def plot_samples(loop):
with loop.timeit('plot_time'):
images = session.run(x_plots)
save_images_collection(images=images,
filename='plotting/{}.png'.format(
loop.epoch),
grid_size=(10, 10))
def plot_samples(loop):
with loop.timeit('plot_time'):
images = session.run(x_plots, feed_dict={is_training: False})
save_images_collection(images=images,
filename=results.prepare_parent(
'plotting/{}.png'.format(loop.epoch)),
grid_size=(10, 10))
def plot_samples(loop):
with loop.timeit('plot_time'):
images = session.run(x_plots, feed_dict={is_training: False})
save_images_collection(images=images,
filename='plotting/{}.png'.format(
loop.epoch),
grid_size=(config.n_clusters, 10),
results=results)
def plot_samples(loop):
with loop.timeit('plot_time'):
session = get_default_session_or_error()
images = session.run(x_plots, feed_dict={is_training: False})
save_images_collection(images=images,
filename='plotting/{}.png'.format(
loop.epoch),
grid_size=(10, 10),
results=results)
def plot_samples(loop):
with loop.timeit('plot_time'):
images = session.run(x_plots)
save_images_collection(
images=images,
filename='plotting/{}.png'.format(loop.epoch),
grid_size=(10, 10),
results=results,
channels_last=config.channels_last,
)
def plot_samples(loop):
with loop.timeit('plot_time'):
session = spt.utils.get_default_session_or_error()
images = session.run(x_plots)
save_images_collection(
images=images,
filename='plotting/{}.png'.format(loop.epoch),
grid_size=(10, 10),
results=results
)
def plot_samples(loop):
with loop.timeit('plot_time'):
try:
# plot reconstructs
for [x] in reconstruct_train_flow:
x_samples = uniform_sampler.sample(x)
images = np.zeros((300,) + config.x_shape, dtype=np.uint8)
images[::3, ...] = np.round(256.0 * x / 2 + 127.5)
images[1::3, ...] = np.round(256.0 * x_samples / 2 + 127.5)
images[2::3, ...] = np.round(session.run(
reconstruct_plots, feed_dict={input_x: x}))
batch_reconstruct_z = session.run(reconstruct_z, feed_dict={input_x: x})
# print(np.mean(batch_reconstruct_z ** 2, axis=-1))
save_images_collection(
images=images,
filename='plotting/train.reconstruct/{}.png'.format(loop.epoch),
grid_size=(20, 15),
results=results,
)
break
# plot reconstructs
for [x] in reconstruct_test_flow:
x_samples = uniform_sampler.sample(x)
images = np.zeros((300,) + config.x_shape, dtype=np.uint8)
images[::3, ...] = np.round(256.0 * x / 2 + 127.5)
images[1::3, ...] = np.round(256.0 * x_samples / 2 + 127.5)
images[2::3, ...] = np.round(session.run(
reconstruct_plots, feed_dict={input_x: x}))
save_images_collection(
images=images,
filename='plotting/test.reconstruct/{}.png'.format(loop.epoch),
grid_size=(20, 15),
results=results,
)
break
# plot samples
[images, gan_images, batch_history_e_z, batch_history_z, batch_history_pure_e_z,
batch_history_ratio] = session.run(
[x_plots, gan_plots, plot_history_e_z, plot_history_z, plot_history_pure_e_z, plot_history_ratio])
# print(batch_history_e_z)
# print(np.mean(batch_history_z ** 2, axis=-1))
# print(batch_history_pure_e_z)
# print(batch_history_ratio)
save_images_collection(
images=np.round(gan_images),
filename='plotting/sample/gan-{}.png'.format(loop.epoch),
grid_size=(10, 10),
results=results,
)
save_images_collection(
images=np.round(images),
filename='plotting/sample/{}.png'.format(loop.epoch),
grid_size=(10, 10),
results=results,
)
except Exception as e:
print(e)
def plot_samples(loop):
with loop.timeit('plot_time'):
# plot samples
images = session.run(x_plots)
# pyplot.scatter(z_points[:, 0], z_points[:, 1], s=5)
# pyplot.savefig(results.system_path('plotting/z_plot/{}.pdf'.format(loop.epoch)))
# pyplot.close()
# print(images)
try:
print(np.max(images), np.min(images))
images = np.round(images)
save_images_collection(
images=images,
filename='plotting/sample/{}.png'.format(loop.epoch),
grid_size=(10, 10),
results=results,
)
# plot reconstructs
for [x] in reconstruct_train_flow:
x_samples = uniform_sampler.sample(x)
images = np.zeros((150, ) + config.x_shape, dtype=np.uint8)
images[::3, ...] = np.round(256.0 * x / 2 + 127.5)
images[1::3, ...] = np.round(256.0 * x_samples / 2 + 127.5)
images[2::3, ...] = np.round(
session.run(reconstruct_plots, feed_dict={input_x: x}))
save_images_collection(
images=images,
filename='plotting/train.reconstruct/{}.png'.format(
loop.epoch),
grid_size=(10, 15),
results=results,
)
break
# plot reconstructs
for [x] in reconstruct_test_flow:
x_samples = uniform_sampler.sample(x)
images = np.zeros((150, ) + config.x_shape, dtype=np.uint8)
images[::3, ...] = np.round(256.0 * x / 2 + 127.5)
images[1::3, ...] = np.round(256.0 * x_samples / 2 + 127.5)
images[2::3, ...] = np.round(
session.run(reconstruct_plots, feed_dict={input_x: x}))
save_images_collection(
images=images,
filename='plotting/test.reconstruct/{}.png'.format(
loop.epoch),
grid_size=(10, 15),
results=results,
)
break
except Exception as e:
print(e)
