/TensorFlow 1.15

# tf.keras.metrics.CategoricalCrossentropy

Computes the crossentropy metric between the labels and predictions.

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]].

#### Usage:

```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]])

# 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

print('Final result: ', m.result().numpy())  # Final result: 1.176
```

Usage with tf.keras API:

```model = tf.keras.Model(inputs, outputs)
model.compile(
'sgd',
loss='mse',
metrics=[tf.keras.metrics.CategoricalCrossentropy()])
```
Args
`name` (Optional) string name of the metric instance.
`dtype` (Optional) data type of the metric result.
`from_logits` (Optional ) 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]. 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`"
Args
`fn` The metric function to wrap, with signature `fn(y_true, y_pred, **kwargs)`.
`name` (Optional) string name of the metric instance.
`dtype` (Optional) data type of the metric result.
`**kwargs` The keyword arguments that are passed on to `fn`.

## Methods

### `reset_states`

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Resets all of the metric state variables.

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

### `result`

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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`

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Accumulates metric statistics.

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

Args
`y_true` The ground truth values.
`y_pred` The predicted values.
`sample_weight` Optional weighting of each example. Defaults to 1. Can be a `Tensor` whose rank is either 0, or the same rank as `y_true`, and must be broadcastable to `y_true`.
Returns
Update op.