"""

"""
import tensorflow as tf
import tensorflow.keras as tfk


def net(ls, input, reuse=False, train=False):

    with tf.variable_scope("CDNet", reuse=reuse):
        net = tf.layers.conv2d(input, ls[1], [3, 3], strides=(1, 1), padding='SAME',
                           activation=tf.nn.relu, trainable=train) # n_batchx192x192x32
        print("conv1 ", net)

        net = lays.conv2d(net, ls[2], [3, 3], stride=(1, 1), padding="SAME",activation_fn=tf.nn.relu,
                          weights_initializer=lays.xavier_initializer(uniform=True),
                          trainable=train) #n_batchx192x192x64
        print("conv2 ", net)

        net1 = lays.conv2d_transpose(net, ls[3], [3, 3], stride=(1, 1), padding="SAME",
                                     activation_fn=tf.nn.relu, weights_initializer=lays.xavier_initializer(uniform=True),
                                     trainable=train) # n_batchx192x192x32
        print("deconv1 ", net1)

        net1 = lays.conv2d_transpose(net1, ls[4], [3, 3], stride=(1, 1), padding='SAME',
                                     activation_fn=tf.nn.relu, weights_initializer=lays.xavier_initializer(uniform=True),
                                     trainable=train)  # n_batchx192x192x64
        print("deconv2 ", net1)

        net1 = lays.conv2d_transpose(net1, ls[5], [3, 3], stride=(1, 1), padding='SAME',
                                     activation_fn=tf.nn.relu, weights_initializer=lays.xavier_initializer(uniform=True),
                                     trainable=train)  # n_batchx192x192x32
        print("deconv3 ", net1)  # ls[6]

        net1 = lays.conv2d_transpose(net1, ls[6], [1, 1], stride=(1, 1), padding='SAME',
                                     weights_initializer=lays.xavier_initializer(uniform=True),
                                     trainable=train)  # n_batchx192x192x3
        print("deconv4 ", net1)

    return net1, net
fe3d030a7cc706b3100339099c61b2151e73ad63
<mask token>

<mask token>


def net(ls, input, reuse=False, train=False):
    with tf.variable_scope('CDNet', reuse=reuse):
        net = tf.layers.conv2d(input, ls[1], [3, 3], strides=(1, 1),
            padding='SAME', activation=tf.nn.relu, trainable=train)
        print('conv1 ', net)
        net = lays.conv2d(net, ls[2], [3, 3], stride=(1, 1), padding='SAME',
            activation_fn=tf.nn.relu, weights_initializer=lays.
            xavier_initializer(uniform=True), trainable=train)
        print('conv2 ', net)
        net1 = lays.conv2d_transpose(net, ls[3], [3, 3], stride=(1, 1),
            padding='SAME', activation_fn=tf.nn.relu, weights_initializer=
            lays.xavier_initializer(uniform=True), trainable=train)
        print('deconv1 ', net1)
        net1 = lays.conv2d_transpose(net1, ls[4], [3, 3], stride=(1, 1),
            padding='SAME', activation_fn=tf.nn.relu, weights_initializer=
            lays.xavier_initializer(uniform=True), trainable=train)
        print('deconv2 ', net1)
        net1 = lays.conv2d_transpose(net1, ls[5], [3, 3], stride=(1, 1),
            padding='SAME', activation_fn=tf.nn.relu, weights_initializer=
            lays.xavier_initializer(uniform=True), trainable=train)
        print('deconv3 ', net1)
        net1 = lays.conv2d_transpose(net1, ls[6], [1, 1], stride=(1, 1),
            padding='SAME', weights_initializer=lays.xavier_initializer(
            uniform=True), trainable=train)
        print('deconv4 ', net1)
    return net1, net

<mask token>
import tensorflow as tf
import tensorflow.keras as tfk


def net(ls, input, reuse=False, train=False):
    with tf.variable_scope('CDNet', reuse=reuse):
        net = tf.layers.conv2d(input, ls[1], [3, 3], strides=(1, 1),
            padding='SAME', activation=tf.nn.relu, trainable=train)
        print('conv1 ', net)
        net = lays.conv2d(net, ls[2], [3, 3], stride=(1, 1), padding='SAME',
            activation_fn=tf.nn.relu, weights_initializer=lays.
            xavier_initializer(uniform=True), trainable=train)
        print('conv2 ', net)
        net1 = lays.conv2d_transpose(net, ls[3], [3, 3], stride=(1, 1),
            padding='SAME', activation_fn=tf.nn.relu, weights_initializer=
            lays.xavier_initializer(uniform=True), trainable=train)
        print('deconv1 ', net1)
        net1 = lays.conv2d_transpose(net1, ls[4], [3, 3], stride=(1, 1),
            padding='SAME', activation_fn=tf.nn.relu, weights_initializer=
            lays.xavier_initializer(uniform=True), trainable=train)
        print('deconv2 ', net1)
        net1 = lays.conv2d_transpose(net1, ls[5], [3, 3], stride=(1, 1),
            padding='SAME', activation_fn=tf.nn.relu, weights_initializer=
            lays.xavier_initializer(uniform=True), trainable=train)
        print('deconv3 ', net1)
        net1 = lays.conv2d_transpose(net1, ls[6], [1, 1], stride=(1, 1),
            padding='SAME', weights_initializer=lays.xavier_initializer(
            uniform=True), trainable=train)
        print('deconv4 ', net1)
    return net1, net

"""

"""
import tensorflow as tf
import tensorflow.keras as tfk


def net(ls, input, reuse=False, train=False):

    with tf.variable_scope("CDNet", reuse=reuse):
        net = tf.layers.conv2d(input, ls[1], [3, 3], strides=(1, 1), padding='SAME',
                           activation=tf.nn.relu, trainable=train) # n_batchx192x192x32
        print("conv1 ", net)

        net = lays.conv2d(net, ls[2], [3, 3], stride=(1, 1), padding="SAME",activation_fn=tf.nn.relu,
                          weights_initializer=lays.xavier_initializer(uniform=True),
                          trainable=train) #n_batchx192x192x64
        print("conv2 ", net)

        net1 = lays.conv2d_transpose(net, ls[3], [3, 3], stride=(1, 1), padding="SAME",
                                     activation_fn=tf.nn.relu, weights_initializer=lays.xavier_initializer(uniform=True),
                                     trainable=train) # n_batchx192x192x32
        print("deconv1 ", net1)

        net1 = lays.conv2d_transpose(net1, ls[4], [3, 3], stride=(1, 1), padding='SAME',
                                     activation_fn=tf.nn.relu, weights_initializer=lays.xavier_initializer(uniform=True),
                                     trainable=train)  # n_batchx192x192x64
        print("deconv2 ", net1)

        net1 = lays.conv2d_transpose(net1, ls[5], [3, 3], stride=(1, 1), padding='SAME',
                                     activation_fn=tf.nn.relu, weights_initializer=lays.xavier_initializer(uniform=True),
                                     trainable=train)  # n_batchx192x192x32
        print("deconv3 ", net1)  # ls[6]

        net1 = lays.conv2d_transpose(net1, ls[6], [1, 1], stride=(1, 1), padding='SAME',
                                     weights_initializer=lays.xavier_initializer(uniform=True),
                                     trainable=train)  # n_batchx192x192x3
        print("deconv4 ", net1)

    return net1, net