# activation_relu: Activation functions In keras: R Interface to 'Keras'

 activation_relu R Documentation

## Activation functions

### Description

`relu(...)`: Applies the rectified linear unit activation function.

`elu(...)`: Exponential Linear Unit.

`selu(...)`: Scaled Exponential Linear Unit (SELU).

`hard_sigmoid(...)`: Hard sigmoid activation function.

`linear(...)`: Linear activation function (pass-through).

`sigmoid(...)`: Sigmoid activation function, `sigmoid(x) = 1 / (1 + exp(-x))`.

`softmax(...)`: Softmax converts a vector of values to a probability distribution.

`softplus(...)`: Softplus activation function, `softplus(x) = log(exp(x) + 1)`.

`softsign(...)`: Softsign activation function, `softsign(x) = x / (abs(x) + 1)`.

`tanh(...)`: Hyperbolic tangent activation function.

`exponential(...)`: Exponential activation function.

`gelu(...)`: Applies the Gaussian error linear unit (GELU) activation function.

`swish(...)`: Swish activation function, `swish(x) = x * sigmoid(x)`.

### Usage

``````activation_relu(x, alpha = 0, max_value = NULL, threshold = 0)

activation_elu(x, alpha = 1)

activation_selu(x)

activation_hard_sigmoid(x)

activation_linear(x)

activation_sigmoid(x)

activation_softmax(x, axis = -1)

activation_softplus(x)

activation_softsign(x)

activation_tanh(x)

activation_exponential(x)

activation_gelu(x, approximate = FALSE)

activation_swish(x)
``````

### Arguments

 `x` Tensor `alpha` Alpha value `max_value` Max value `threshold` Threshold value for thresholded activation. `axis` Integer, axis along which the softmax normalization is applied `approximate` A bool, whether to enable approximation.

### Details

Activations functions can either be used through `layer_activation()`, or through the activation argument supported by all forward layers.

• `activation_selu()` to be used together with the initialization "lecun_normal".

• `activation_selu()` to be used together with the dropout variant "AlphaDropout".

### Value

Tensor with the same shape and dtype as `x`.