def main():
images, labels = dataset.load_test_images()
num_scatter = len(images)
_images, _, label_id = dataset.sample_labeled_data(images, labels,
num_scatter)
with tf.device(config.device):
t = build_graph(is_test=True)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=True)) as sess:
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(config.ckpt_dir))
z, _x = sess.run(
[t.z_r, t.x_r],
feed_dict={
t.x: _images,
t.z_prior:
np.random.normal(size=(len(_images), config.ndim_noise))
})
plot.scatter_labeled_z(z, label_id, dir=config.ckpt_dir)
plot.tile_images(_x[:100], dir=config.ckpt_dir)
plot.plot_loss_tendency(config.ckpt_dir)
hist_value, hist_head = plot.load_pickle_to_data(config.ckpt_dir)
for loss_name in ['reconstruction']:
plot.plot_loss_trace(hist_value[loss_name], loss_name, config.ckpt_dir)
plot.plot_adversarial_trace(hist_value['discriminator'],
hist_value['generator'], 'z', config.ckpt_dir)
def main():
# load MNIST images
images, labels = dataset.load_test_images()
# Settings
num_scatter = len(images)
_images, _, label_id = dataset.sample_labeled_data(images, labels,
num_scatter)
with tf.device(config.device):
t = build_graph(is_test=True)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=True)) as sess:
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(config.ckpt_dir))
representation, x_reconstruction = sess.run([t.yz, t.x_r],
feed_dict={t.x: _images})
plot.scatter_labeled_z(representation, label_id, dir=config.ckpt_dir)
plot.tile_images(x_reconstruction[:100], dir=config.ckpt_dir)
hist_value, hist_head = plot.load_pickle_to_data(config.ckpt_dir)
for loss_name in ['reconstruction']:
plot.plot_loss_trace(hist_value[loss_name], loss_name,
config.ckpt_dir)
plot.plot_adversarial_trace(hist_value['discriminator_y'],
hist_value['generator_y'], 'y',
config.ckpt_dir)
plot.plot_adversarial_trace(hist_value['discriminator_z'],
hist_value['generator_z'], 'z',
config.ckpt_dir)
def main():
# load MNIST images
images, labels = dataset.load_test_images()
# Settings
num_anologies = 10
pylab.gray()
# generate style vector z
x = dataset.sample_unlabeled_data(images, num_anologies)
x = (x + 1) / 2
with tf.device(config.device):
x_input, img_y, img_z, reconstruction = build_graph(is_test=True)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=True)) as sess:
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(config.ckpt_dir))
z = sess.run(img_z, feed_dict={x_input: x})
for m in range(num_anologies):
pylab.subplot(num_anologies, config.ndim_y + 2, m * 12 + 1)
pylab.imshow(x[m].reshape((28, 28)), interpolation='none')
pylab.axis('off')
all_y = np.identity(config.ndim_y, dtype=np.float32)
for m in range(num_anologies):
fixed_z = np.repeat(z[m].reshape(1, -1), config.ndim_y, axis=0)
gen_x = sess.run(reconstruction,
feed_dict={
img_z: fixed_z,
img_y: all_y
})
gen_x = (gen_x + 1) / 2
for n in range(config.ndim_y):
pylab.subplot(num_anologies, config.ndim_y + 2, m * 12 + 3 + n)
pylab.imshow(gen_x[n].reshape((28, 28)), interpolation='none')
pylab.axis('off')
fig = pylab.gcf()
fig.set_size_inches(num_anologies, config.ndim_y)
pylab.savefig('{}/analogy.png'.format(config.ckpt_dir))
hist_value, hist_head = plot.load_pickle_to_data(config.ckpt_dir)
for loss_name in [
'reconstruction', 'validation_accuracy', 'supervised'
]:
plot.plot_loss_trace(hist_value[loss_name], loss_name,
config.ckpt_dir)
plot.plot_adversarial_trace(hist_value['discriminator_y'],
hist_value['generator_y'], 'y',
config.ckpt_dir)
plot.plot_adversarial_trace(hist_value['discriminator_z'],
hist_value['generator_z'], 'z',
config.ckpt_dir)
def main():
# load MNIST images
images, labels = dataset.load_test_images()
# Settings
num_scatter = len(images)
_images, _, label_id = dataset.sample_labeled_data(images, labels,
num_scatter)
with tf.device(config.device):
t = build_graph(is_test=True)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=True)) as sess:
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(config.ckpt_dir))
representation, x_reconstruction = sess.run([t.yz, t.x_r],
feed_dict={t.x: _images})
plot.scatter_labeled_z(representation, label_id, dir=config.ckpt_dir)
plot.tile_images(x_reconstruction[:100], dir=config.ckpt_dir)
# z distributed plot
num_segments = 20
limit = (-5, 5)
x_values = np.linspace(limit[0], limit[1], num_segments)
y_values = np.linspace(limit[0], limit[1], num_segments)
vacant = np.zeros((28 * num_segments, 28 * num_segments))
for i, x_element in enumerate(x_values):
for j, y_element in enumerate(y_values):
x_reconstruction = sess.run(
t.x_r,
feed_dict={
t.yz: np.reshape([x_element, y_element], [1, 2])
})
vacant[(num_segments - 1 - i) * 28:(num_segments - i) * 28,
j * 28:(j + 1) * 28] = x_reconstruction.reshape(28, 28)
vacant = (vacant + 1) / 2
pylab.figure(figsize=(10, 10), dpi=400, facecolor='white')
pylab.imshow(vacant, cmap='gray', origin='upper')
pylab.tight_layout()
pylab.axis('off')
pylab.savefig("{}/clusters.png".format(config.ckpt_dir))
# loss part
hist_value, hist_head = plot.load_pickle_to_data(config.ckpt_dir)
for loss_name in ['reconstruction', 'supervised']:
plot.plot_loss_trace(hist_value[loss_name], loss_name,
config.ckpt_dir)
plot.plot_adversarial_trace(hist_value['discriminator_y'],
hist_value['generator_y'], 'y',
config.ckpt_dir)
plot.plot_adversarial_trace(hist_value['discriminator_z'],
hist_value['generator_z'], 'z',
config.ckpt_dir)
plot.plot_adversarial_trace(hist_value['validation_accuracy'],
hist_value['transform'],
'validation_accuracy', config.ckpt_dir)
location_num += 1
y_onehot = sess.run(t.y_r, feed_dict={t.x: x_batch})
y_labels = opt.argmax_label_from_distribution(y_onehot)
for m in range(batch_size):
cluster = int(y_labels[m])
counter_cluster[cluster] += 1
if counter_cluster[cluster] < num_plots_per_cluster:
x = (x_batch[m] + 1.0) / 2
pylab.subplot(
num_clusters, num_plots_per_cluster + 2,
cluster * (num_plots_per_cluster + 2) +
counter_cluster[cluster] + 2)
pylab.imshow(x.reshape((image_width, image_height)),
interpolation='none')
pylab.axis('off')
fig = pylab.gcf()
fig.set_size_inches(num_plots_per_cluster, config.ndim_y)
pylab.savefig('{}/analogy.png'.format(config.ckpt_dir))
print(counter_cluster)
hist_value, hist_head = plot.load_pickle_to_data(config.ckpt_dir)
for loss_name in ['reconstruction']:
plot.plot_loss_trace(hist_value[loss_name], loss_name, config.ckpt_dir)
plot.plot_adversarial_trace(hist_value['discriminator_y'],
hist_value['generator_y'], 'y', config.ckpt_dir)
plot.plot_adversarial_trace(hist_value['discriminator_z'],
hist_value['generator_z'], 'z', config.ckpt_dir)