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)