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tf.keras.metrics.CategoricalCrossentropy

Computes the crossentropy metric between the labels and predictions.

Inherits From: `Mean`, `Metric`, `Layer`, `Module`

This is the crossentropy metric class to be used when there are multiple label classes (2 or more). Here we assume that labels are given as a `one_hot` representation. eg., When labels values are [2, 0, 1], `y_true` = [[0, 0, 1], [1, 0, 0], [0, 1, 0]].

`name` (Optional) string name of the metric instance.
`dtype` (Optional) data type of the metric result.
`from_logits` (Optional) Whether output is expected to be a logits tensor. By default, we consider that output encodes a probability distribution.
`label_smoothing` (Optional) Float in [0, 1]. When > 0, label values are smoothed, meaning the confidence on label values are relaxed. e.g. `label_smoothing=0.2` means that we will use a value of `0.1` for label `0` and `0.9` for label `1`"

Standalone usage:

````# EPSILON = 1e-7, y = y_true, y` = y_pred`
`# y` = clip_ops.clip_by_value(output, EPSILON, 1. - EPSILON)`
`# y` = [[0.05, 0.95, EPSILON], [0.1, 0.8, 0.1]]`
`# xent = -sum(y * log(y'), axis = -1)`
`#      = -((log 0.95), (log 0.1))`
`#      = [0.051, 2.302]`
`# Reduced xent = (0.051 + 2.302) / 2`
`m = tf.keras.metrics.CategoricalCrossentropy()`
`m.update_state([[0, 1, 0], [0, 0, 1]],`
`               [[0.05, 0.95, 0], [0.1, 0.8, 0.1]])`
`m.result().numpy()`
`1.1769392`
```
````m.reset_state()`
`m.update_state([[0, 1, 0], [0, 0, 1]],`
`               [[0.05, 0.95, 0], [0.1, 0.8, 0.1]],`
`               sample_weight=tf.constant([0.3, 0.7]))`
`m.result().numpy()`
`1.6271976`
```

Usage with `compile()` API:

``````model.compile(
optimizer='sgd',
loss='mse',
metrics=[tf.keras.metrics.CategoricalCrossentropy()])
``````

Methods

`reset_state`

View source

Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

`result`

View source

Computes and returns the metric value tensor.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.

`update_state`

View source

Accumulates metric statistics.

`y_true` and `y_pred` should have the same shape.

Args
`y_true` Ground truth values. shape = `[batch_size, d0, .. dN]`.
`y_pred` The predicted values. shape = `[batch_size, d0, .. dN]`.
`sample_weight` Optional `sample_weight` acts as a coefficient for the metric. If a scalar is provided, then the metric is simply scaled by the given value. If `sample_weight` is a tensor of size `[batch_size]`, then the metric for each sample of the batch is rescaled by the corresponding element in the `sample_weight` vector. If the shape of `sample_weight` is `[batch_size, d0, .. dN-1]` (or can be broadcasted to this shape), then each metric element of `y_pred` is scaled by the corresponding value of `sample_weight`. (Note on `dN-1`: all metric functions reduce by 1 dimension, usually the last axis (-1)).

Returns
Update op.

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[{ "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" }]