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## Usage of activations
Activations can either be used through an `Activation` layer, or through the `activation` argument supported by all forward layers:
```python
from keras.layers import Activation, Dense
model.add(Dense(64))
model.add(Activation('tanh'))
```
This is equivalent to:
```python
model.add(Dense(64, activation='tanh'))
```
You can also pass an element-wise TensorFlow/Theano/CNTK function as an activation:
```python
from keras import backend as K
model.add(Dense(64, activation=K.tanh))
```
## Available activations
### elu
```python
keras.activations.elu(x, alpha=1.0)
```
Exponential linear unit.
__Arguments__
- __x__: Input tensor.
- __alpha__: A scalar, slope of negative section.
__Returns__
The exponential linear activation: `x` if `x > 0` and
`alpha * (exp(x)-1)` if `x < 0`.
__References__
- [Fast and Accurate Deep Network Learning by Exponential
Linear Units (ELUs)](https://arxiv.org/abs/1511.07289)
----
### softmax
```python
keras.activations.softmax(x, axis=-1)
```
Softmax activation function.
__Arguments__
- __x__: Input tensor.
- __axis__: Integer, axis along which the softmax normalization is applied.
__Returns__
Tensor, output of softmax transformation.
__Raises__
- __ValueError__: In case `dim(x) == 1`.
----
### selu
```python
keras.activations.selu(x)
```
Scaled Exponential Linear Unit (SELU).
SELU is equal to: `scale * elu(x, alpha)`, where alpha and scale
are predefined constants. The values of `alpha` and `scale` are
chosen so that the mean and variance of the inputs are preserved
between two consecutive layers as long as the weights are initialized
correctly (see `lecun_normal` initialization) and the number of inputs
is "large enough" (see references for more information).
__Arguments__
- __x__: A tensor or variable to compute the activation function for.
__Returns__
The scaled exponential unit activation: `scale * elu(x, alpha)`.
__Note__
- To be used together with the initialization "lecun_normal".
- To be used together with the dropout variant "AlphaDropout".
__References__
- [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
----
### softplus
```python
keras.activations.softplus(x)
```
Softplus activation function.
__Arguments__
- __x__: Input tensor.
__Returns__
The softplus activation: `log(exp(x) + 1)`.
----
### softsign
```python
keras.activations.softsign(x)
```
Softsign activation function.
__Arguments__
- __x__: Input tensor.
__Returns__
The softsign activation: `x / (abs(x) + 1)`.
----
### relu
```python
keras.activations.relu(x, alpha=0.0, max_value=None, threshold=0.0)
```
Rectified Linear Unit.
With default values, it returns element-wise `max(x, 0)`.
Otherwise, it follows:
`f(x) = max_value` for `x >= max_value`,
`f(x) = x` for `threshold <= x < max_value`,
`f(x) = alpha * (x - threshold)` otherwise.
__Arguments__
- __x__: Input tensor.
- __alpha__: float. Slope of the negative part. Defaults to zero.
- __max_value__: float. Saturation threshold.
- __threshold__: float. Threshold value for thresholded activation.
__Returns__
A tensor.
----
### tanh
```python
keras.activations.tanh(x)
```
Hyperbolic tangent activation function.
__Arguments__
- __x__: Input tensor.
__Returns__
The hyperbolic activation:
`tanh(x) = (exp(x) - exp(-x)) / (exp(x) + exp(-x))`
----
### sigmoid
```python
keras.activations.sigmoid(x)
```
Sigmoid activation function.
__Arguments__
- __x__: Input tensor.
__Returns__
The sigmoid activation: `1 / (1 + exp(-x))`.
----
### hard_sigmoid
```python
keras.activations.hard_sigmoid(x)
```
Hard sigmoid activation function.
Faster to compute than sigmoid activation.
__Arguments__
- __x__: Input tensor.
__Returns__
Hard sigmoid activation:
- `0` if `x < -2.5`
- `1` if `x > 2.5`
- `0.2 * x + 0.5` if `-2.5 <= x <= 2.5`.
----
### exponential
```python
keras.activations.exponential(x)
```
Exponential (base e) activation function.
__Arguments__
- __x__: Input tensor.
__Returns__
Exponential activation: `exp(x)`.
----
### linear
```python
keras.activations.linear(x)
```
Linear (i.e. identity) activation function.
__Arguments__
- __x__: Input tensor.
__Returns__
Input tensor, unchanged.
## On "Advanced Activations"
Activations that are more complex than a simple TensorFlow/Theano/CNTK function (eg. learnable activations, which maintain a state) are available as [Advanced Activation layers](layers/advanced-activations.md), and can be found in the module `keras.layers.advanced_activations`. These include `PReLU` and `LeakyReLU`.
|
