with tf.Graph().as_default(): with tf.compat.v1.Session() as sess: v = tf.Variable(0.0) a = tf.constant(1.0) sess.run(tf.compat.v1.global_variables_initializer()) for i in range(5): update_op = v.assign_add(1.0) b = a + v res_b = sess.run(b) res_v = sess.run(v) print(res_v)0.00.00.00.00.0
with tf.Graph().as_default(): with tf.compat.v1.Session() as sess: v = tf.Variable(0.0) a = tf.constant(1.0) sess.run(tf.compat.v1.global_variables_initializer()) for i in range(5): update_op = v.assign_add(1.0) calc = [a + v] # `tf.tuple` ensures `update_op` is run before `b` b = tf.tuple(calc, [tf.group(update_op)]) res_b = sess.run(b) res_v = sess.run(v) print(res_v)1.02.03.04.05.0
Args
tensors
A list of Tensors or IndexedSlices, some entries can be None.
control_inputs
List of additional ops to finish before returning.
name
(optional) A name to use as a name_scope for the operation.
Returns
Same as tensors.
Raises
ValueError
If tensors does not contain any Tensor or IndexedSlices.
TypeError
If control_inputs is not a list of Operation or Tensor
objects.