def joint_log_prob(z):
gen_out = gen(z, reuse=tf.AUTO_REUSE, training=False)
diff_img = gen_out - tf.constant(noisy_mat4d)
prior = tfd.MultivariateNormalDiag(loc=np.zeros(z_dim,
dtype=np.float32),
scale_diag=np.ones(
z_dim, dtype=np.float32))
dim_window = iter_no * (tile_size**2)
if (iter_no == 1):
diff_img_visible = tf.reshape(
tf.slice(diff_img, [0, tile_tl_row, tile_tl_col, 0],
[1, tile_size, tile_size, 1]),
[int(dim_window / iter_no)])
like = tfd.MultivariateNormalDiag(
loc=np.zeros(dim_window, dtype=np.float32),
scale_diag=np.sqrt(noise_var) *
np.ones(dim_window, dtype=np.float32))
return (prior.log_prob(z) + like.log_prob(diff_img_visible))
else:
ls = []
for ii in range(iter_no):
ls.append(
tf.reshape(
tf.slice(diff_img, [0, rows[ii], cols[ii], 0],
[1, tile_size, tile_size, 1]),
[int(dim_window / iter_no)]))
diff_img_visible = tf.concat(ls, axis=0)
like = tfd.MultivariateNormalDiag(
loc=np.zeros(dim_window, dtype=np.float32),
scale_diag=np.sqrt(noise_var) *
np.ones(dim_window, dtype=np.float32))
return (prior.log_prob(z) + like.log_prob(diff_img_visible))
def joint_log_prob(z):
gen_out = gen(z, reuse=tf.AUTO_REUSE, training=False)
diff_img = tf.reshape(gen_out - tf.constant(noisy_mat4d), [dim_like])
if random_sampling==True:
diff_img = tf.gather_nd(diff_img, indices)
prior = tfd.MultivariateNormalDiag(loc=np.zeros(z_dim, dtype=np.float32),
scale_diag=np.ones(z_dim, dtype=np.float32))
like = tfd.MultivariateNormalDiag(loc=np.zeros(args.n_meas, dtype=np.float32),
scale_diag=np.sqrt(args.noise_var)*np.ones(args.n_meas, dtype=np.float32))
return (prior.log_prob(z) + like.log_prob(diff_img))
def joint_log_prob(z):
gen_out = gen(z, reuse=tf.AUTO_REUSE, training=False)
diff_img = tf.reshape(gen_out - tf.constant(noisy_mat4d), [dim_like])
prior = tfd.MultivariateNormalDiag(loc=np.zeros(z_dim,
dtype=np.float32),
scale_diag=np.ones(
z_dim, dtype=np.float32))
like = tfd.MultivariateNormalDiag(loc=np.zeros(dim_like,
dtype=np.float32),
scale_diag=np.sqrt(noise_var) *
np.ones(dim_like, dtype=np.float32))
return (prior.log_prob(z) + like.log_prob(diff_img))
def joint_log_prob(z):
gen_out = gen(z, reuse=tf.AUTO_REUSE, training=False)
diff_img = gen_out - tf.constant(noisy_mat4d)
visible_img = tf.boolean_mask(diff_img, mask)
prior = tfd.MultivariateNormalDiag(loc=np.zeros(z_dim,
dtype=np.float32),
scale_diag=np.ones(
z_dim, dtype=np.float32))
like = tfd.MultivariateNormalDiag(
loc=np.zeros(dim_inpaint, dtype=np.float32),
scale_diag=np.sqrt(noise_var) *
np.ones(dim_inpaint, dtype=np.float32))
return (prior.log_prob(z) + like.log_prob(visible_img))
#noisy_mat4d = np.tile(noisy_mat3d, (batch_size, 1, 1, 1)).astype(np.float32)
#noisy_mat4d = np.load('./oed_experiments/random_sample_zmap/tile2x2/digit{}_var{}/noisy_mat4d.npy'.format(PARAMS.digit, PARAMS.noise_var))
if (iter_no != 0):
var_info = np.load(sample_dir + '/../var_info.npy'.format(sample_dir))
#var_info = np.load('./oed_experiments/oed/tile2x2/digit{}_var{}_N{}/var_info.npy'.format(PARAMS.digit, noise_var, N))
if (iter_no == 1):
tile_tl_row = int(var_info[iter_no - 1, 0])
tile_tl_col = int(var_info[iter_no - 1, 1])
else:
rows = var_info[:iter_no, 0].astype(np.int32)
cols = var_info[:iter_no, 1].astype(np.int32)
with tf.Graph().as_default() as g:
z1 = tf.placeholder(tf.float32, shape=[batch_size, z_dim])
gen_out = gen(z1, reuse=tf.AUTO_REUSE, training=False)
diff_img = gen_out - tf.constant(noisy_mat4d)
model_path = './checkpoints/mnist_wgan_gp1000/Epoch_(999)_(171of171).ckpt'
variables_to_restore = slim.get_variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
with tf.Session(graph=g) as sess:
saver.restore(sess, model_path)
loss = np.zeros((n_eff))
x_mean = np.zeros((28, 28, 1))
x2_mean = np.zeros((28, 28, 1))
for ii in range(n_iter):
g_z, diff = sess.run([gen_out, diff_img],
feed_dict={
#initial_state = tf.constant(np.zeros((batch_size, z_dim)).astype(np.float32))
initial_state = tf.constant(
np.random.normal(size=[batch_size, z_dim]).astype(np.float32))
samples, [st_size, log_accept_ratio] = tfp.mcmc.sample_chain(
num_results=N,
num_burnin_steps=burn,
current_state=initial_state,
kernel=adaptive_hmc,
trace_fn=lambda _, pkr: [
pkr.inner_results.accepted_results.step_size, pkr.inner_results.
log_accept_ratio
])
p_accept = tf.reduce_mean(tf.exp(tf.minimum(log_accept_ratio, 0.)))
zz = tf.placeholder(tf.float32, shape=[N - burn, z_dim])
gen_out1 = gen(zz, reuse=tf.AUTO_REUSE, training=False)
model_path = './checkpoints/mnist_wgan_gp1000/Epoch_(999)_(171of171).ckpt'
variables_to_restore = slim.get_variables_to_restore()
#variables_to_restore = [v for v in variables if v.name.split(':')[0]!='dummy']
saver = tf.train.Saver(variables_to_restore)
with tf.Session(graph=g) as sess:
saver.restore(sess, model_path)
samples_ = sess.run(samples)
np.save(save_dir + '/samples.npy', samples_)
#np.save(save_dir+'/noisy_mat4d.npy', noisy_mat4d)
#print('acceptance ratio = {}'.format(sess.run(p_accept)))
cov=np.eye(dim_like, dim_like) * PARAMS.noise_var,
size=1)
np.save(meas_path + '/noise_mat3d.npy', noise_mat3d)
else:
noise_mat3d = np.load(meas_path + '/noise_mat3d.npy')
y_hat3d = test_sample + np.reshape(noise_mat3d,
[PARAMS.img_h, PARAMS.img_w, PARAMS.img_c])
y_hat4d = np.tile(y_hat3d, (PARAMS.batch_size, 1, 1, 1)).astype(np.float32)
with tf.Graph().as_default() as g:
z1 = tf.placeholder(tf.float32, shape=[PARAMS.batch_size, PARAMS.z_dim])
dummy = tf.Variable(name='dummy', trainable=False, initial_value=z1)
assign_op1 = dummy.assign(z1)
gen_out = gen(dummy, reuse=tf.AUTO_REUSE, training=False)
diff_img = gen_out - tf.constant(y_hat4d)
loss = 0.5 * tf.linalg.norm(diff_img)**2 + (0.5 * PARAMS.noise_var *
tf.linalg.norm(dummy)**2)
optimizer = tf.contrib.opt.ScipyOptimizerInterface(loss,
var_list=[dummy],
options={
'maxiter': 10000,
'disp': True
})
variables = slim.get_variables_to_restore()
variables_to_restore = [
v for v in variables if v.name.split(':')[0] != 'dummy'
]
