = get_args()
config = process_config(args.config)
except Exception as e:
print("missing or invalid arguments %s" % e)
exit(0)
os.environ["CUDA_VISIBLE_devices"] = config.gpu
import tensorflow as tf
# create the experiments dirs
create_dirs([config.summary_dir, config.checkpoint_dir])
# create tensorflow session
gpuconfig = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
gpuconfig.gpu_options.visible_device_list = config.gpu
sess = tf.Session(config=gpuconfig)
# create your data generator
data = DataGenerator(config)
# create an instance of the model you want
model = invariant_basic(config, data)
# create trainer and pass all the previous components to it
trainer = Trainer(sess, model, data, config)
# load model if exists
model.load(sess)
# here you train your model
trainer.train()
def main():
# capture the config path from the run arguments
# then process the json configuration file
try:
args = get_args()
config = process_config(args.config)
except Exception as e:
print("missing or invalid arguments %s" % e)
exit(0)
os.environ["CUDA_VISIBLE_devices"] = config.gpu
import tensorflow as tf
import numpy as np
tf.set_random_seed(100)
np.random.seed(100)
base_summary_folder = config.summary_dir
base_exp_name = config.exp_name
# create the experiments dirs
create_dirs([config.summary_dir, config.checkpoint_dir])
for lr in [0.00008 * (2**i) for i in range(8)]:
for decay in [0.6, 0.7, 0.8, 0.9]:
config.learning_rate = lr
config.decay_rate = decay
config.exp_name = base_exp_name + " lr={0}_decay={1}".format(
lr, decay)
curr_dir = os.path.join(base_summary_folder,
"lr={0}_decay={1}".format(lr, decay))
config.summary_dir = curr_dir
create_dirs([curr_dir])
# create your data generator
data = DataGenerator(config)
gpuconfig = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
gpuconfig.gpu_options.visible_device_list = config.gpu
sess = tf.Session(config=gpuconfig)
# create an instance of the model you want
model = invariant_basic(config, data)
# create trainer and pass all the previous components to it
trainer = Trainer(sess, model, data, config)
# here you train your model
acc, loss = trainer.train()
sess.close()
tf.reset_default_graph()
doc_utils.summary_10fold_results(config.summary_dir)
def main():
# capture the config path from the run arguments
# then process the json configuration file
try:
args = get_args()
config = process_config(args.config)
except Exception as e:
print("missing or invalid arguments %s" % e)
exit(0)
os.environ["CUDA_VISIBLE_DEVICES"] = config.gpu
# import tensorflow as tf
import torch
# create the experiments dirs
create_dirs([config.summary_dir, config.checkpoint_dir])
# create tensorflow session
# gpuconfig = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
# gpuconfig.gpu_options.visible_device_list = config.gpus_list
# gpuconfig.gpu_options.allow_growth = True
# sess = tf.Session(config=gpuconfig)
if config.cuda:
print(f'Using GPU : {torch.cuda.get_device_name(int(config.gpu))}')
else:
print(f'Using CPU')
# create your data generator
data = DataGenerator(config)
# data = torch.from_numpy(data)
# create an instance of the model you want
model = invariant_basic(config, data)
if config.cuda:
model = model.cuda()
for name, param in model.named_parameters():
# if param.device.type != 'cuda':
print(f'{name}, device type {param.device.type}')
# create trainer and pass all the previous components to it
# trainer = Trainer(sess, model, data, config)
trainer = Trainer(model, data, config)
# load model if exists
# model.load(sess)
# here you train your model
trainer.train()
def main():
# capture the config path from the run arguments
# then process the json configuration file
try:
args = get_args()
config = process_config(args.config, dataset_name='QM9')
except Exception as e:
print("missing or invalid arguments %s" % e)
exit(0)
os.environ["CUDA_VISIBLE_DEVICES"] = config.gpu
import tensorflow as tf
import numpy as np
tf.set_random_seed(100)
np.random.seed(100)
print("lr = {0}".format(config.hyperparams.learning_rate))
print("decay = {0}".format(config.hyperparams.decay_rate))
if config.target_param is not False: # (0 == False) while (0 is not False)
print("target parameter: {0}".format(config.target_param))
print(config.architecture)
# create the experiments dirs
create_dirs([config.summary_dir, config.checkpoint_dir])
doc_utils.doc_used_config(config)
# create your data generator
data = DataGenerator(config)
gpuconfig = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
gpuconfig.gpu_options.visible_device_list = config.gpus_list
gpuconfig.gpu_options.allow_growth = True
sess = tf.Session(config=gpuconfig)
# create an instance of the model you want
model = invariant_basic(config, data)
# create trainer and pass all the previous components to it
trainer = Trainer(sess, model, data, config)
# here you train your model
trainer.train()
# test model, restore best model
test_dists, test_loss = trainer.test(load_best_model=True)
sess.close()
tf.reset_default_graph()
doc_utils.summary_qm9_results(config.summary_dir, test_dists, test_loss,
trainer.best_epoch)
def main():
# capture the config path from the run arguments
# then process the json configuration file
try:
args = get_args()
config = process_config(args.config)
except Exception as e:
print("missing or invalid arguments %s" % e)
exit(0)
os.environ["CUDA_VISIBLE_DEVICES"] = config.gpu
import tensorflow as tf
import numpy as np
tf.set_random_seed(100)
np.random.seed(100)
print("lr = {0}".format(config.learning_rate))
print("decay = {0}".format(config.decay_rate))
print(config.architecture)
# create the experiments dirs
create_dirs([config.summary_dir, config.checkpoint_dir])
for exp in range(1, config.num_exp + 1):
for fold in range(1, 11):
print("Experiment num = {0}\nFold num = {1}".format(exp, fold))
# create your data generator
config.num_fold = fold
data = DataGenerator(config)
gpuconfig = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
gpuconfig.gpu_options.visible_device_list = config.gpus_list
gpuconfig.gpu_options.allow_growth = True
sess = tf.Session(config=gpuconfig)
# create an instance of the model you want
model = invariant_basic(config, data)
# create trainer and pass all the previous components to it
trainer = Trainer(sess, model, data, config)
# here you train your model
acc, loss = trainer.train()
doc_utils.doc_results(acc, loss, exp, fold, config.summary_dir)
sess.close()
tf.reset_default_graph()
doc_utils.summary_10fold_results(config.summary_dir)