pathdir = "keras_models/" + run_uuid
model_dir = self.get_directory_path(pathdir, False)
ktrain_cls.keras_save_model(model, model_dir)
# Write out TensorFlow events as a run artifact
print("Uploading TensorFlow events as a run artifact.")
mlflow.log_artifacts(output_dir, artifact_path="events")
print("loss function use", args.loss)
if __name__ == '__main__':
#
# main used for testing the functions
#
parser = KParseArgs()
args = parser.parse_args()
flag = len(sys.argv) == 1
if flag:
print("Using Default Baseline parameters")
else:
print("Using Experimental parameters")
print("hidden_layers:", args.hidden_layers)
print("output:", args.output)
print("epochs:", args.epochs)
print("loss:", args.loss)
KTrain().train_models(args, flag)
def build_run_args_list(self, run_data):
a_list = []
for p in run_data.params:
if p.key == 'loss_function':
a_list.append('--loss')
else:
a_list.append('--' + p.key)
a_list.append(p.value)
return a_list
if __name__ == '__main__':
#
# main used for testing the functions
#
parser = KParseArgs()
args = parser.parse_args()
flag = len(sys.argv) == 1
cls = KReproduce()
data = cls.get_run_data(args.run_uuid, args.tracking_server)
args_list = cls.build_run_args_list(data)
print("run_uuid:", args.run_uuid)
args = parser.parse_args_list(args_list)
print("hidden_layers:", args.hidden_layers)
print("output:", args.output)
print("epochs:", args.epochs)