def train(
self,
data_augmentation,
num_epochs=5,
epoch_length=32,
learning_rate=1e-5,
num_rois=32,
use_gpu=False,
):
"""Fit deep learning model."""
# Initialize paths when creating the results folder
base_path = self.__generate_results_path("training")
annotate_path = base_path + "/annotate.txt"
weights_output_path = base_path + "/flowchart_3b_model.hdf5"
config_output_filename = base_path + "/config.pickle"
# Create folder training folder
os.mkdir(base_path)
# Instance Trainer
trainer = Trainer(base_path, use_gpu)
# Recover data from dataset
trainer.recover_data(self.dataset_path,
annotate_path,
generate_annotate=True)
# Configure trainer
trainer.configure(
data_augmentation,
self.num_rois,
weights_output_path,
self.weights_input_path,
num_epochs=num_epochs,
epoch_length=epoch_length,
learning_rate=learning_rate,
)
trainer.save_config(config_output_filename)
trainer.train()
