classifier¶

Classifier Context.

Classes

ClassifierContext ashpy.ClassifierContext provide the standard functions to test a classifier.

class ashpy.contexts.classifier.ClassifierContext(classifier_model=None, loss=None, dataset=None, metrics=None, log_eval_mode=<LogEvalMode.TEST: 1>, global_step=<tf.Variable 'global_step:0' shape=() dtype=int64, numpy=0>, checkpoint=None)[source]¶

Bases: ashpy.contexts.context.Context

ashpy.ClassifierContext provide the standard functions to test a classifier.

__init__(classifier_model=None, loss=None, dataset=None, metrics=None, log_eval_mode=<LogEvalMode.TEST: 1>, global_step=<tf.Variable 'global_step:0' shape=() dtype=int64, numpy=0>, checkpoint=None)[source]¶

Instantiate the ashpy.contexts.classifier.ClassifierContext context.

Parameters:

classifier_model (tf.keras.Model) – A tf.keras.Model model.
loss (ashpy.losses.executor.Executor) – Loss function, format f(y_true, y_pred).
dataset (tf.data.Dataset) – The test dataset.
metrics (list of [ashpy.metrics.metric.Metric]) – List of ashpy.metrics.metric.Metric with which to measure training and validation data performances.
log_eval_mode (ashpy.modes.LogEvalMode) – Models’ mode to use when evaluating and logging.
global_step (tf.Variable) – tf.Variable that keeps track of the training steps.
checkpoint (tf.train.Checkpoint) – checkpoint to use to keep track of models status.

Return type:

None

classifier_model¶

Retrieve the Model Object.

Return type:	`Model`
Returns:	`tf.keras.Model` – The classifier model.

loss¶

Retrieve the loss value.

Return type:	Optional[Executor]

training_set¶

Return the training set.

Return type:	`DatasetV2`
Returns:	`tf.data.Dataset` – The training set.

validation_set¶

Return the validation set.

Return type:	`Optional`[`DatasetV2`]
Returns:	`tf.data.Dataset` – The validation set.