def test_get_noise_continuous_dim2_syntax(self):
util.get_eval_noise_continuous_dim2(
noise_samples=4,
categorical_sample_points=np.arange(0, 10),
continuous_sample_points=np.linspace(-2.0, 2.0, 10),
unstructured_noise_dims=62,
continuous_noise_dims=2)
def evaluate(hparams, run_eval_loop=True):
"""Runs an evaluation loop.
Args:
hparams: An HParams instance containing the eval hyperparameters.
run_eval_loop: Whether to run the full eval loop. Set to False for testing.
"""
with tf.name_scope('inputs'):
noise_args = (hparams.noise_samples, CAT_SAMPLE_POINTS,
CONT_SAMPLE_POINTS, hparams.unstructured_noise_dims,
hparams.continuous_noise_dims)
# Use fixed noise vectors to illustrate the effect of each dimension.
display_noise1 = util.get_eval_noise_categorical(*noise_args)
display_noise2 = util.get_eval_noise_continuous_dim1(*noise_args)
display_noise3 = util.get_eval_noise_continuous_dim2(*noise_args)
_validate_noises([display_noise1, display_noise2, display_noise3])
# Visualize the effect of each structured noise dimension on the generated
# image.
def generator_fn(inputs):
return networks.infogan_generator(inputs,
len(CAT_SAMPLE_POINTS),
is_training=False)
with tf.variable_scope(
'Generator') as genscope: # Same scope as in training.
categorical_images = generator_fn(display_noise1)
reshaped_categorical_img = tfgan.eval.image_reshaper(
categorical_images, num_cols=len(CAT_SAMPLE_POINTS))
tf.summary.image('categorical', reshaped_categorical_img, max_outputs=1)
with tf.variable_scope(genscope, reuse=True):
continuous1_images = generator_fn(display_noise2)
reshaped_continuous1_img = tfgan.eval.image_reshaper(
continuous1_images, num_cols=len(CONT_SAMPLE_POINTS))
tf.summary.image('continuous1', reshaped_continuous1_img, max_outputs=1)
with tf.variable_scope(genscope, reuse=True):
continuous2_images = generator_fn(display_noise3)
reshaped_continuous2_img = tfgan.eval.image_reshaper(
continuous2_images, num_cols=len(CONT_SAMPLE_POINTS))
tf.summary.image('continuous2', reshaped_continuous2_img, max_outputs=1)
# Evaluate image quality.
all_images = tf.concat(
[categorical_images, continuous1_images, continuous2_images], 0)
tf.summary.scalar('MNIST_Classifier_score', util.mnist_score(all_images))
# Write images to disk.
image_write_ops = []
if hparams.write_to_disk:
image_write_ops.append(
_get_write_image_ops(hparams.eval_dir, 'categorical_infogan.png',
reshaped_categorical_img[0]))
image_write_ops.append(
_get_write_image_ops(hparams.eval_dir, 'continuous1_infogan.png',
reshaped_continuous1_img[0]))
image_write_ops.append(
_get_write_image_ops(hparams.eval_dir, 'continuous2_infogan.png',
reshaped_continuous2_img[0]))
# For unit testing, use `run_eval_loop=False`.
if not run_eval_loop:
return
evaluation.evaluate_repeatedly(
hparams.checkpoint_dir,
hooks=[
evaluation.SummaryAtEndHook(hparams.eval_dir),
evaluation.StopAfterNEvalsHook(1)
],
eval_ops=image_write_ops,
max_number_of_evaluations=hparams.max_number_of_evaluations)
