gan

Collection of GANs trainers.

Classes

AdversarialTrainer

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

EncoderTrainer

Primitive Trainer for GANs using an Encoder sub-network.

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

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

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 (Optional[List[Metric]]) – (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 (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

_train_step[source]

Training step with the distribution strategy.

call(dataset, log_freq=10, measure_performance_freq=10)[source]

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)[source]

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.

class ashpy.trainers.gan.EncoderTrainer(generator, discriminator, encoder, generator_optimizer, discriminator_optimizer, encoder_optimizer, generator_loss, discriminator_loss, encoder_loss, epochs, metrics=None, callbacks=None, logdir='/home/docs/checkouts/readthedocs.org/user_builds/ashpy/checkouts/latest/docs/source/log', log_eval_mode=<LogEvalMode.TEST: 1>, global_step=None)[source]

Bases: ashpy.trainers.gan.AdversarialTrainer

Primitive Trainer for GANs using an Encoder sub-network.

The implementation is thought to be used with the BCE losses. To use another loss function consider subclassing the model and overriding the train_step method.

Examples

import shutil
import operator

def real_gen():
    label = 0
    for _ in tf.range(100):
        yield ((10.0,), (label,))

latent_dim = 100

generator = tf.keras.Sequential([tf.keras.layers.Dense(1)])

left_input = tf.keras.layers.Input(shape=(1,))
left = tf.keras.layers.Dense(10, activation=tf.nn.elu)(left_input)

right_input = tf.keras.layers.Input(shape=(latent_dim,))
right = tf.keras.layers.Dense(10, activation=tf.nn.elu)(right_input)

net = tf.keras.layers.Concatenate()([left, right])
out = tf.keras.layers.Dense(1)(net)

discriminator = tf.keras.Model(inputs=[left_input, right_input], outputs=[out])

encoder = tf.keras.Sequential([tf.keras.layers.Dense(latent_dim)])

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

epochs = 2

# Fake pre-trained classifier
num_classes = 1
classifier = tf.keras.Sequential(
    [tf.keras.layers.Dense(10), tf.keras.layers.Dense(num_classes)]
)

logdir = "testlog/adversarial_encoder"

if os.path.exists(logdir):
    shutil.rmtree(logdir)

metrics = [
    metrics.gan.EncodingAccuracy(
        classifier,
        # model_selection_operator=operator.gt,
        logdir=logdir
    )
]

trainer = trainers.gan.EncoderTrainer(
    generator=generator,
    discriminator=discriminator,
    encoder=encoder,
    discriminator_optimizer=tf.optimizers.Adam(1e-4),
    generator_optimizer=tf.optimizers.Adam(1e-5),
    encoder_optimizer=tf.optimizers.Adam(1e-6),
    generator_loss=generator_bce,
    discriminator_loss=minmax,
    encoder_loss=encoder_bce,
    epochs=epochs,
    metrics=metrics,
    logdir=logdir,
)

batch_size = 10
discriminator_input = tf.data.Dataset.from_generator(
    real_gen, (tf.float32, tf.int64), ((1), (1))
).batch(batch_size)

dataset = discriminator_input.map(
    lambda x, y: ((x, y), tf.random.normal(shape=(batch_size, latent_dim)))
)

trainer(dataset)

shutil.rmtree(logdir)
Initializing checkpoint.
Starting epoch 1.
[10] Saved checkpoint: testlog/adversarial_encoder/ckpts/ckpt-1
Epoch 1 completed.
Starting epoch 2.
[20] Saved checkpoint: testlog/adversarial_encoder/ckpts/ckpt-2
Epoch 2 completed.
Training finished after 2 epochs.
__init__(generator, discriminator, encoder, generator_optimizer, discriminator_optimizer, encoder_optimizer, generator_loss, discriminator_loss, encoder_loss, epochs, metrics=None, callbacks=None, logdir='/home/docs/checkouts/readthedocs.org/user_builds/ashpy/checkouts/latest/docs/source/log', log_eval_mode=<LogEvalMode.TEST: 1>, global_step=None)[source]

Instantiate a EncoderTrainer.

Parameters
_train_step[source]

Perform the training step using the distribution strategy.

call(dataset, log_freq=10, measure_performance_freq=10)[source]

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)[source]

Adversarial training step.

Parameters
  • real_xy – input batch as extracted from the discriminator input dataset. (features, label) pair

  • g_inputs – batch of noise as generated by the generator input dataset.

Returns

d_loss, g_loss, e_loss – discriminator, generator, encoder loss values.