""" Q: Can you pass 2 gradients for the same variable to the optimizer? """
import tensorflow as tf


v = tf.get_variable(name='v', initializer=tf.constant(1.0), dtype=tf.float32)

l1 = v * 2
l2 = v * 4

optimizer = tf.train.AdamOptimizer()
g1 = optimizer.compute_gradients(loss=l1, var_list=[v])
g2 = optimizer.compute_gradients(loss=l2, var_list=[v])
apply_g1 = optimizer.apply_gradients(g1)
apply_g2 = optimizer.apply_gradients(g2)
apply_both = optimizer.apply_gradients(g1 + g2)

# We see below that applying individual updates is not equivalent to averaging the updates when multiple gradients apply to a single variable
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print(sess.run(v))
    sess.run(apply_both)
    print(sess.run(v))
    sess.run(apply_both)
    print(sess.run(v))

    sess.run(tf.global_variables_initializer())
    print(sess.run(v))
    sess.run(apply_g1)
    sess.run(apply_g2)
    print(sess.run(v))
    sess.run(apply_g1)
    sess.run(apply_g2)
    print(sess.run(v))