Tarihi kaydet! Google I / O 18-20 Mayıs'ta geri dönüyor

tf.clip_by_value

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)`
`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 ...`
```

`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).

A clipped `Tensor` or `IndexedSlices`.

`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`