[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.

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)

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.


[source]

ELU

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

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]

ParametricSoftplus

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

Parametric Softplus of the form: 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


[source]

ThresholdedLinear

keras.layers.advanced_activations.ThresholdedLinear(theta=1.0)

Thresholded Linear Activation.

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

Zero-Bias Autoencoders and the Benefits of Co-Adapting Features


[source]

ThresholdedReLU

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

Thresholded Rectified Activation.

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

Zero-Bias Autoencoders and the Benefits of Co-Adapting Features


[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')

SReLU

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

References

Deep Learning with S-shaped Rectified Linear Activation Units