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.
generator_fn: A python lambda that takes
generator_inputsas inputs and returns the outputs of the GAN generator.
discriminator_fn: A python lambda that takes
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.
True, check that generator produces Tensors that are the same shape as real data. Otherwise, skip this check.
A GANModel namedtuple.
ValueError: If the generator outputs a Tensor that isn't the same shape as
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