TensorFlow 1 version | View source on GitHub |
Clips tensor values to a specified min and max.
tf.clip_by_value(
t, clip_value_min, clip_value_max, name=None
)
Given a tensor t
, this operation returns a tensor of the same type and
shape as t
with its values clipped to clip_value_min
and clip_value_max
.
Any values less than clip_value_min
are set to clip_value_min
. Any values
greater than clip_value_max
are set to clip_value_max
.
For example:
Basic usage passes a scalar as the min and max value.
t = tf.constant([[-10., -1., 0.], [0., 2., 10.]])
t2 = tf.clip_by_value(t, clip_value_min=-1, clip_value_max=1)
t2.numpy()
array([[-1., -1., 0.],
[ 0., 1., 1.]], dtype=float32)
The min and max can be the same size as t
, or broadcastable to that size.
t = tf.constant([[-1, 0., 10.], [-1, 0, 10]])
clip_min = [[2],[1]]
t3 = tf.clip_by_value(t, clip_value_min=clip_min, clip_value_max=100)
t3.numpy()
array([[ 2., 2., 10.],
[ 1., 1., 10.]], dtype=float32)
Broadcasting fails, intentionally, if you would expand the dimensions of t
t = tf.constant([[-1, 0., 10.], [-1, 0, 10]])
clip_min = [[[2, 1]]] # Has a third axis
t4 = tf.clip_by_value(t, clip_value_min=clip_min, clip_value_max=100)
Traceback (most recent call last):
InvalidArgumentError: Incompatible shapes: [2,3] vs. [1,1,2]
It throws a TypeError
if you try to clip an int
to a float
value
(tf.cast
the input to float
first).
t = tf.constant([[1, 2], [3, 4]], dtype=tf.int32)
t5 = tf.clip_by_value(t, clip_value_min=-3.1, clip_value_max=3.1)
Traceback (most recent call last):
TypeError: Cannot convert ...
Args | |
---|---|
t
|
A Tensor or IndexedSlices .
|
clip_value_min
|
The minimum value to clip to. A scalar Tensor or one that
is broadcastable to the shape of t .
|
clip_value_max
|
The maximum value to clip to. A scalar Tensor or one that
is broadcastable to the shape of t .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A clipped Tensor or IndexedSlices .
|
Raises | |
---|---|
tf.errors.InvalidArgumentError : If the clip tensors would trigger array
broadcasting that would make the returned tensor larger than the input.
|
|
TypeError
|
If dtype of the input is int32 and dtype of
the clip_value_min or clip_value_max is float32
|