tf.contrib.gan.GANModel

Class GANModel

Defined in tensorflow/contrib/gan/python/namedtuples.py.

A GANModel contains all the pieces needed for GAN training.

Generative Adversarial Networks (https://arxiv.org/abs/1406.2661) attempt to create an implicit generative model of data by solving a two agent game. The generator generates candidate examples that are supposed to match the data distribution, and the discriminator aims to tell the real examples apart from the generated samples.

Args:

  • generator_inputs: The random noise source that acts as input to the generator.
  • generated_data: The generated output data of the GAN.
  • generator_variables: A list of all generator variables.
  • generator_scope: Variable scope all generator variables live in.
  • generator_fn: The generator function.
  • real_data: A tensor or real data.
  • discriminator_real_outputs: The discriminator's output on real data.
  • discriminator_gen_outputs: The discriminator's output on generated data.
  • discriminator_variables: A list of all discriminator variables.
  • discriminator_scope: Variable scope all discriminator variables live in.
  • discriminator_fn: The discriminator function.

Properties

discriminator_fn

Alias for field number 10

discriminator_gen_outputs

Alias for field number 7

discriminator_real_outputs

Alias for field number 6

discriminator_scope

Alias for field number 9

discriminator_variables

Alias for field number 8

generated_data

Alias for field number 1

generator_fn

Alias for field number 4

generator_inputs

Alias for field number 0

generator_scope

Alias for field number 3

generator_variables

Alias for field number 2

real_data

Alias for field number 5

Methods

__new__

__new__(
    _cls,
    generator_inputs,
    generated_data,
    generator_variables,
    generator_scope,
    generator_fn,
    real_data,
    discriminator_real_outputs,
    discriminator_gen_outputs,
    discriminator_variables,
    discriminator_scope,
    discriminator_fn
)

Create new instance of GANModel(generator_inputs, generated_data, generator_variables, generator_scope, generator_fn, real_data, discriminator_real_outputs, discriminator_gen_outputs, discriminator_variables, discriminator_scope, discriminator_fn)