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import tensorflow as tf
from urllib.parse import urlparse
import mlflow
import mlflow.keras
from pathlib import Path
from kidney_classification.utils.common import save_json
from kidney_classification.entity.config_entity import EvaluationConfig
class Evaluation:
def __init__(self, config: EvaluationConfig):
self.config = config
self.valid_generator = None # Initialize to None
def _valid_generator(self):
img_height, img_width = self.config.params_image_size[:-1]
self.valid_generator = tf.keras.utils.image_dataset_from_directory(
self.config.training_data,
image_size=(img_height, img_width),
validation_split=0.30,
subset="validation",
seed=123,
)
self.valid_generator = self.valid_generator.map(lambda x, y: (x / 255, y))
AUTOTUNE = tf.data.AUTOTUNE
self.valid_generator = self.valid_generator.cache().prefetch(
buffer_size=AUTOTUNE
)
@staticmethod
def load_model(path: Path) -> tf.keras.Model:
return tf.keras.models.load_model(path)
def evaluation(self):
self.model = self.load_model(self.config.path_of_model)
self._valid_generator()
self.score = self.model.evaluate(self.valid_generator)
self.save_score()
def save_score(self):
scores = {"loss": self.score[0], "accuracy": self.score[1]}
save_json(path=Path("scores.json"), data=scores)
def log_into_mlflow(self):
mlflow.set_registry_uri(self.config.mlflow_uri)
tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
with mlflow.start_run():
mlflow.log_params(self.config.all_params)
mlflow.log_metrics({"loss": self.score[0], "accuracy": self.score[1]})
# Model registry does not work with file store
if tracking_url_type_store != "file":
mlflow.keras.log_model(
self.model, "model", registered_model_name="VGG16Model"
)
else:
mlflow.keras.log_model(self.model, "model")
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