Help protect the Great Barrier Reef with TensorFlow on Kaggle

# tf.keras.metrics.SparseCategoricalCrossentropy

Computes the crossentropy metric between the labels and predictions.

### Used in the notebooks

Used in the guide

Use this crossentropy metric when there are two or more label classes. We expect labels to be provided as integers. If you want to provide labels using `one-hot` representation, please use `CategoricalCrossentropy` metric. There should be `# classes` floating point values per feature for `y_pred` and a single floating point value per feature for `y_true`.

In the snippet below, there is a single floating point value per example for `y_true` and `# classes` floating pointing values per example for `y_pred`. The shape of `y_true` is `[batch_size]` and the shape of `y_pred` is `[batch_size, num_classes]`.

`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.
`axis` (Optional) Defaults to -1. The dimension along which the metric is computed.

#### Standalone usage:

````# y_true = one_hot(y_true) = [[0, 1, 0], [0, 0, 1]]`
`# logits = log(y_pred)`
`# softmax = exp(logits) / sum(exp(logits), axis=-1)`
`# softmax = [[0.05, 0.95, EPSILON], [0.1, 0.8, 0.1]]`
`# xent = -sum(y * log(softmax), 1)`
`# log(softmax) = [[-2.9957, -0.0513, -16.1181],`
`#                [-2.3026, -0.2231, -2.3026]]`
`# y_true * log(softmax) = [[0, -0.0513, 0], [0, 0, -2.3026]]`
`# xent = [0.0513, 2.3026]`
`# Reduced xent = (0.0513 + 2.3026) / 2`
`m = tf.keras.metrics.SparseCategoricalCrossentropy()`
`m.update_state([1, 2],`
`               [[0.05, 0.95, 0], [0.1, 0.8, 0.1]])`
`m.result().numpy()`
`1.1769392`
```
````m.reset_state()`
`m.update_state([1, 2],`
`               [[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.SparseCategoricalCrossentropy()])
``````

## Methods

### `merge_state`

View source

Merges the state from one or more metrics.

This method can be used by distributed systems to merge the state computed by different metric instances. Typically the state will be stored in the form of the metric's weights. For example, a tf.keras.metrics.Mean metric contains a list of two weight values: a total and a count. If there were two instances of a tf.keras.metrics.Accuracy that each independently aggregated partial state for an overall accuracy calculation, these two metric's states could be combined as follows:

````m1 = tf.keras.metrics.Accuracy()`
`_ = m1.update_state([[1], [2]], [[0], [2]])`
```
````m2 = tf.keras.metrics.Accuracy()`
`_ = m2.update_state([[3], [4]], [[3], [4]])`
```
````m2.merge_state([m1])`
`m2.result()````