BaseContext

Inheritance Diagram

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class ashpy.contexts.base_context.BaseContext(metrics=None, dataset=None, log_eval_mode=<LogEvalMode.TEST: 0>, global_step=<tf.Variable 'global_step:0' shape=() dtype=int64, numpy=0>, ckpt=None)

Bases: object

ashpy.contexts.BaseContext provide an interface for all contexts.

Methods

__init__([metrics, dataset, log_eval_mode, …])	Initialize the Context.
measure_metrics()	Measure the metrics.
model_selection()	Use the metrics to perform model selection.

Attributes

dataset	Retrieve the dataset.
global_step	Retrieve the global_step.
log_eval_mode	Retrieve model(s) mode.
metrics	Retrieve the metrics.

__init__(metrics=None, dataset=None, log_eval_mode=<LogEvalMode.TEST: 0>, global_step=<tf.Variable 'global_step:0' shape=() dtype=int64, numpy=0>, ckpt=None)

Initialize the Context.

Parameters

• metrics (list of [ashpy.metrics.metric.Metric]) – List of ashpy.metrics.metric.Metric objects.

• dataset (tf.data.Dataset) – The dataset to use, that contains everything needed to use the model in this context.

• log_eval_mode (ashpy.modes.LogEvalMode) – Models’ mode to use when evaluating and logging.

• global_step (tf.Variable) – Keeps track of the training steps.

• ckpt (tf.train.Checkpoint) – Checkpoint to use to keep track of models status.

Return type

None

_validate_metrics()

Check if every metric is an ashpy.metrics.Metric.

property dataset

Retrieve the dataset.

Return type

DatasetV2

Returns

tf.data.Dataset.

property global_step

Retrieve the global_step.

Return type

Variable

Returns

tf.Variable.

property log_eval_mode

Retrieve model(s) mode.

Return type

LogEvalMode

Returns

ashpy.modes.LogEvalMode.

measure_metrics()

Measure the metrics.

Return type

None

property metrics

Retrieve the metrics.

Return type

List[Metric]

Returns

list of [ashpy.metrics.metric.Metric].

model_selection()

Use the metrics to perform model selection.

Return type

None