tf.raw_ops.HistogramFixedWidth
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Return histogram of values.
tf.raw_ops.HistogramFixedWidth(
values,
value_range,
nbins,
dtype=tf.dtypes.int32,
name=None
)
Given the tensor values, this operation returns a rank 1 histogram counting
the number of entries in values that fall into every bin. The bins are
equal width and determined by the arguments value_range and nbins.
# 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]
with tf.get_default_session() as sess:
hist = tf.histogram_fixed_width(new_values, value_range, nbins=5)
variables.global_variables_initializer().run()
sess.run(hist) => [2, 1, 1, 0, 2]
Args |
|---|
values
|
A Tensor. Must be one of the following types: int32, int64, float32, float64.
Numeric Tensor.
|
value_range
|
A Tensor. Must have the same type as values.
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
|
A Tensor of type int32.
Scalar int32 Tensor. Number of histogram bins.
|
dtype
|
An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor of type dtype.
|
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Last updated 2024-04-26 UTC.
[null,null,["Last updated 2024-04-26 UTC."],[],[],null,["# tf.raw_ops.HistogramFixedWidth\n\n\u003cbr /\u003e\n\nReturn histogram of values.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.raw_ops.HistogramFixedWidth`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/HistogramFixedWidth)\n\n\u003cbr /\u003e\n\n tf.raw_ops.HistogramFixedWidth(\n values,\n value_range,\n nbins,\n dtype=../../tf/dtypes#int32,\n name=None\n )\n\nGiven the tensor `values`, this operation returns a rank 1 histogram counting\nthe number of entries in `values` that fall into every bin. The bins are\nequal width and determined by the arguments `value_range` and `nbins`. \n\n # Bins will be: (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)\n nbins = 5\n value_range = [0.0, 5.0]\n new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]\n\n with tf.get_default_session() as sess:\n hist = tf.histogram_fixed_width(new_values, value_range, nbins=5)\n variables.global_variables_initializer().run()\n sess.run(hist) =\u003e [2, 1, 1, 0, 2]\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|---------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `values` | A `Tensor`. Must be one of the following types: `int32`, `int64`, `float32`, `float64`. Numeric `Tensor`. |\n| `value_range` | A `Tensor`. Must have the same type as `values`. Shape \\[2\\] `Tensor` of same `dtype` as `values`. values \\\u003c= value_range\\[0\\] will be mapped to hist\\[0\\], values \\\u003e= value_range\\[1\\] will be mapped to hist\\[-1\\]. |\n| `nbins` | A `Tensor` of type `int32`. Scalar `int32 Tensor`. Number of histogram bins. |\n| `dtype` | An optional [`tf.DType`](../../tf/dtypes/DType) from: `tf.int32, tf.int64`. Defaults to [`tf.int32`](../../tf#int32). |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `Tensor` of type `dtype`. ||\n\n\u003cbr /\u003e"]]
