GANContext¶

Inheritance Diagram

class ashpy.contexts.gan.GANContext(dataset=None, generator_model=None, discriminator_model=None, generator_loss=None, discriminator_loss=None, metrics=None, log_eval_mode=<LogEvalMode.TRAIN: 2>, global_step=<tf.Variable 'global_step:0' shape=() dtype=int64, numpy=0>, checkpoint=None)[source]¶

Bases: ashpy.contexts.context.Context

ashpy.contexts.gan.GANContext measure the specified metrics on the GAN.

Methods

__init__([dataset, generator_model, …]) Initialize the Context.

Attributes

`current_batch`	Return the current batch.
`dataset`	Retrieve the dataset.
`discriminator_loss`	Retrieve the discriminator loss.
`discriminator_model`	Retrieve the discriminator model.
`exception`	Return the exception.
`fake_samples`	Retrieve the fake samples, i.e.
`generator_inputs`	Retrieve the generator inputs.
`generator_loss`	Retrieve the generator loss.
`generator_model`	Retrieve the generator model.
`global_step`	Retrieve the global_step.
`log_eval_mode`	Retrieve model(s) mode.
`metrics`	Retrieve the metrics.

__init__(dataset=None, generator_model=None, discriminator_model=None, generator_loss=None, discriminator_loss=None, metrics=None, log_eval_mode=<LogEvalMode.TRAIN: 2>, global_step=<tf.Variable 'global_step:0' shape=() dtype=int64, numpy=0>, checkpoint=None)[source]¶

Initialize the Context.

Parameters:

dataset (tf.data.Dataset) – Dataset of tuples. [0] true dataset, [1] generator input dataset.
generator_model (tf.keras.Model) – The generator.
discriminator_model (tf.keras.Model) – The discriminator.
generator_loss (ashpy.losses.Executor()) – The generator loss.
discriminator_loss (ashpy.losses.Executor()) – The discriminator loss.
metrics (list of [ashpy.metrics.metric.Metric]) – All the metrics to be used to evaluate the model.
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

discriminator_loss¶

Retrieve the discriminator loss.

Return type:	Optional[Executor]

discriminator_model¶

Retrieve the discriminator model.

Return type:	`Model`
Returns:	`tf.keras.Model`.

fake_samples¶

Retrieve the fake samples, i.e. output of the generator.

Return type:	`Optional`[`Tensor`]

generator_inputs¶

Retrieve the generator inputs.

Return type:	`Optional`[`Tensor`]

generator_loss¶

Retrieve the generator loss.

Return type:	Optional[Executor]

generator_model¶

Retrieve the generator model.

Return type:	`Model`
Returns:	`tf.keras.Model`.