name="tau",
constraint=lambda t: tf.clip_by_value(t, 0, 1))
weights = tf.zeros_like(x_true)
gamma = tf.Variable(0.99,
dtype="float32",
name="gamma",
constraint=lambda t: tf.clip_by_value(t, 0, 1))
for i in range(n_iter):
#convolution layer
with tf.variable_scope('admm_iterations_{}'.format(i),
reuse=tf.AUTO_REUSE):
#first proximal layer
update2 = x - odl_op_layer_adjoint(m / tau + odl_op_layer(x) -
z) * tau
update2 = prelu(apply_conv(update2), name='prelu_1')
update2 = prelu(apply_conv(update2), name='prelu_2')
update2 = apply_conv(update2, filters=1)
x = update2
#second proximal layer
update = tf.concat([sigma * (odl_op_layer(x) + m / sigma), y_rt],
axis=-1)
update = prelu(apply_conv(update), name='prelu_3')
update = prelu(apply_conv(update), name='prelu_4')
update = apply_conv(update, filters=1)
z = update
#dual update
primal_values = []
dual_values = []
with tf.name_scope('tomography'):
with tf.name_scope('initial_values'):
primal = tf.concat([tf.zeros_like(x_true)] * n_primal, axis=-1)
dual = tf.concat([tf.zeros_like(y_rt)] * n_dual, axis=-1)
for i in range(n_iter):
with tf.variable_scope('dual_iterate_{}'.format(i)):
evalpt = primal[..., 1:2]
evalop = tf.maximum(tf.exp(-mu_water * odl_op_layer(evalpt)),
tf.exp(-10.0))
update = tf.concat([dual, evalop, y_rt], axis=-1)
update = prelu(apply_conv(update), name='prelu_1')
update = prelu(apply_conv(update), name='prelu_2')
update = apply_conv(update, filters=n_dual)
dual = dual + update
with tf.variable_scope('primal_iterate_{}'.format(i)):
evalpt_fwd = primal[..., 0:1]
evalop_fwd = (-mu_water) * tf.exp(
-mu_water * odl_op_layer(evalpt_fwd))
evalpt = dual[..., 0:1]
evalop = odl_op_layer_adjoint(evalop_fwd * dual[..., 0:1])
update = tf.concat([primal, evalop], axis=-1)
update = prelu(apply_conv(update), name='prelu_1')
update = prelu(apply_conv(update), name='prelu_2')
# primal = tf.concat([x_fbp] * n_primal, axis=3)
# primal = tf.concat([tf.zeros_like(x_true)] * n_primal, axis=-1)
# zero initialization, tf.zeros(tf.shape()) should be faster than tf.zeros_like()
primal = tf.concat([tf.zeros(tf.shape(x_true))] * n_primal, axis=-1)
dual = tf.concat([tf.zeros(tf.shape(y_rt))] * n_dual, axis=-1)
print (tf.shape(primal), tf.shape(dual))
for i in range(n_iter):
with tf.variable_scope('dual_iterate_{}'.format(i)):
evalpt = primal[..., 1:2]
# prevent overflow when attenuation is large
evalop = tf.maximum(tf.exp(-mu_water * odl_op_layer(evalpt)), tf.exp(-15.0))
update = tf.concat([dual, evalop, y_rt], axis=-1)
# print np.shape(update)
update = prelu(apply_conv(update), name='prelu_1')
# y_arr, x_true_arr = generate_data()
# print (sess.run(update, feed_dict={x_true: x_true_arr,
# y_rt: y_arr}))
update = prelu(apply_conv(update), name='prelu_2')
update = apply_conv(update, filters=n_dual)
dual = dual + update
with tf.variable_scope('primal_iterate_{}'.format(i)):
evalpt_fwd = primal[..., 0:1]
evalop_fwd = (-mu_water) * tf.maximum(tf.exp(-mu_water * odl_op_layer(evalpt_fwd)), tf.exp(-15.0))
evalpt = dual[..., 0:1]
evalop = odl_op_layer_adjoint(evalop_fwd * evalpt)
update = tf.concat([primal, evalop], axis=-1)
