params.save(json_path)
# Launch training with this config
cmd = "{python} train.py --model_dir {model_dir} --data_dir {data_dir}".format(
python=PYTHON, model_dir=model_dir, data_dir=data_dir)
print(cmd)
check_call(cmd, shell=True)
if __name__ == "__main__":
# Load the "reference" parameters from parent_dir json file
args = parser.parse_args()
json_path = os.path.join(args.parent_dir, 'params.json')
assert os.path.isfile(
json_path), "No json configuration file found at {}".format(json_path)
params = Params(json_path)
# Perform hypersearch over one parameter
learning_rates = [1e-4, 1e-3, 1e-2]
batch_sizes = [32, 128, 256]
for learning_rate in learning_rates:
for batch_size in batch_sizes:
# Modify the relevant parameter in params
params.learning_rate = learning_rate
params.batch_size = batch_size
# Launch job (name has to be unique)
job_name = "learning_rate_{}".format(learning_rate)
launch_training_job(args.parent_dir, args.data_dir, job_name,
params)
parser.add_argument('--save_checkpoints_epochs',
default=1,
help="Directory containing the dataset")
if __name__ == '__main__':
tf.reset_default_graph()
tf.logging.set_verbosity(tf.logging.INFO)
random_seed = 230
tf.set_random_seed(random_seed)
# Load the parameters from json file
args = parser.parse_args()
json_path = os.path.join(args.model_dir, 'params.json')
assert os.path.isfile(
json_path), "No json configuration file found at {}".format(json_path)
params = Params(json_path)
params.batch_size = int(args.batch_size)
assert args.save_checkpoints_steps is not None or args.save_checkpoints_epochs is not None
if args.save_checkpoints_epochs is not None:
files = glob.glob(os.path.join(args.data_dir, "*_train_*tfrecord"))
num_examples = util.count_records(files)
steps_each_epoch = num_examples // params.batch_size
save_checkpoints_steps = steps_each_epoch * int(
args.save_checkpoints_epochs)
else:
save_checkpoints_steps = int(args.save_checkpoints_steps)
# Define the model
tf.logging.info("Creating the model...")
tf_config = tf.ConfigProto()
