# layer_activation_elu

## Exponential Linear Unit.

## Description

It follows: `f(x) = alpha * (exp(x) - 1.0)`

for `x < 0`

, `f(x) = x`

for `x >= 0`

.

## Usage

```
layer_activation_elu(
object, alpha = 1,
input_shape = NULL,
batch_input_shape = NULL,
batch_size = NULL,
dtype = NULL,
name = NULL,
trainable = NULL,
weights = NULL
)
```

## Arguments

Arguments | Description |
---|---|

object | What to compose the new `Layer` instance with. Typically a Sequential model or a Tensor (e.g., as returned by `layer_input()` ). The return value depends on `object` . If `object` is: - missing or `NULL` , the `Layer` instance is returned. - a `Sequential` model, the model with an additional layer is returned. - a Tensor, the output tensor from `layer_instance(object)` is returned. |

alpha | Scale for the negative factor. |

input_shape | Input shape (list of integers, does not include the samples axis) which is required when using this layer as the first layer in a model. |

batch_input_shape | Shapes, including the batch size. For instance, `batch_input_shape=c(10, 32)` indicates that the expected input will be batches of 10 32-dimensional vectors. `batch_input_shape=list(NULL, 32)` indicates batches of an arbitrary number of 32-dimensional vectors. |

batch_size | Fixed batch size for layer |

dtype | The data type expected by the input, as a string (`float32` , `float64` , `int32` …) |

name | An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn’t provided. |

trainable | Whether the layer weights will be updated during training. |

weights | Initial weights for layer. |

## See Also

Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). Other activation layers: `layer_activation_leaky_relu()`

, `layer_activation_parametric_relu()`

, `layer_activation_relu()`

, `layer_activation_selu()`

, `layer_activation_softmax()`

, `layer_activation_thresholded_relu()`

, `layer_activation()`