Load pretrained model as legs of siamese netwrok

siamese.py

import tensorflow as tf

# 1. Create and save a network

# c = a*b
g = tf.Graph()
with g.as_default():
    a = tf.placeholder(tf.float32, name='a')
    b = tf.Variable(initial_value=tf.truncated_normal((1,)), name='b')
    c = tf.multiply(a, b, name='c')
    s1 = tf.train.Saver()

with tf.Session(graph=g) as sess:
    sess.run(tf.global_variables_initializer())
    b_init = sess.run(b)
    print('initial value: b=%.2f' % b_init)
    s1.save(sess, 'g1')


# 2. Load saved model as legs of siamese network

g_siamese = tf.Graph()

with g_siamese.as_default():
    # two inputs
    a1 = tf.placeholder(tf.float32)
    a2 = tf.placeholder(tf.float32)

    # load g1 and map a to a1 and a2
    # we'll have two references to a single network
    tf.train.import_meta_graph(
        'g1.meta',
        import_scope='g1',
        input_map={'a:0': [a1, a2]})
    
    b_ = g_siamese.get_tensor_by_name('g1/b:0')
    c1, c2 = tf.split(g_siamese.get_tensor_by_name('g1/c:0'), 2)

    # we want a1*b - a2*b = 3
    cost = tf.abs(c1 - c2 - 3)
    train = tf.train.AdamOptimizer(0.1).minimize(cost)
    init_op = tf.global_variables_initializer()
    loader = tf.train.Saver(var_list={'b': b_})


# 3. Optimize b for given a1 and a2

feed_dict = {a1: 1.0, a2: -1.0}
print('a1=%s and a2=%s; optimal b=%s' % (feed_dict[a1], feed_dict[a2],
                                         3.0/(feed_dict[a1]-feed_dict[a2])))

with tf.Session(graph=g_siamese) as sess:
    sess.run(init_op)
    loader.restore(sess, './g1')

    cost_value, b_value = sess.run([cost, b_], feed_dict=feed_dict)
    print('initial values: b=%.2f (restored); cost=%.2f' % (b_value, cost_value))

    for _ in range(100):
        sess.run(train, feed_dict=feed_dict)
    cost_value, b_value = sess.run([cost, b_], feed_dict=feed_dict)
    print('after training: b=%.2f; cost=%.2f' % (b_value, cost_value))

Hi, after training, I would like to eval one leg of the network such as :

feed_dict = {a1: 1.0}
sess.run([c1], feed_dict=feed_dict)

But it does not work