context¶

Primitive Context Interface.

Contexts are checkpointable (subclassed from tf.train.Checkpoint) collections of variable encapsulated in a Python Class as a way to seamlessly handle information transfer.

Classes

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

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

Bases: object

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

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

Initialize the Context.

Parameters:

metrics (tuple 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.
checkpoint (tf.train.Checkpoint) – Checkpoint to use to keep track of models status.

Return type:

None

current_batch¶

Return the current batch.

Return type:	`Optional`[`Tensor`]

dataset¶

Retrieve the dataset.

Return type:	`DatasetV2`
Returns:	`tf.data.Dataset` the current dataset

exception¶

Return the exception.

Return type:	`Optional`[`Exception`]

global_step¶

Retrieve the global_step.

Return type:	`Variable`
Returns:	`tf.Variable`.

log_eval_mode¶

Retrieve model(s) mode.

Return type:	`LogEvalMode`
Returns:	`ashpy.modes.LogEvalMode`.

metrics¶

Retrieve the metrics.

Return type:	`Tuple`[`Metric`]
Returns:	`tuple` of (`ashpy.metrics.metric.Metric`).