### LeakyReLU

```
keras.layers.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.PReLU(alpha_initializer='zeros', alpha_regularizer=None, alpha_constraint=None, shared_axes=None)
```

Parametric Rectified Linear Unit.

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

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

,
where `alpha`

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**

**alpha_initializer**: initializer function for the weights.**alpha_regularizer**: regularizer for the weights.**alpha_constraint**: constraint for the weights.**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.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**

### ThresholdedReLU

```
keras.layers.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**

### Softmax

```
keras.layers.Softmax(axis=-1)
```

Softmax activation function.

**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**

axis: Integer, axis along which the softmax normalization is applied.

### ReLU

```
keras.layers.ReLU(max_value=None, negative_slope=0.0, threshold=0.0)
```

Rectified Linear Unit activation function.

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) = negative_slope * (x - threshold)`

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**

max_value: float >= 0. Maximum activation value. negative_slope: float >= 0. Negative slope coefficient. threshold: float. Threshold value for thresholded activation.