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Clips tensor values to a specified min and max.

Used in the notebooks

Used in the tutorials

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)
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)
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 ...

t A Tensor or IndexedSlices.
clip_value_min The minimum