data_mining:neural_network:gradient_descent [AE Wiki]

This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong.
====== Gradient descent ======

===== Mini batch gradient descent =====

For t=1, ..., number_of_batches:
  Vectorized Forward prop on $X^{t}$
    $Z^{[1]} = W^{[1]} X^{t} + b^{[1]}$
    $A^{[1]} = g^{[1]}(Z^{[1]})$
    ...
    $A^{[L]} = g^{[L]}(Z^{[L]})$
  Compute cost $J^{[t]}$ = 1/1000 * ...
  Backprop to compute gradients for $J^{[t]}$
  Update weights $W^{[l]} = W^{[l]} - \alpha d W^{[l]}; b^{[l]} = b^{[l]} - \alpha d b^{[l]}$

===== Exponentially weighted averages =====

$V_t = \beta V_{t-1} + (1-\beta) \Theta_t$

$\beta = 0.98$ is smoother than $\beta = 0.5$ (2 days for average in latter case)

$V_\Theta = 0$

$V_\Theta = \beta V + (1-\beta) \Theta_1$

$V_\Theta = \beta V + (1-\beta) \Theta_2$

...

Easy method to compute averages for longer periods

==== Bias correction ====

$V_0 = 0$

$V_1 = \beta V_0 + 0,02 \Theta_1$

$V_\Theta = \beta V + 0,02 \Theta_2$

...

Corrected:

$\frac{V_t}{1-\beta^t}$
===== Gradient Descent with Momentum =====

Idea: Compute exponentially weighted average of gradients and use it to update weights.

$V_{dW} = \beta V_{dW} + (1-\beta) dW$

$V_{db} = \beta V_{db} + (1-\beta) db$

$W = W - \alpha V_{dW}$

$b = b - \alpha V_{db}$

$\beta$ friction; $V_{db}$ velocity; $db$ acceleration

Often $\beta = 0.9$ is used.
===== RMSprop =====

Root mean squared

Goal: Slow movements in vertical direction (b direction), fast in horizontal (w direction; bowl is wider than high)

Compute dW, db on minibatch

$s_{dW} = \beta s_{dW} + (1-\beta) d W^2$ (element-wise squared), small

$s_{db} = \beta s_{db} + (1-\beta) d b^2$ large

$W = W - \alpha dW/\sqrt{s_{dW}}$ (same for b)
===== Adam =====

Adaptive moment estimation

Momentum + RMSprop + Bias correction

  * $\alpha$: to be tuned
  * $\beta_1$: 0.9
  * $\beta_2$: 0.999
  * $\sigma$: $10^{-8}$

===== Learning rate decay =====

$\alpha = \frac{1}{1+ \text{decay_rate} * \text{epoch_num}} \alpha_0$

or

$\alpha = 0,95^{\text{epoch_num}} \alpha_0$


===== Saddle points =====

In high-dimensional spaces it's more likely to end up at a saddle point (than in local optima). E.g. 20000 parameter, highly unlikely that it's a local minimum you get stuck. Plateus make learning slow.