def plot_16_original_and_recon(self, img_original):
n = img_original.shape[1]
m = int(np.sqrt(n))
img_shape = (m, m)
fig = utils.plot_16_images_2d_and_return(img_original, img_shape=img_shape)
plt.show(fig)
img_recon = self.recon(img_original)
fig = utils.plot_16_images_2d_and_return(img_recon, img_shape=img_shape)
plt.show(fig)
def plot_16_loading_vectors(self):
z = np.zeros(shape=(16, self.dim_z))
for i in range(16):
if i < self.dim_z:
z[i, i] = 1
feed_dict = {self.z: z}
images = self.sess.run(self.x_recon, feed_dict=feed_dict)
n = images.shape[1]
m = int(np.sqrt(n))
img_shape = (m, m)
fig = utils.plot_16_images_2d_and_return(images, img_shape=img_shape)
plt.show(fig)
def plot_16_generated(self, figure_index=0):
if not os.path.exists(self.figure_save_dir):
os.makedirs(self.figure_save_dir)
z = np.random.normal(0., 1., size=(16, self.dim_z))
feed_dict = {self.z: z}
images = self.sess.run(self.x_recon, feed_dict=feed_dict)
n = images.shape[1]
m = int(np.sqrt(n))
img_shape = (m, m)
fig = utils.plot_16_images_2d_and_return(images, img_shape=img_shape)
fig.savefig(self.figure_save_dir + '/{}.png'.format(figure_index), bbox_inches='tight')
plt.close(fig)
