Search results
Results from the WOW.Com Content Network
Backpropagation then consists essentially of evaluating this expression from right to left (equivalently, multiplying the previous expression for the derivative from left to right), computing the gradient at each layer on the way; there is an added step, because the gradient of the weights is not just a subexpression: there's an extra ...
The perceptron uses the Heaviside step function as the activation function (), and that means that ′ does not exist at zero, and is equal to zero elsewhere, which makes the direct application of the delta rule impossible.
Back_Propagation_Through_Time(a, y) // a[t] is the input at time t. y[t] is the output Unfold the network to contain k instances of f do until stopping criterion is met: x := the zero-magnitude vector // x is the current context for t from 0 to n − k do // t is time. n is the length of the training sequence Set the network inputs to x, a[t ...
A perceptron traditionally used a Heaviside step function as its nonlinear activation function. However, the backpropagation algorithm requires that modern MLPs use continuous activation functions such as sigmoid or ReLU. [8] Multilayer perceptrons form the basis of deep learning, [9] and are applicable across a vast set of diverse domains. [10]
Backpropagation of errors in multilayer perceptrons, a technique used in machine learning, is a special case of reverse accumulation. [2] Forward accumulation was introduced by R.E. Wengert in 1964. [13] According to Andreas Griewank, reverse accumulation has been suggested since the late 1960s, but the inventor is unknown. [14]
Neural backpropagation is the phenomenon in which, after the action potential of a neuron creates a voltage spike down the axon (normal propagation), another impulse is generated from the soma and propagates towards the apical portions of the dendritic arbor or dendrites (from which much of the original input current originated).
The backpropagation step then repeatedly pops off vertices, which are naturally sorted by their distance from , descending. For each popped node v {\displaystyle v} , we iterate over its predecessors u ∈ p ( v ) {\displaystyle u\in p(v)} : the contribution of v {\displaystyle v} towards δ s ( u ) {\displaystyle \delta _{s}(u)} is added, that is,
This can perform significantly better than "true" stochastic gradient descent described, because the code can make use of vectorization libraries rather than computing each step separately as was first shown in [6] where it was called "the bunch-mode back-propagation algorithm". It may also result in smoother convergence, as the gradient ...