def assign_sub(ref, value, use_locking=None, name=None):
"""Update 'ref' by subtracting 'value' from it.
This operation outputs "ref" after the update is done.
This makes it easier to chain operations that need to use the reset value.
Args:
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 type as `ref`.
The value to be subtracted to the variable.
use_locking: An optional `bool`. Defaults to `False`.
If True, the subtraction will be protected by a lock;
otherwise the behavior is undefined, but may exhibit less contention.
name: A name for the operation (optional).
Returns:
Same as "ref". Returned as a convenience for operations that want
to use the new value after the variable has been updated.
"""
if ref.dtype._is_ref_dtype:
return gen_state_ops.assign_sub(ref,
value,
use_locking=use_locking,
name=name)
return ref.assign_sub(value)
def assign_sub(ref, value, use_locking=None, name=None):
"""Update 'ref' by subtracting 'value' from it.
This operation outputs "ref" after the update is done.
This makes it easier to chain operations that need to use the reset value.
Args:
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 type as `ref`.
The value to be subtracted to the variable.
use_locking: An optional `bool`. Defaults to `False`.
If True, the subtraction will be protected by a lock;
otherwise the behavior is undefined, but may exhibit less contention.
name: A name for the operation (optional).
Returns:
Same as "ref". Returned as a convenience for operations that want
to use the new value after the variable has been updated.
"""
if ref.dtype._is_ref_dtype:
return gen_state_ops.assign_sub(
ref, value, use_locking=use_locking, name=name)
return ref.assign_sub(value)
def assign_sub(ref, value, use_locking=None, name=None):
"""Update `ref` by subtracting `value` from it.
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.subtract`, this op does not broadcast. `ref` and `value`
must have the same shape.
Args:
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 subtracted to the variable.
use_locking: An optional `bool`. Defaults to `False`. If True, the
subtraction will be protected by a lock; otherwise the behavior is
undefined, but may exhibit less contention.
name: A name for the operation (optional).
Returns:
Same as `ref`. Returned as a convenience for operations that want
to use the new value after the variable has been updated.
@compatibility(TF2)
`tf.compat.v1.assign_sub` is mostly compatible with eager
execution and `tf.function`.
To switch to the native TF2 style, one could use method 'assign_sub' of
`tf.Variable`:
#### How to Map Arguments
| TF1 Arg Name | TF2 Arg Name | Note |
| :-------------------- | :-------------- | :------------------------- |
| `ref` | `self` | In `assign_sub()` method |
| `value` | `value` | In `assign_sub()` method |
| `use_locking` | `use_locking` | In `assign_sub()` method |
| `name` | `name` | In `assign_sub()` 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(1, dtype=tf.int64)
... sess.run(a.initializer)
... update_op = tf.compat.v1.assign_sub(a, 1)
... res_a = sess.run(update_op)
... res_a
0
After:
>>> b = tf.Variable(1, dtype=tf.int64)
>>> res_b = b.assign_sub(1)
>>> res_b.numpy()
0
@end_compatibility
"""
if ref.dtype._is_ref_dtype:
return gen_state_ops.assign_sub(ref,
value,
use_locking=use_locking,
name=name)
return ref.assign_sub(value)
