# tf.contrib.gan.gan_model

tf.contrib.gan.gan_model(
generator_fn,
discriminator_fn,
real_data,
generator_inputs,
generator_scope='Generator',
discriminator_scope='Discriminator',
check_shapes=True
)


Returns GAN model outputs and variables.

#### Args:

• generator_fn: A python lambda that takes generator_inputs as inputs and returns the outputs of the GAN generator.
• discriminator_fn: A python lambda that takes real_data/generated data and generator_inputs. Outputs a Tensor in the range [-inf, inf].
• real_data: A Tensor representing the real data.
• generator_inputs: A Tensor or list of Tensors to the generator. In the vanilla GAN case, this might be a single noise Tensor. In the conditional GAN case, this might be the generator's conditioning.
• generator_scope: Optional generator variable scope. Useful if you want to reuse a subgraph that has already been created.
• discriminator_scope: Optional discriminator variable scope. Useful if you want to reuse a subgraph that has already been created.
• check_shapes: If True, check that generator produces Tensors that are the same shape as real data. Otherwise, skip this check.

#### Returns:

A GANModel namedtuple.

#### Raises:

• ValueError: If the generator outputs a Tensor that isn't the same shape as real_data.