| 266 | |
| 267 | |
| 268 | def sample_graph(): |
| 269 | |
| 270 | # ====================================== |
| 271 | # = graph = |
| 272 | # ====================================== |
| 273 | |
| 274 | # placeholders & inputs |
| 275 | zs = [tl.truncated_normal([args.n_samples, z_dim], minval=-args.truncation_threshold, maxval=args.truncation_threshold) for z_dim in args.z_dims] |
| 276 | eps = tl.truncated_normal([args.n_samples, args.eps_dim], minval=-args.truncation_threshold, maxval=args.truncation_threshold) |
| 277 | |
| 278 | # generate |
| 279 | x_f = G_test(zs, eps, training=False) |
| 280 | |
| 281 | # ====================================== |
| 282 | # = run function = |
| 283 | # ====================================== |
| 284 | |
| 285 | save_dir = './output/%s/samples_training/sample' % (args.experiment_name) |
| 286 | py.mkdir(save_dir) |
| 287 | |
| 288 | def run(epoch, iter): |
| 289 | x_f_opt = sess.run(x_f) |
| 290 | sample = im.immerge(x_f_opt, n_rows=int(args.n_samples ** 0.5)) |
| 291 | im.imwrite(sample, '%s/Epoch-%d_Iter-%d.jpg' % (save_dir, epoch, iter)) |
| 292 | |
| 293 | return run |
| 294 | |
| 295 | |
| 296 | def traversal_graph(): |