def main() -> None:
timer = Timer('Model training')
model_trainer, images = set_up_components('base_model_location')
model_trainer.train_and_save_all_models(images)
model_trainer.charts.finalize()
print('class 1: ' + images.class_labels[0] + ', class 2: ' +
images.class_labels[1])
timer.stop()
timer.print_results()
def main():
# Start execution and parse arguments
timer = Timer('Classifying a test set')
image_folders, list_of_models, color_mode, image_size = process_input_arguments()
# Import images
images = LabeledImages(SEED)
images.load_testing_images(image_folders, image_size, color_mode)
print('Images imported.')
combined_results = pd.DataFrame()
combined_results['filename'] = images.test_img_names
combined_results['actual_class'] = images.test_labels
all_predictions = pd.DataFrame()
for model_path in list_of_models:
classify_images_with_a_model(images.class_labels, all_predictions, images, model_path)
all_predictions['voted_probability'] = all_predictions.mean(axis=1)
# calculate_confusion_matrix(combined_results)
combined_results = combined_results.join(all_predictions)
combined_results['tp'] = combined_results.eval('actual_class == 1 and voted_probability >= 0.5')
combined_results['fn'] = combined_results.eval('actual_class == 1 and voted_probability < 0.5')
combined_results['fp'] = combined_results.eval('actual_class == 0 and voted_probability >= 0.5')
combined_results['tn'] = combined_results.eval('actual_class == 0 and voted_probability < 0.5')
combined_results['voted_label'] = combined_results.eval('voted_probability >= 0.5')
combined_results['tp'] = combined_results['tp'].map(lambda v: 1 if v else 0)
combined_results['fn'] = combined_results['fn'].map(lambda v: 1 if v else 0)
combined_results['fp'] = combined_results['fp'].map(lambda v: 1 if v else 0)
combined_results['tn'] = combined_results['tn'].map(lambda v: 1 if v else 0)
combined_results['voted_label'] = combined_results['voted_label'].map(lambda v: 1 if v else 0)
combined_results.columns = ['filename', 'actual_class'] + list_of_models + \
['voted_probability', 'tp', 'fn', 'fp', 'tn', 'voted_label']
if not os.path.exists('predictions'):
os.makedirs('predictions')
write_dataframe_to_csv('predictions', 'model_vote_predict', combined_results)
# Finish execution
timer.stop()
timer.print_results()
def main() -> None:
timer = Timer('Model training')
cnn_arguments = CNNArguments()
new_images = LabeledImages(SEED)
new_images.load_images_from_folders(cnn_arguments.training_image_folder,
cnn_arguments.image_size,
cnn_arguments.color_mode,
shuffle=True,
n_folds=cnn_arguments.n_folds)
architecture = SmithsonianModel(SEED, cnn_arguments.lr,
cnn_arguments.image_size,
cnn_arguments.color_mode)
trainer = ModelTrainer(cnn_arguments.n_epochs, cnn_arguments.batch_size,
cnn_arguments.n_folds, architecture, SEED)
trainer.train_and_save_all_models(new_images)
trainer.charts.finalize()
print('class 1: ' + new_images.class_labels[0] + ', class 2: ' +
new_images.class_labels[1])
timer.stop()
timer.print_results()
