def train(model, model_name): loader = DataLoader() pretrain_data, pretrain_labels, pretrain_names = loader.load_pretrain_datasets( ) # pretrain model model.fit(pretrain_data, pretrain_labels, batch_size=BATCH_SIZE, epochs=PRETRAIN_EPOCHS) deep_utils.create_directory("../models") model_filename = "../models/pretrained_" + model_name + ".h5" model.save(model_filename) train_data, train_labels, train_names = loader.load_train_datasets() test_data, test_labels, test_names = loader.load_test_datasets() # train model model.fit(train_data, train_labels, validation_data=(test_data, test_labels), batch_size=BATCH_SIZE, epochs=TRAIN_EPOCHS) deep_utils.create_directory("../models") model_filename = "../models/fine_tuned_" + model_name + ".h5" model.save(model_filename) # evaluate model scores = model.evaluate(test_data, test_labels, verbose=1) return scores
def train(model, model_name): loader = DataLoader() train_data, train_labels, train_names = loader.load_train_datasets() test_data, test_labels, test_names = loader.load_test_datasets() model.fit(train_data, train_labels, batch_size=BATCH_SIZE, epochs=TRAIN_EPOCHS, validation_data=(test_data, test_labels), shuffle=True) # save trained model deep_utils.create_directory("../models") model_filename = "../models/base_" + model_name + ".h5" model.save(model_filename) scores = model.evaluate(test_data, test_labels, verbose=1) return scores