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tf.compat.v1.assign_add

Update ref by adding value to it.

Migrate to TF2

tf.compat.v1.assign_add is mostly compatible with eager execution and tf.function.

To switch to the native TF2 style, one could use method 'assign_add' of tf.Variable:

How to Map Arguments

TF1 Arg Name TF2 Arg Name Note
ref self In assign_add() method
value value In assign_add() method
use_locking use_locking In assign_add() method
name name In assign_add() method
- read_value Set to True to replicate behavior (True is default)

Before & After Usage Example

Before:

with tf.Graph().as_default():
  with tf.compat.v1.Session() as sess:
    a = tf.compat.v1.Variable(0, dtype=tf.int64)
    sess.run(a.initializer)
    update_op = tf.compat.v1.assign_add(a, 1)
    res_a = sess.run(update_op)
    res_a
1

After:

b = tf.Variable(0, dtype=tf.int64)
res_b = b.assign_add(1)
res_b.numpy()
1

Description

This operation outputs ref after the update is done. This makes it easier to chain operations that need to use the reset value. Unlike tf.math.add, this op does not broadcast. ref and value must have the same shape.

ref A mutable Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half. Should be from a Variable node.
value A Tensor. Must have the same shape and dtype as ref. The value to be added to the variable.
use_locking An optional bool. Defaults to False. If True, the addition will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
name A name for the operation (optional).

Same as ref. Returned as a convenience for operations that want to use the new value after the variable has been updated.