# tf.histogram_fixed_width_bins

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/histogram_ops.py#L31-L100) |

Bins the given values for use in a histogram.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.histogram_fixed_width_bins`](https://www.tensorflow.org/api_docs/python/tf/histogram_fixed_width_bins)

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    tf.histogram_fixed_width_bins(
        values,
        value_range,
        nbins=100,
        dtype=https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/dtypes#int32,
        name=None
    )

Given the tensor `values`, this operation returns a rank 1 `Tensor`
representing the indices of a histogram into which each element
of `values` would be binned. The bins are equal width and
determined by the arguments `value_range` and `nbins`.

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| ## Args ||
|---|---|
| `values` | Numeric `Tensor`. |
| `value_range` | Shape \[2\] `Tensor` of same `dtype` as `values`. values \<= value_range\[0\] will be mapped to hist\[0\], values \>= value_range\[1\] will be mapped to hist\[-1\]. |
| `nbins` | Scalar `int32 Tensor`. Number of histogram bins. |
| `dtype` | dtype for returned histogram. |
| `name` | A name for this operation (defaults to 'histogram_fixed_width'). |

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| ## Returns ||
|---|---|
| A `Tensor` holding the indices of the binned values whose shape matches `values`. ||

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| ## Raises ||
|---|---|
| `TypeError` | If any unsupported dtype is provided. |
| [`tf.errors.InvalidArgumentError`](https://www.tensorflow.org/api_docs/python/tf/errors/InvalidArgumentError) | If value_range does not satisfy value_range\[0\] \< value_range\[1\]. |

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#### Examples:

    # Bins will be:  (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)

    nbins = 5
    value_range = [0.0, 5.0]
    new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
    indices = tf.histogram_fixed_width_bins(new_values, value_range, nbins=5)
    indices.numpy()
    array([0, 0, 1, 2, 4, 4], dtype=int32)