Source code for ashpy.contexts.classifier

# Copyright 2019 Zuru Tech HK Limited. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
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"""Classifier Context."""

from __future__ import annotations

from typing import TYPE_CHECKING, Optional, Tuple

import tensorflow as tf  # pylint: disable=import-error
from ashpy.contexts.context import Context
from ashpy.modes import LogEvalMode

    from ashpy.losses.executor import Executor
    from ashpy.metrics import Metric
    from ashpy.losses import ClassifierLoss

[docs]class ClassifierContext(Context): """:py:class:`ashpy.ClassifierContext` provide the standard functions to test a classifier."""
[docs] def __init__( self, classifier_model: tf.keras.Model = None, loss: ClassifierLoss = None, # ?: Do we really need to default these values to None? dataset: = None, metrics: Tuple[Metric] = None, log_eval_mode: LogEvalMode = LogEvalMode.TEST, global_step: tf.Variable = tf.Variable( 0, name="global_step", trainable=False, dtype=tf.int64 ), checkpoint: 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.Executor`): Loss function, format f(y_true, y_pred). dataset (:py:class:``): 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. checkpoint (:py:class:`tf.train.Checkpoint`): checkpoint to use to keep track of models status. """ super().__init__(metrics, dataset, log_eval_mode, global_step, checkpoint) self._classifier_model = classifier_model self._loss = loss self._validation_set: Optional[] = None self._training_set: Optional[] = None
@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`: The classifier model. """ return self._classifier_model @property def validation_set(self) -> Optional[]: """ Return the validation set. Returns: :py:class:``: The validation set. """ return self._validation_set @validation_set.setter def validation_set(self, _validation_set: -> None: """ Set for the validation set. Args: _validation_set (:py:class:``): validation set. """ self._validation_set = _validation_set @property def training_set(self) -> """ Return the training set. Returns: :py:class:``: The training set. """ return self._training_set @training_set.setter def training_set(self, _training_set: -> None: """ Set the training set. Args: _training_set ( :py:class:``): training set to set. """ self._training_set = _training_set