class BaseModel(Model):
def __init__(self, hyperparameters: Dict[str, Any]):
super(BaseModel, self).__init__()
self.hyperparameters = hyperparameters
self.model = Sequential()
def predict_name(self, code_block: str):
raise NotImplementedError
@staticmethod
def from_file(path: str):
"""
:arg path directory path to a file that contains, config, model and weights.
:return a model populated from a file path.
"""
return load_model('{}/model.h5'.format(path))
def save(self, filepath, overwrite=True, include_optimizer=True) -> None:
self.model.save_weights(filepath)
model_type = type(self).__name__
model_config_to_save = {
"model_type": model_type,
"hyperparameters": self.hyperparameters,
}
# Save hyperparameters
with open('{path}/{name}/model_config.json'.format(
path=filepath, name=model_type)) as fp:
json.dump(model_config_to_save, fp)
# Save the model architecture
with open('{path}/{name}/model.json'.format(
path=filepath, name=model_type)) as model_json:
model_json.write(self.model.to_json())
# Save the weight
self.model.save_weights('{path}/{name}/model_weights.h5'.format(
path=filepath, name=model_type))
# Save the model completely
self.model.save('{path}/{name}/model.h5'.format(path=filepath,
name=model_type))
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
model.fit(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size)
model.save('potato.h5')
model.save_weights('potato_weights.h5')
