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
