tf.compat.v1.assign

Update ref by assigning value to it.

tf.compat.v1.assign(
    ref, value, validate_shape=None, use_locking=None, name=None
)

Migrate to TF2

tf.compat.v1.assign is mostly compatible with eager execution and tf.function. However, argument 'validate_shape' will be ignored. To avoid shape validation, set 'shape' to tf.TensorShape(None) when constructing the variable:

import tensorflow as tf
a = tf.Variable([1], shape=tf.TensorShape(None))
tf.compat.v1.assign(a, [2,3])

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

How to Map Arguments

TF1 Arg Name	TF2 Arg Name	Note
`ref`	`self`	In `assign()` method
`value`	`value`	In `assign()` method
`validate_shape`	Not supported	Specify `shape` in the constructor to replicate behavior
`use_locking`	`use_locking`	In `assign()` method
`name`	`name`	In `assign()` method
-	`read_value`	Set to True to replicate behavior (True is default)

Description

This operation outputs a Tensor that holds the new value of ref after the value has been assigned. This makes it easier to chain operations that need to use the reset value.

Args
`ref`	A mutable `Tensor`. Should be from a `Variable` node. May be uninitialized.
`value`	A `Tensor`. Must have the same shape and dtype as `ref`. The value to be assigned to the variable.
`validate_shape`	An optional `bool`. Defaults to `True`. If true, the operation will validate that the shape of 'value' matches the shape of the Tensor being assigned to. If false, 'ref' will take on the shape of 'value'.
`use_locking`	An optional `bool`. Defaults to `True`. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
`name`	A name for the operation (optional).

Returns
A `Tensor` that will hold the new value of `ref` after the assignment has completed.

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(a, 2)
    res_a = sess.run(update_op)
    res_a
2

After:

b = tf.Variable(0, dtype=tf.int64)
res_b = b.assign(2)
res_b.numpy()
2