AdversarialTrainer

Inheritance Diagram

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class ashpy.trainers.AdversarialTrainer(generator, discriminator, generator_optimizer, discriminator_optimizer, generator_loss, discriminator_loss, epochs, metrics=None, callbacks=None, logdir=PosixPath('/home/docs/checkouts/readthedocs.org/user_builds/ashpy/checkouts/master/docs/source/log'), log_eval_mode=<LogEvalMode.TEST: 1>, global_step=None)

Bases: ashpy.trainers.trainer.Trainer

Primitive Trainer for GANs subclassed from ashpy.trainers.Trainer.

Examples

import shutil
import operator

generator = models.gans.ConvGenerator(
    layer_spec_input_res=(7, 7),
    layer_spec_target_res=(28, 28),
    kernel_size=(5, 5),
    initial_filters=32,
    filters_cap=16,
    channels=1,
)

discriminator = models.gans.ConvDiscriminator(
    layer_spec_input_res=(28, 28),
    layer_spec_target_res=(7, 7),
    kernel_size=(5, 5),
    initial_filters=16,
    filters_cap=32,
    output_shape=1,
)

# Losses
generator_bce = losses.gan.GeneratorBCE()
minmax = losses.gan.DiscriminatorMinMax()

# Real data
batch_size = 2
mnist_x, mnist_y = tf.zeros((100,28,28)), tf.zeros((100,))

# Trainer
epochs = 2
logdir = "testlog/adversarial"
metrics = [
    metrics.gan.InceptionScore(
        # Fake inception model
        models.gans.ConvDiscriminator(
            layer_spec_input_res=(299, 299),
            layer_spec_target_res=(7, 7),
            kernel_size=(5, 5),
            initial_filters=16,
            filters_cap=32,
            output_shape=10,
        ),
        model_selection_operator=operator.gt,
    )
]
trainer = trainers.gan.AdversarialTrainer(
    generator=generator,
    discriminator=discriminator,
    generator_optimizer=tf.optimizers.Adam(1e-4),
    discriminator_optimizer=tf.optimizers.Adam(1e-4),
    generator_loss=generator_bce,
    discriminator_loss=minmax,
    epochs=epochs,
    metrics=metrics,
    logdir=logdir,
)

# take only 2 samples to speed up tests
real_data = (
    tf.data.Dataset.from_tensor_slices(
    (tf.expand_dims(mnist_x, -1), tf.expand_dims(mnist_y, -1))).take(batch_size)
    .batch(batch_size)
    .prefetch(1)
)

# Add noise in the same dataset, just by mapping.
# The return type of the dataset must be: tuple(tuple(a,b), noise)
dataset = real_data.map(
    lambda x, y: ((x, y), tf.random.normal(shape=(batch_size, 100)))
)

trainer(dataset)
shutil.rmtree(logdir)

Initializing checkpoint.
Starting epoch 1.
[1] Saved checkpoint: testlog/adversarial/ckpts/ckpt-1
Epoch 1 completed.
Starting epoch 2.
[2] Saved checkpoint: testlog/adversarial/ckpts/ckpt-2
Epoch 2 completed.
Training finished after 2 epochs.

Methods

__init__(generator, discriminator, …[, …])	Instantiate a AdversarialTrainer.
call(dataset[, log_freq, …])	Perform the adversarial training.
train_step(real_xy, g_inputs)	Train step for the AdversarialTrainer.

Attributes

ckpt_id_callbacks
ckpt_id_discriminator
ckpt_id_generator
ckpt_id_global_step
ckpt_id_optimizer_discriminator
ckpt_id_optimizer_generator
ckpt_id_steps_per_epoch
context	Return the training context.

__init__(generator, discriminator, generator_optimizer, discriminator_optimizer, generator_loss, discriminator_loss, epochs, metrics=None, callbacks=None, logdir=PosixPath('/home/docs/checkouts/readthedocs.org/user_builds/ashpy/checkouts/master/docs/source/log'), log_eval_mode=<LogEvalMode.TEST: 1>, global_step=None)

Instantiate a AdversarialTrainer.

Parameters:

• generator (tf.keras.Model) – A tf.keras.Model describing the Generator part of a GAN.
• discriminator (tf.keras.Model) – A tf.keras.Model describing the Discriminator part of a GAN.
• generator_optimizer (tf.optimizers.Optimizer) – A tf.optimizers to use for the Generator.
• discriminator_optimizer (tf.optimizers.Optimizer) – A tf.optimizers to use for the Discriminator.
• generator_loss (ashpy.losses.executor.Executor) – A ash Executor to compute the loss of the Generator.
• discriminator_loss (ashpy.losses.executor.Executor) – A ash Executor to compute the loss of the Discriminator.
• epochs (int) – number of training epochs.
• metrics (Union[Tuple[Metric], List[Metric], None]) – (List): list of ashpy.metrics.Metric to measure on training and validation data.
• callbacks (List) – list of ashpy.callbacks.Callback to measure on training and validation data.
• logdir (Union[Path, str]) – checkpoint and log directory.
• log_eval_mode (LogEvalMode) – models’ mode to use when evaluating and logging.
• global_step (Optional[tf.Variable]) – tf.Variable that keeps track of the training steps.

Returns:

None

_build_and_restore_models(dataset)

Build and restore a Subclassed model by firstly calling it on some data.

_train_step

Training step with the distribution strategy.

call(dataset, log_freq=10, measure_performance_freq=10)

Perform the adversarial training.

Parameters:

• dataset (tf.data.Dataset) – The adversarial training dataset.
• log_freq (int) – Specifies how many steps to run before logging the losses, e.g. log_frequency=10 logs every 10 steps of training. Pass log_frequency<=0 in case you don’t want to log.
• measure_performance_freq (int) –

  Specifies how many steps to run before measuring the performance, e.g. measure_performance_freq=10 measures performance every 10 steps of training. Pass measure_performance_freq<=0 in case you don’t want to measure

  performance.

train_step(real_xy, g_inputs)

Train step for the AdversarialTrainer.

Parameters:

• real_xy – input batch as extracted from the input dataset. (features, label) pair.
• g_inputs – batch of generator_input as generated from the input dataset.

Returns:

d_loss, g_loss, fake –

discriminator, generator loss values. fake is the

generator output.