AdversarialTrainer

Inheritance Diagram

Inheritance diagram of ashpy.trainers.gan.AdversarialTrainer

class ashpy.trainers.gan.AdversarialTrainer(generator, discriminator, generator_optimizer, discriminator_optimizer, generator_loss, discriminator_loss, epochs, metrics=None, logdir='/home/docs/checkouts/readthedocs.org/user_builds/ashpy/checkouts/latest/docs/source/log', post_process_callback=None, log_eval_mode=<LogEvalMode.TEST: 0>, global_step=<tf.Variable 'global_step:0' shape=() dtype=int64, numpy=0>)[source]

Bases: ashpy.trainers.base_trainer.BaseTrainer

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

Examples

import shutil
import operator
from ashpy.models.gans import Generator, Discriminator
from ashpy.metrics import InceptionScore
from ashpy.losses.gan import DiscriminatorMinMax, GeneratorBCE

generator = Generator(
    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 = Discriminator(
    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 = GeneratorBCE()
minmax = 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 = [
    InceptionScore(
        # Fake inception model
        Discriminator(
            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 = AdversarialTrainer(
    generator,
    discriminator,
    tf.optimizers.Adam(1e-4),
    tf.optimizers.Adam(1e-4),
    generator_bce,
    minmax,
    epochs,
    metrics,
    logdir,
)

# Dataset
noise_dataset = tf.data.Dataset.from_tensors(0).repeat().map(
    lambda _: tf.random.normal(shape=(100,), dtype=tf.float32, mean=0.0, stddev=1)
).batch(batch_size).prefetch(1)

# 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.
[1] Saved checkpoint: testlog/adversarial/ckpts/ckpt-1
Epoch 1 completed.
[2] Saved checkpoint: testlog/adversarial/ckpts/ckpt-2
Epoch 2 completed.

Methods

__init__(generator, discriminator, …[, …])

Instantiate a AdversarialTrainer.

call(dataset)

Perform the adversarial training.

train_step(real_xy, g_inputs)

Train step for the AdversarialTrainer.

__init__(generator, discriminator, generator_optimizer, discriminator_optimizer, generator_loss, discriminator_loss, epochs, metrics=None, logdir='/home/docs/checkouts/readthedocs.org/user_builds/ashpy/checkouts/latest/docs/source/log', post_process_callback=None, log_eval_mode=<LogEvalMode.TEST: 0>, global_step=<tf.Variable 'global_step:0' shape=() dtype=int64, numpy=0>)[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 – (List): list of tf.metrics to measure on training and validation data

  • logdir – checkpoint and log directory.

  • post_process_callback (callable) – the function to postprocess the model output, if needed

  • log_eval_mode – models’ mode to use when evaluating and logging.

  • global_step – tf.Variable that keeps track of the training steps.

Returns

None

_measure_performance(dataset)[source]

Measure performance on dataset.

Parameters

dataset (tf.data.Dataset) –

_train_step[source]

Training step with the distribution strategy.

call(dataset)[source]

Perform the adversarial training.

Parameters

dataset (tf.data.Dataset) – The adversarial training dataset.

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.