tf.contrib.gan.gan_model( generator_fn, discriminator_fn, real_data, generator_inputs, generator_scope='Generator', discriminator_scope='Discriminator', check_shapes=True )
Defined in tensorflow/contrib/gan/python/train.py
.
Returns GAN model outputs and variables.
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.A GANModel namedtuple.
ValueError
: If the generator outputs a Tensor that isn't the same shape as real_data
.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/gan/gan_model