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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

• Rectifier Nonlinearities Improve Neural Network Acoustic Models

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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

• Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

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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

• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

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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

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

• Inferring Nonlinear Neuronal Computation Based on Physiologically Plausible Inputs

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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

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

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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

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

• Deep Learning with S-shaped Rectified Linear Activation Units