tf.contrib.gan.infogan_model( generator_fn, discriminator_fn, real_data, unstructured_generator_inputs, structured_generator_inputs, generator_scope='Generator', discriminator_scope='Discriminator' )
Defined in tensorflow/contrib/gan/python/train.py
.
Returns an InfoGAN model outputs and variables.
See https://arxiv.org/abs/1606.03657 for more details.
generator_fn
: A python lambda that takes a list of Tensors 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 2-tuple of (logits, distribution_list). logits
are in the range [-inf, inf], and distribution_list
is a list of Tensorflow distributions representing the predicted noise distribution of the ith structure noise.real_data
: A Tensor representing the real data.unstructured_generator_inputs
: A list of Tensors to the generator. These tensors represent the unstructured noise or conditioning.structured_generator_inputs
: A list of Tensors to the generator. These tensors must have high mutual information with the recognizer.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.An InfoGANModel namedtuple.
ValueError
: If the generator outputs a Tensor that isn't the same shape as real_data
.ValueError
: If the discriminator output is malformed.
© 2018 The TensorFlow Authors. All rights reserved.
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/infogan_model