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

for `x < 0`

, `f(x) = x`

for `x >= 0`

.

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

- 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.

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()`