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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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"""Classifier Context."""
from __future__ import annotations
from typing import TYPE_CHECKING, List, Optional
import tensorflow as tf # pylint: disable=import-error
from ashpy.contexts.base_context import BaseContext
from ashpy.modes import LogEvalMode
if TYPE_CHECKING:
from ashpy.losses.executor import Executor
from ashpy.metrics import Metric
[docs]class ClassifierContext(BaseContext):
""":py:class:`ashpy.ClassifierContext` provide the standard functions to test a classifier."""
[docs] def __init__(
self,
classifier_model: tf.keras.Model = None,
loss: Executor = None, # ?: Do we really need to default these values to None?
dataset: tf.data.Dataset = None,
metrics: List[Metric] = None,
log_eval_mode: LogEvalMode = LogEvalMode.TEST,
global_step: tf.Variable = tf.Variable(
0, name="global_step", trainable=False, dtype=tf.int64
),
ckpt: tf.train.Checkpoint = None,
) -> None:
r"""
Instantiate the :py:class:`ashpy.contexts.classifier.ClassifierContext` context.
Args:
classifier_model (:py:class:`tf.keras.Model`): A :py:class:`tf.keras.Model`
model.
loss (:py:class:`ashpy.losses.Executor`): Loss function, format f(y_true, y_pred).
dataset (:py:class:`tf.data.Dataset`): The test dataset.
metrics (:obj:`list` of [:py:class:`ashpy.metrics.metric.Metric`]): List of
:py:class:`ashpy.metrics.metric.Metric` with which to measure training
and validation data performances.
log_eval_mode (:py:obj:`ashpy.modes.LogEvalMode`): Models' mode to use when
evaluating and logging.
global_step (:py:obj:`tf.Variable`): tf.Variable that keeps track of the
training steps.
ckpt (:py:class:`tf.train.Checkpoint`): checkpoint to use to keep track of
models status.
"""
super().__init__(metrics, dataset, log_eval_mode, global_step, ckpt)
self._classifier_model = classifier_model
self._loss = loss
@property
def loss(self) -> Optional[Executor]:
"""Retrieve the loss value."""
return self._loss
@property
def classifier_model(self) -> tf.keras.Model:
r"""
Retrieve the Model Object.
Returns:
:py:class:`tf.keras.Model`.
"""
return self._classifier_model