Uses of Class
org.nd4j.linalg.activations.BaseActivationFunction

Packages that use BaseActivationFunction

Package	Description
org.nd4j.linalg.activations.impl

Uses of BaseActivationFunction in org.nd4j.linalg.activations.impl

Subclasses of BaseActivationFunction in org.nd4j.linalg.activations.impl

Modifier and Type	Class and Description
class	ActivationCube f(x) = x^3
class	ActivationELU f(x) = alpha * (exp(x) - 1.0); x < 0 = x ; x>= 0 alpha defaults to 1, if not specified
class	ActivationGELU GELU activation function - Gaussian Error Linear Units
class	ActivationHardSigmoid f(x) = min(1, max(0, 0.2*x + 0.5))
class	ActivationHardTanH ⎧ 1, if x > 1 f(x) = ⎨ -1, if x < -1 ⎩ x, otherwise
class	ActivationIdentity f(x) = x
class	ActivationLReLU Leaky RELU f(x) = max(0, x) + alpha * min(0, x) alpha defaults to 0.01
class	ActivationMish https://arxiv.org/ftp/arxiv/papers/1908/1908.08681.pdf
class	ActivationPReLU /** Parametrized Rectified Linear Unit (PReLU) f(x) = alpha * x for x < 0, f(x) = x for x >= 0 alpha has the same shape as x and is a learned parameter.
class	ActivationRationalTanh Rational tanh approximation From https://arxiv.org/pdf/1508.01292v3 f(x) = 1.7159 * tanh(2x/3) where tanh is approximated as follows, tanh(y) ~ sgn(y) * { 1 - 1/(1+|y|+y^2+1.41645*y^4)} Underlying implementation is in native code
class	ActivationRectifiedTanh Rectified tanh Essentially max(0, tanh(x)) Underlying implementation is in native code
class	ActivationReLU f(x) = max(0, x)
class	ActivationReLU6 f(x) = min(max(input, cutoff), 6)
class	ActivationRReLU f(x) = max(0,x) + alpha * min(0, x) alpha is drawn from uniform(l,u) during training and is set to l+u/2 during test l and u default to 1/8 and 1/3 respectively Empirical Evaluation of Rectified Activations in Convolutional Network
class	ActivationSELU https://arxiv.org/pdf/1706.02515.pdf
class	ActivationSigmoid f(x) = 1 / (1 + exp(-x))
class	ActivationSoftmax f_i(x) = exp(x_i - shift) / sum_j exp(x_j - shift) where shift = max_i(x_i)
class	ActivationSoftPlus f(x) = log(1+e^x)
class	ActivationSoftSign f_i(x) = x_i / (1+|x_i|)
class	ActivationSwish f(x) = x * sigmoid(x)
class	ActivationTanH f(x) = (exp(x) - exp(-x)) / (exp(x) + exp(-x))
class	ActivationThresholdedReLU Thresholded RELU f(x) = x for x > theta, f(x) = 0 otherwise.