def train_model(foldername,Model_Type,num_objects=2, num_experiments=1, enhance_data=False, batch_size=1, show_network_summary=True): model_trainer = ModelTraining() if Model_Type in "ResNet": model_trainer.setModelTypeAsResNet() elif Model_Type in "SqueezeNet": model_trainer.setModelTypeAsSqueezeNet() elif Model_Type in "InceptionV3": model_trainer.setModelTypeAsInceptionV3() elif Model_Type in "DenseNet": model_trainer.setModelTypeAsDenseNet() model_trainer.setDataDirectory(foldername) model_trainer.trainModel(num_objects=num_objects, num_experiments=num_experiments, enhance_data=enhance_data, batch_size=batch_size, show_network_summary=show_network_summary)
def test_densenet_training(clear_keras_session): trainer = ModelTraining() trainer.setModelTypeAsDenseNet() trainer.setDataDirectory(data_directory=sample_dataset) trainer.trainModel(num_objects=10, num_experiments=1, enhance_data=True, batch_size=8, show_network_summary=True) assert os.path.isdir(sample_dataset_json_folder) assert os.path.isdir(sample_dataset_models_folder) assert os.path.isfile( os.path.join(sample_dataset_json_folder, "model_class.json")) assert (len(os.listdir(sample_dataset_models_folder)) > 0) shutil.rmtree(os.path.join(sample_dataset_json_folder)) shutil.rmtree(os.path.join(sample_dataset_models_folder))
from imageai.Prediction.Custom import ModelTraining import os trainer = ModelTraining() trainer.setModelTypeAsDenseNet() trainer.setDataDirectory("idenprof") trainer.trainModel( num_objects=10, num_experiments=50, enhance_data=True, batch_size=8, show_network_summary=True, continue_from_model="idenprof_densenet-0.763500.h5" ) # Download the model via this link https://github.com/OlafenwaMoses/ImageAI/releases/tag/models-v3