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
