Join the SIG TFX-Addons community and help make TFX even better!

# tft.histogram

Computes a histogram over x, given the bin boundaries or bin count.

Ex (1): counts, boundaries = histogram([0, 1, 0, 1, 0, 3, 0, 1], range(5)) counts: [4, 3, 0, 1, 0] boundaries: [0, 1, 2, 3, 4]

Ex (2): Can be used to compute class weights. counts, classes = histogram([0, 1, 0, 1, 0, 3, 0, 1], categorical=True) probabilities = counts / tf.reduce_sum(counts) class_weights = dict(map(lambda (a, b): (a.numpy(), 1.0 / b.numpy()), zip(classes, probabilities)))

`x` A `Tensor` or `SparseTensor`.
`boundaries` (Optional) A `Tensor` or `int` used to build the histogram; ignored if `categorical` is True. If possible, provide boundaries as multiple sorted values. Default to 10 intervals over the 0-1 range, or find the min/max if an int is provided (not recommended because multi-phase analysis is inefficient).
`categorical` (Optional) A `bool` that treats `x` as discrete values if true.
`name` (Optional) A name for this operation.

`counts` The histogram, as counts per bin.
`boundaries` A `Tensor` used to build the histogram representing boundaries.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]