'num_hid': 20,
'num_hid_dec': 100,
'output_dist': 'Bernoulli', # Bernoulli or Gaussian
# 'output_dist': 'Gaussian',
'non_linear': 'tf.nn.relu',
# 'non_linear': 'tf.nn.tanh',
'weight_decay': 5e-5
}
# Train loop options
loop_config = {
'num_steps': args.num_steps,
'steps_per_ckpt': args.steps_per_ckpt
}
dataset = mnist.read_data_sets("../MNIST_data/", one_hot=True)
m = get_train_model(opt)
sess = tf.Session()
sess.run(tf.initialize_all_variables())
saver = tf.train.Saver(tf.all_variables())
task_name = 'vae_mnist_half'
time_obj = datetime.datetime.now()
model_id = timestr = '{}-{:04d}{:02d}{:02d}{:02d}{:02d}{:02d}'.format(
task_name, time_obj.year, time_obj.month, time_obj.day,
time_obj.hour, time_obj.minute, time_obj.second)
results_folder = args.results
logs_folder = args.logs
exp_folder = os.path.join(results_folder, model_id)
exp_logs_folder = os.path.join(logs_folder, model_id)
filter_x = (1 / np.sqrt(np.exp(lg_var)) / np.sqrt(2 * np.pi) *
np.exp(-0.5 * (span_x - mu_x) *
(span_x - mu_x) / np.exp(lg_var)))
# [H, F]
filter_y = (1 / np.sqrt(np.exp(lg_var)) / np.sqrt(2 * np.pi) *
np.exp(-0.5 * (span_y - mu_y) *
(span_y - mu_y) / np.exp(lg_var)))
read = filter_y.transpose().dot(img).dot(filter_x)
return read
if not os.path.exists('img.npy'):
from data_api import mnist
dataset = mnist.read_data_sets("../MNIST_data/", one_hot=True)
img = dataset.train.images[0].reshape([im_height, im_width])
np.save('img.npy', img)
else:
img = np.load('img.npy')
# Controller variables.
num_img = 8
f, axarr = plt.subplots(2, num_img)
for ii in xrange(num_img):
gg_x = 0.0
gg_y = 0.0
ddelta = 0.1 * (ii + 1)
log.info('ddelta: {}'.format(ddelta))
lg_var = 0.01