CFD in buildings

In the context of artificial neural networks, the rectifier is an activation function defined as

$f(x)=\max(0,x)$

where x is the input to a neuron. This activation function has been argued to be more biologically plausible than the widely used logistic sigmoid (which is inspired by probability theory; see logistic regression) and its more practical counterpart, the hyperbolic tangent. A unit employing the rectifier is also called a rectified linear unit (ReLU). A smooth approximate to the rectifier is the analytic function

$f(x)=\log(1+e^{x})$

which is called the softplus function. The derivative of softplus is $f'(x)=e^{x}/(e^{x}+1)=1/(1+e^{-x})$, i.e. the logistic function.

Rectified linear units find applications in computer vision using deep neural nets.

Noisy ReLUs

Rectified linear units can be extended to include Gaussian noise, making them noisy ReLUs, giving

$f(x)=\max(0,x+{\mathcal {N}}(0,\sigma (x)))$

Noisy ReLUs have been used with some success in restricted Boltzmann machines for computer vision tasks.

References

43 year old Petroleum Engineer Harry from Deep River, usually spends time with hobbies and interests like renting movies, property developers in singapore new condominium and vehicle racing. Constantly enjoys going to destinations like Camino Real de Tierra Adentro.