import jax import jax.numpy as jnp class Activation: name2func = {} @staticmethod def sigmoid_act(z): z = jnp.clip(z * 5, -60, 60) return 1 / (1 + jnp.exp(-z)) @staticmethod def tanh_act(z): z = jnp.clip(z * 2.5, -60, 60) return jnp.tanh(z) @staticmethod def sin_act(z): z = jnp.clip(z * 5, -60, 60) return jnp.sin(z) @staticmethod def gauss_act(z): z = jnp.clip(z * 5, -3.4, 3.4) return jnp.exp(-z ** 2) @staticmethod def relu_act(z): return jnp.maximum(z, 0) @staticmethod def elu_act(z): return jnp.where(z > 0, z, jnp.exp(z) - 1) @staticmethod def lelu_act(z): leaky = 0.005 return jnp.where(z > 0, z, leaky * z) @staticmethod def selu_act(z): lam = 1.0507009873554804934193349852946 alpha = 1.6732632423543772848170429916717 return jnp.where(z > 0, lam * z, lam * alpha * (jnp.exp(z) - 1)) @staticmethod def softplus_act(z): z = jnp.clip(z * 5, -60, 60) return 0.2 * jnp.log(1 + jnp.exp(z)) @staticmethod def identity_act(z): return z @staticmethod def clamped_act(z): return jnp.clip(z, -1, 1) @staticmethod def inv_act(z): z = jnp.maximum(z, 1e-7) return 1 / z @staticmethod def log_act(z): z = jnp.maximum(z, 1e-7) return jnp.log(z) @staticmethod def exp_act(z): z = jnp.clip(z, -60, 60) return jnp.exp(z) @staticmethod def abs_act(z): return jnp.abs(z) @staticmethod def hat_act(z): return jnp.maximum(0, 1 - jnp.abs(z)) @staticmethod def square_act(z): return z ** 2 @staticmethod def cube_act(z): return z ** 3 def act(idx, z, act_funcs): """ calculate activation function for each node """ idx = jnp.asarray(idx, dtype=jnp.int32) # change idx from float to int res = jax.lax.switch(idx, act_funcs, z) return res