Defined in tensorflow/contrib/gan/python/eval/__init__.py.
TFGAN evaluation module.
This module supports techniques such as Inception Score, Frechet Inception distance, and Sliced Wasserstein distance.
Modules
classifier_metrics module: Model evaluation tools for TFGAN.
eval_utils module: Utility file for visualizing generated images.
summaries module: Common TFGAN summaries.
Functions
add_cyclegan_image_summaries(...): Adds image summaries for CycleGAN.
add_gan_model_image_summaries(...): Adds image summaries for real and fake images.
add_gan_model_summaries(...): Adds typical GANModel summaries.
add_image_comparison_summaries(...): Adds image summaries to compare triplets of images.
add_regularization_loss_summaries(...): Adds summaries for a regularization losses..
add_stargan_image_summaries(...): Adds image summaries to see StarGAN image results.
classifier_score(...): Classifier score for evaluating a conditional generative model.
classifier_score_from_logits(...): Classifier score for evaluating a generative model from logits.
diagonal_only_frechet_classifier_distance_from_activations(...): Classifier distance for evaluating a generative model.
frechet_classifier_distance(...): Classifier distance for evaluating a generative model.
frechet_classifier_distance_from_activations(...): Classifier distance for evaluating a generative model.
get_graph_def_from_disk(...): Get a GraphDef proto from a disk location.
get_graph_def_from_resource(...): Get a GraphDef proto from within a .par file.
get_graph_def_from_url_tarball(...): Get a GraphDef proto from a tarball on the web.
image_grid(...): Arrange a minibatch of images into a grid to form a single image.
image_reshaper(...): A reshaped summary image.
kernel_classifier_distance(...): Kernel "classifier" distance for evaluating a generative model.
kernel_classifier_distance_and_std(...): Kernel "classifier" distance for evaluating a generative model.
kernel_classifier_distance_and_std_from_activations(...): Kernel "classifier" distance for evaluating a generative model.
kernel_classifier_distance_from_activations(...): Kernel "classifier" distance for evaluating a generative model.
mean_only_frechet_classifier_distance_from_activations(...): Classifier distance for evaluating a generative model from activations.
preprocess_image(...): Prepare a batch of images for evaluation.
run_image_classifier(...): Runs a network from a frozen graph.
run_inception(...): Run images through a pretrained Inception classifier.
sliced_wasserstein_distance(...): Compute the Wasserstein distance between two distributions of images.