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# tf.keras.metrics.LogCoshError

Computes the logarithm of the hyperbolic cosine of the prediction error.

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

`logcosh = log((exp(x) + exp(-x))/2)`, where x is the error (y_pred - y_true)

`name` (Optional) string name of the metric instance.
`dtype` (Optional) data type of the metric result.

#### Standalone usage:

````m = tf.keras.metrics.LogCoshError()`
`m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])`
`m.result().numpy()`
`0.10844523`
```
````m.reset_state()`
`m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],`
`               sample_weight=[1, 0])`
`m.result().numpy()`
`0.21689045`
```

Usage with `compile()` API:

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

## 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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