/TensorFlow 1.15

# tf.keras.metrics.SparseCategoricalCrossentropy

Computes the crossentropy metric between the labels and predictions.

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]`.

#### Usage:

```m = tf.keras.metrics.SparseCategoricalCrossentropy()
m.update_state(
[1, 2],
[[0.05, 0.95, 0], [0.1, 0.8, 0.1]])

# 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

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