[source]

### ELU

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

Exponential Linear Unit: `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

[source]

### LeakyReLU

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

Special version of a Rectified Linear Unit that 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.

[source]

### PReLU

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

Parametric Rectified Linear Unit: `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

[source]

### ParametricSoftplus

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

Parametric Softplus: `alpha * log(1 + exp(beta * 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_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

[source]

### ThresholdedReLU

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

Thresholded Rectified Linear Unit: `f(x) = x for x > theta` `f(x) = 0 otherwise`.

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

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

References

[source]

### 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.

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

• 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