Exemple #1
0
def iv_net(x, training=True):
    with tf.variable_scope('iv_net', reuse=tf.AUTO_REUSE):
        x = ms.fc(x, units=40 * 40 * 32)
        x = ms.bn(x, training=training)
        x = tf.reshape(x, [-1, 40, 40, 32])
        x = fn.id_blk(x, 32, [3, 3])
        x = fn.id_blk(x, 32, [3, 3])
        x = ms.max_pool(x, [2, 2], 2)

        x = ms.conv(x, 64, [3, 3])
        x = ms.bn(x, training=training)
        x = ms.activation(x, relu=True)
        x = fn.id_blk(x, 64, [3, 3])
        x = fn.id_blk(x, 64, [3, 3])
        x = ms.max_pool(x, [2, 2], 2)

        x = ms.conv(x, 128, [3, 3])
        x = ms.bn(x, training=training)
        x = ms.activation(x, relu=True)
        x = fn.id_blk(x, 128, [3, 3])
        x = fn.id_blk(x, 128, [3, 3])
        x = ms.max_pool(x, [2, 2], 2)

        x = ms.conv(x, 256, [3, 3])
        x = ms.bn(x, training=training)
        x = ms.activation(x, relu=True)
        x = fn.id_blk(x, 256, [3, 3])
        x = fn.id_blk(x, 256, [3, 3])

        x = tf.reshape(x, [-1, 5 * 5 * 256])
        x = ms.fc(x, units=5)
        x = ms.activation(x, relu=False)

    return x
def fw_net(x, training=True):

    with tf.name_scope('fw_net'):

        with tf.variable_scope('fc1'):
            x = ms.fc(x, 5 * 256)
            # x = ms.bn(x)
            x = ms.activation(x, relu=True)
            x = tf.reshape(x, [-1, 5, 256])

        with tf.variable_scope('identical_conv1'):

            x = ms.conv1d(x, 256, 3)
            # x = ms.bn(x, training=training)
            x = ms.activation(x, relu=True)

            x = ms.conv1d(x, 256, 3)
            # x = ms.bn(x, training=training)
            x = ms.activation(x, relu=True)

            x = ms.conv1d(x, 256, 3)
            # x = ms.bn(x, training=training)
            x = ms.activation(x, relu=True)

            x = ms.conv1d(x, 256, 3)
            # x = ms.bn(x, training=training)
            x = ms.activation(x, relu=True)
            print(x.shape)  # (128, 5, 256)

        with tf.variable_scope('t_conv1'):
            x = ms.t_conv1d(x, tf.Variable(tf.random.normal([3, 128, 256])),
                            (128, 10, 128), 2)
            # x = ms.bn(x)
            x = ms.activation(x, relu=True)

        with tf.variable_scope('identical_conv2'):
            x = ms.conv1d(x, 128, 3)
            # x = ms.bn(x, training=training)
            x = ms.activation(x, relu=True)

            x = ms.conv1d(x, 128, 3)
            # x = ms.bn(x, training=training)
            x = ms.activation(x, relu=True)

            x = ms.conv1d(x, 128, 3)
            # x = ms.bn(x, training=training)
            x = ms.activation(x, relu=True)

            x = ms.conv1d(x, 128, 3)
            # x = ms.bn(x, training=training)
            x = ms.activation(x, relu=True)

        with tf.variable_scope('t_conv2'):
            x = ms.t_conv1d(x, tf.Variable(tf.random.normal((3, 64, 128))),
                            (128, 20, 64), 2)
            # x = ms.bn(x)
            x = ms.activation(x, relu=True)

        with tf.variable_scope('identical_conv3'):
            x = ms.conv1d(x, 64, 3)
            # x = ms.bn(x, training=training)
            x = ms.activation(x, relu=True)

            x = ms.conv1d(x, 64, 3)
            # x = ms.bn(x, training=training)
            x = ms.activation(x, relu=True)

            x = ms.conv1d(x, 64, 3)
            # x = ms.bn(x, training=training)
            x = ms.activation(x, relu=True)

            x = ms.conv1d(x, 64, 3)
            # x = ms.bn(x, training=training)
            x = ms.activation(x, relu=True)

        with tf.variable_scope('t_conv3'):
            x = ms.t_conv1d(x, tf.Variable(tf.random.normal((3, 32, 64))),
                            (128, 40, 32), 2)
            # x = ms.bn(x)
            x = ms.activation(x, relu=True)

        with tf.variable_scope('identical_conv4'):
            x = ms.conv1d(x, 32, 3)
            # x = ms.bn(x, training=training)
            x = ms.activation(x, relu=True)

            x = ms.conv1d(x, 32, 3)
            # x = ms.bn(x, training=training)
            x = ms.activation(x, relu=True)

            x = ms.conv1d(x, 32, 3)
            # x = ms.bn(x, training=training)
            x = ms.activation(x, relu=True)

            x = ms.conv1d(x, 32, 3)
            # x = ms.bn(x, training=training)
            x = ms.activation(x, relu=True)

        x = tf.reshape(x, [-1, 40 * 32])

        with tf.variable_scope('fc2'):
            x = ms.fc(x, units=603)
            # x = ms.bn(x, training=training)
            x = ms.activation(x, relu=False)

    return x
Exemple #3
0
def fw_net(x, training=True):
    with tf.name_scope('fw_net'):
        with tf.variable_scope('fw_net'):

            with tf.name_scope('fc1'):
                with tf.variable_scope('fc1'):
                    x = ms.fc(x, 5 * 5 * 256)
                    # x = ms.bn(x)
                    x = ms.activation(x, relu=True)
                x = tf.reshape(x, [-1, 5, 5, 256])

            with tf.name_scope('identical_conv1'):
                with tf.variable_scope('identical_conv1'):
                    x = ms.conv(x, 256, [3, 3])
                    # x = ms.bn(x, training=training)
                    x = ms.activation(x, relu=True)

                    x = ms.conv(x, 256, [3, 3])
                    # x = ms.bn(x, training=training)
                    x = ms.activation(x, relu=True)

                    x = ms.conv(x, 256, [3, 3])
                    # x = ms.bn(x, training=training)
                    x = ms.activation(x, relu=True)

                    x = ms.conv(x, 256, [3, 3])
                    # x = ms.bn(x, training=training)
                    x = ms.activation(x, relu=True)

                    x = ms.conv(x, 256, [3, 3])
                    # x = ms.bn(x, training=training)
                    x = ms.activation(x, relu=True)

            with tf.name_scope('t_conv1'):
                with tf.variable_scope('t_conv1'):
                    x = ms.t_conv2d(x, 128, [2, 2], 2)
                    # x = ms.bn(x)
                    x = ms.activation(x, relu=True)

            with tf.name_scope('identical_conv2'):
                with tf.variable_scope('identical_conv2'):
                    x = ms.conv(x, 128, [3, 3])
                    # x = ms.bn(x, training=training)
                    x = ms.activation(x, relu=True)

                    x = ms.conv(x, 128, [3, 3])
                    # x = ms.bn(x, training=training)
                    x = ms.activation(x, relu=True)

                    x = ms.conv(x, 128, [3, 3])
                    # x = ms.bn(x, training=training)
                    x = ms.activation(x, relu=True)

                    x = ms.conv(x, 128, [3, 3])
                    # x = ms.bn(x, training=training)
                    x = ms.activation(x, relu=True)

                    x = ms.conv(x, 128, [3, 3])
                    # x = ms.bn(x, training=training)
                    x = ms.activation(x, relu=True)

            with tf.name_scope('t_conv2'):
                with tf.variable_scope('t_conv2'):
                    x = ms.t_conv2d(x, 64, [2, 2], 2)
                    # x = ms.bn(x)
                    x = ms.activation(x, relu=True)

            with tf.name_scope('identical_conv3'):
                with tf.variable_scope('identical_conv3'):
                    x = ms.conv(x, 64, [3, 3])
                    # x = ms.bn(x, training=training)
                    x = ms.activation(x, relu=True)

                    x = ms.conv(x, 64, [3, 3])
                    # x = ms.bn(x, training=training)
                    x = ms.activation(x, relu=True)

                    x = ms.conv(x, 64, [3, 3])
                    # x = ms.bn(x, training=training)
                    x = ms.activation(x, relu=True)

                    x = ms.conv(x, 64, [3, 3])
                    # x = ms.bn(x, training=training)
                    x = ms.activation(x, relu=True)

                    x = ms.conv(x, 64, [3, 3])
                    # x = ms.bn(x, training=training)
                    x = ms.activation(x, relu=True)

            with tf.name_scope('t_conv3'):
                with tf.variable_scope('t_conv3'):
                    x = ms.t_conv2d(x, 32, [2, 2], 2)
                    # x = ms.bn(x)
                    x = ms.activation(x, relu=True)

            with tf.name_scope('identical_conv4'):
                with tf.variable_scope('identical_conv4'):
                    x = ms.conv(x, 32, [3, 3])
                    # x = ms.bn(x, training=training)
                    x = ms.activation(x, relu=True)

                    x = ms.conv(x, 32, [3, 3])
                    # x = ms.bn(x, training=training)
                    x = ms.activation(x, relu=True)

                    x = ms.conv(x, 32, [3, 3])
                    # x = ms.bn(x, training=training)
                    x = ms.activation(x, relu=True)

                    x = ms.conv(x, 32, [3, 3])
                    # x = ms.bn(x, training=training)
                    x = ms.activation(x, relu=True)

                    x = ms.conv(x, 32, [3, 3])
                    # x = ms.bn(x, training=training)
                    x = ms.activation(x, relu=True)

                x = tf.reshape(x, [-1, 40 * 40 * 32])

            with tf.name_scope('fc2'):
                with tf.variable_scope('fc2'):
                    x = ms.fc(x, units=603)
                    # x = ms.bn(x, training=training)
                    x = ms.activation(x, relu=False)

    return x
Exemple #4
0
                t = ms.conv(t, 32, [3, 3])
                # x = ms.bn(x, training=training)
                t = ms.activation(t, relu=True)

                t = ms.conv(t, 32, [3, 3])
                # x = ms.bn(x, training=training)
                t = ms.activation(t, relu=True)

                t = ms.conv(t, 32, [3, 3])
                # x = ms.bn(x, training=training)
                t = ms.activation(t, relu=True)

            t = tf.reshape(t, [-1, 40 * 40 * 32])

            with tf.variable_scope('fc2'):
                t = ms.fc(t, units=402)
                # x = ms.bn(x, training=training)
                t = ms.activation(t, relu=False)

    new_loss = ms.huber_loss(t, y_)
    new_optimizer = tf.train.AdamOptimizer(learning_rate=LR,
                                           beta1=0.9,
                                           beta2=0.999,
                                           epsilon=1e-8,
                                           name='new_adam')

    new_op = new_optimizer.minimize(loss=new_loss)

    var = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
                            scope='trans_part')