else:
# load
with open(config_path, 'r') as f:
config = json.load(f)
# split
img_gen_config = config['img_gen_config']
model_config = config['model_config']
train_config = config['train_config']
# warn
warn_message = f'Using config from {config_path_rel}'
warnings.warn(warn_message)
# build img data generators
train_generator, validation_generator, test_generator, class_names = build_set_generators(
**img_gen_config)
# define model
cl = Classifier(img_gen_config=img_gen_config,
model_config=model_config,
input_shape=train_generator.x.shape[1:])
cl.class_names = class_names
# cl.train(train_generator, validation_generator, train_config)
cl.train(train_generator, validation_generator, train_config)
# eval
cl.evaluate(test_generator)
# save
cl.save()
from models import Classifier
import pickle
model_path = './models/model7.hd5'
# load dataset
with open('./datasets/80k_no_priorityroad_grey.pickle', 'rb') as f:
(x_train, y_train), (x_val, y_val), (x_test, y_test) = pickle.load(f)
if __name__ == '__main__':
# make model
model = Classifier()
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# train model
model.fit(x_train, y_train, epochs=5, batch_size=32, validation_data=(x_val, y_val))
# test model
model.evaluate(x_test, y_test)
# save model
model.save(model_path)
print('MODEL SAVED')
