def main(_, run_eval_loop=True):
# Fetch real images.
with tf.name_scope('inputs'):
real_images, _, _ = data_provider.provide_data(
'train', FLAGS.num_images_generated, FLAGS.dataset_dir)
image_write_ops = None
if FLAGS.eval_real_images:
tf.summary.scalar(
'MNIST_Classifier_score',
util.mnist_score(real_images, FLAGS.classifier_filename))
else:
# In order for variables to load, use the same variable scope as in the
# train job.
with tf.variable_scope('Generator'):
images = networks.unconditional_generator(tf.random_normal(
[FLAGS.num_images_generated, FLAGS.noise_dims]),
is_training=False)
tf.summary.scalar(
'MNIST_Frechet_distance',
util.mnist_frechet_distance(real_images, images,
FLAGS.classifier_filename))
tf.summary.scalar('MNIST_Classifier_score',
util.mnist_score(images, FLAGS.classifier_filename))
if FLAGS.num_images_generated >= 100 and FLAGS.write_to_disk:
reshaped_images = tfgan.eval.image_reshaper(images[:100, ...],
num_cols=10)
uint8_images = data_provider.float_image_to_uint8(reshaped_images)
image_write_ops = tf.write_file(
'%s/%s' % (FLAGS.eval_dir, 'unconditional_gan.png'),
tf.image.encode_png(uint8_images[0]))
# For unit testing, use `run_eval_loop=False`.
if not run_eval_loop: return
tf.contrib.training.evaluate_repeatedly(
FLAGS.checkpoint_dir,
hooks=[
tf.contrib.training.SummaryAtEndHook(FLAGS.eval_dir),
tf.contrib.training.StopAfterNEvalsHook(1)
],
eval_ops=image_write_ops,
max_number_of_evaluations=FLAGS.max_number_of_evaluations)
def main(_, run_eval_loop=True):
# Fetch real images.
with tf.name_scope('inputs'):
real_images, _, _ = data_provider.provide_data(
'train', FLAGS.num_images_generated, FLAGS.dataset_dir)
image_write_ops = None
if FLAGS.eval_real_images:
tf.summary.scalar('MNIST_Classifier_score',
util.mnist_score(real_images, FLAGS.classifier_filename))
else:
# In order for variables to load, use the same variable scope as in the
# train job.
with tf.variable_scope('Generator'):
images = networks.unconditional_generator(
tf.random_normal([FLAGS.num_images_generated, FLAGS.noise_dims]),
is_training=False)
tf.summary.scalar('MNIST_Frechet_distance',
util.mnist_frechet_distance(
real_images, images, FLAGS.classifier_filename))
tf.summary.scalar('MNIST_Classifier_score',
util.mnist_score(images, FLAGS.classifier_filename))
if FLAGS.num_images_generated >= 100 and FLAGS.write_to_disk:
reshaped_images = tfgan.eval.image_reshaper(
images[:100, ...], num_cols=10)
uint8_images = data_provider.float_image_to_uint8(reshaped_images)
image_write_ops = tf.write_file(
'%s/%s'% (FLAGS.eval_dir, 'unconditional_gan.png'),
tf.image.encode_png(uint8_images[0]))
# For unit testing, use `run_eval_loop=False`.
if not run_eval_loop: return
tf.contrib.training.evaluate_repeatedly(
FLAGS.checkpoint_dir,
hooks=[tf.contrib.training.SummaryAtEndHook(FLAGS.eval_dir),
tf.contrib.training.StopAfterNEvalsHook(1)],
eval_ops=image_write_ops,
max_number_of_evaluations=FLAGS.max_number_of_evaluations)
MNIST_CLASSIFIER_FROZEN_GRAPH = 'mnist/classify_mnist_graph_def.pb'
# For variables to load, use the same variable scope as in the train job.
with tf.variable_scope('Generator', reuse=True):
eval_images = gan_model.generator_fn(tf.random_normal(
[num_images_to_eval, noise_dims]),
is_training=False)
# Calculate Inception score.
eval_score = util.mnist_score(eval_images, MNIST_CLASSIFIER_FROZEN_GRAPH)
# Calculate Frechet Inception distance.
with tf.device('/cpu:0'):
real_images, _, _ = data_provider.provide_data('train', num_images_to_eval,
MNIST_DATA_DIR)
frechet_distance = util.mnist_frechet_distance(real_images, eval_images,
MNIST_CLASSIFIER_FROZEN_GRAPH)
# Reshape eval images for viewing.
generated_data_to_visualize = tfgan.eval.image_reshaper(eval_images[:20, ...],
num_cols=10)
# We have the option to train the discriminator more than one step for every
# step of the generator. In order to do this, we use a `GANTrainSteps` with
# desired values. For this example, we use the default 1 generator train step
# for every discriminator train step.
train_step_fn = tfgan.get_sequential_train_steps()
global_step = tf.train.get_or_create_global_step()
loss_values, mnist_scores, frechet_distances = [], [], []
with tf.train.SingularMonitoredSession() as sess: