### LeakyReLU

```
keras.layers.advanced_activations.LeakyReLU(alpha=0.3)
```

Leaky version of a Rectified Linear Unit.

It allows a small gradient when the unit is not active:
`f(x) = alpha * x for x < 0`

,
`f(x) = x for x >= 0`

.

**Input shape**

Arbitrary. Use the keyword argument `input_shape`

(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.

**Output shape**

Same shape as the input.

**Arguments**

**alpha**: float >= 0. Negative slope coefficient.

**References**

### PReLU

```
keras.layers.advanced_activations.PReLU(init='zero', weights=None, shared_axes=None)
```

Parametric Rectified Linear Unit.

It follows:
`f(x) = alphas * x for x < 0`

,
`f(x) = x for x >= 0`

,
where `alphas`

is a learned array with the same shape as x.

**Input shape**

Arbitrary. Use the keyword argument `input_shape`

(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.

**Output shape**

Same shape as the input.

**Arguments**

**init**: initialization function for the weights.**weights**: initial weights, as a list of a single Numpy array.**shared_axes**: the axes along which to share learnable parameters for the activation function. For example, if the incoming feature maps are from a 2D convolution with output shape`(batch, height, width, channels)`

, and you wish to share parameters across space so that each filter only has one set of parameters, set`shared_axes=[1, 2]`

.

**References**

### ELU

```
keras.layers.advanced_activations.ELU(alpha=1.0)
```

Exponential Linear Unit.

It follows:
`f(x) = alpha * (exp(x) - 1.) for x < 0`

,
`f(x) = x for x >= 0`

.

**Input shape**

Arbitrary. Use the keyword argument `input_shape`

(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.

**Output shape**

Same shape as the input.

**Arguments**

**alpha**: scale for the negative factor.

**References**

### ParametricSoftplus

```
keras.layers.advanced_activations.ParametricSoftplus(alpha_init=0.2, beta_init=5.0, weights=None, shared_axes=None)
```

Parametric Softplus.

It follows:
`f(x) = alpha * log(1 + exp(beta * x))`

**Input shape**

`input_shape`

(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.

**Output shape**

Same shape as the input.

**Arguments**

**alpha_init**: float. Initial value of the alpha weights.**beta_init**: float. Initial values of the beta weights.**weights**: initial weights, as a list of 2 numpy arrays.**shared_axes**: the axes along which to share learnable parameters for the activation function. For example, if the incoming feature maps are from a 2D convolution with output shape`(batch, height, width, channels)`

, and you wish to share parameters across space so that each filter only has one set of parameters, set`shared_axes=[1, 2]`

.

**References**

### ThresholdedReLU

```
keras.layers.advanced_activations.ThresholdedReLU(theta=1.0)
```

Thresholded Rectified Linear Unit.

It follows:
`f(x) = x for x > theta`

,
`f(x) = 0 otherwise`

.

**Input shape**

`input_shape`

(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.

**Output shape**

Same shape as the input.

**Arguments**

**theta**: float >= 0. Threshold location of activation.

**References**

### SReLU

```
keras.layers.advanced_activations.SReLU(t_left_init='zero', a_left_init='glorot_uniform', t_right_init='glorot_uniform', a_right_init='one', shared_axes=None)
```

S-shaped Rectified Linear Unit.

It follows:
`f(x) = t^r + a^r(x - t^r) for x >= t^r`

,
`f(x) = x for t^r > x > t^l`

,
`f(x) = t^l + a^l(x - t^l) for x <= t^l`

.

**Input shape**

`input_shape`

(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.

**Output shape**

Same shape as the input.

**Arguments**

**t_left_init**: initialization function for the left part intercept**a_left_init**: initialization function for the left part slope**t_right_init**: initialization function for the right part intercept**a_right_init**: initialization function for the right part slope**shared_axes**: the axes along which to share learnable parameters for the activation function. For example, if the incoming feature maps are from a 2D convolution with output shape`(batch, height, width, channels)`

, and you wish to share parameters across space so that each filter only has one set of parameters, set`shared_axes=[1, 2]`

.

**References**