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tf.keras.losses.categorical_crossentropy

Computes the categorical crossentropy loss.

Usage:

````y_true = [[0, 1, 0], [0, 0, 1]]`
`y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]`
`loss = tf.keras.losses.categorical_crossentropy(y_true, y_pred)`
`assert loss.shape == (2,)`
`loss.numpy()`
`array([0.0513, 2.303], dtype=float32)`
```

`y_true` Tensor of one-hot true targets.
`y_pred` Tensor of predicted targets.
`from_logits` Whether `y_pred` is expected to be a logits tensor. By default, we assume that `y_pred` encodes a probability distribution.
`label_smoothing` Float in [0, 1]. If > `0` then smooth the labels.

Categorical crossentropy loss value.

[{ "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"label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]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" }]