plt.ylim(-5, 5)
plt.gca().set_aspect('equal', adjustable='box')
ut.load_model_by_name(model,
opt.eval_model,
global_step=opt.eval_model_iter)
model.eval()
os.makedirs(os.path.join("plots", opt.dataset), exist_ok=True)
results = defaultdict(list)
is_full = set()
i = 0
for x, y in train_gen:
if len(is_full) > 1 and len(is_full) == len(results):
break
i += 1
z = model.encode(x.to(device), y.to(device)).cpu().data.numpy()
x_np = x.cpu().data.numpy()
y_np = y.cpu().data.numpy()
for cls, latent in zip(y_np, z):
cls = cls[0]
if len(results[cls]) < opt.latent_max:
results[cls].append(latent)
elif len(results[cls]) == opt.latent_max:
is_full.add(cls)
latent_list = []
pca = PCA(n_components=2)
init_figure()
for cls, latent in results.items():
latent = np.stack(latent, axis=0)
latent_list.append(latent)
latent_r = pca.fit_transform(latent)
for i in range(predictions.shape[0]):
plt.subplot(4, 4, i + 1)
plt.imshow(predictions[i, :, :, 0], cmap='gray')
plt.axis('off')
file_dir = './image/'+ dire
if not os.path.exists(file_dir):
os.makedirs(file_dir)
plt.savefig(file_dir +'/image_at_epoch_{:04d}.png'.format(degree))
plt.close()
def generate_images(model, data):
fig = plt.figure(figsize=(4, 4))
for i in range(data.shape[0]):
plt.subplot(4, 4, i + 1)
plt.imshow(data[i, :, :, 0], cmap='gray')
plt.axis('off')
plt.show()
for i in range(1, 6):
model = CVAE(latent_dim=16, beta=i)
checkpoint = tf.train.Checkpoint(model=model)
checkpoint.restore("checkpoints/2_20method" + str(i) + "/ckpt-10")
mean, logvar = model.encode(test_sample)
r_m = np.identity(model.latent_dim)
z = model.reparameterize(mean, logvar)
theta = np.radians(60)
c, s = np.cos(theta), np.sin(theta)
r_m[0, [0, 1]], r_m[1, [0, 1]] = [c, s], [-s, c]
rota_z = matvec(tf.cast(r_m, dtype=tf.float32), z)
phi_z = model.decode(rota_z)
generate_and_save_images(phi_z, 1, 'test3' + "/beta_test" + str(i))