[
https://issues.apache.org/jira/browse/MADLIB-1210?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Frank McQuillan updated MADLIB-1210:
------------------------------------
Description:
Story
As a data scientist,
I want to use momentum methods in MLP,
so that I get significantly better convergence behavior.
Details
Adding momentum will get the MADlib MLP algorithm closer to state of the art.
1) Implement momentum term, default value ~0.9
Ref [1]:
"Momentum update is another approach that almost always enjoys better converge
rates on deep networks."
2) Implement Nesterov momentum, default TRUE
Ref [1]:
"Nesterov Momentum is a slightly different version of the momentum update that
has recently been gaining popularity. It enjoys stronger theoretical converge
guarantees for convex functions and in practice it also consistently works
slightly better than standard momentum."
Ref [2]
"Nesterov’s accelerated gradient (abbrv. NAG; Nesterov, 1983) is a first-order
optimization method which is proven to have a better convergence rate guarantee
than gradient descent for general convex functions with Lipshitz-continuous
derivatives (O(1/T2) versus O(1/T))"
Interface
There are 2 new optimization params for momentum, which apply for both
classification and regression:
{code}
'learning_rate_init = <value>,
learning_rate_policy = <value>,
gamma = <value>,
power = <value>,
iterations_per_step = <value>,
n_iterations = <value>,
n_tries = <value>,
lambda = <value>,
tolerance = <value>,
batch_size = <value>,
n_epochs = <value>,
momentum = <value>,
nesterov_momentum= <value>'
momentum
Default: 0.9. Momentum can help accelerate learning and
avoid local minima when using gradient descent. Value must be in the
range 0 to 1, where 0 means no momentum.
nesterov_momentum
Default: TRUE. Nesterov momentum can provide better results than using
classical momentum alone, due to its look ahead characteristics.
In classical momentum you first correct velocity and step with that
velocity, whereas in Nesterov momentum you first step in the velocity
direction then make a correction to the velocity vector based on
new location.
Nesterov momentum is only used when the 'momentum' parameter is > 0.
{code}
Open questions
1) Does momentum and Nesterov momentum work equally well with and without
mini-batching?
Is there any guidance we need to give to users on this?
Acceptance
[1] Use a 2D Rosenbock
[1] Find/create a dataset that can be used to compare the usefulness of
momentum with and without Nesterov, mini-batch, and SGD. This usefulness can be
compared based on convergence, which includes both speed and accuracy.
References
[1] http://cs231n.github.io/neural-networks-3/#sgd
[2] http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf, a link
from previous source.
[3]
http://ruder.io/optimizing-gradient-descent/index.html#gradientdescentoptimizationalgorithms
[4]
http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html
[5] https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
was:
Story
As a data scientist,
I want to use momentum methods in MLP,
so that I get significantly better convergence behavior.
Details
Adding momentum will get the MADlib MLP algorithm closer to state of the art.
1) Implement momentum term, default value ~0.9
Ref [1]:
"Momentum update is another approach that almost always enjoys better converge
rates on deep networks."
2) Implement Nesterov momentum, default TRUE
Ref [1]:
"Nesterov Momentum is a slightly different version of the momentum update that
has recently been gaining popularity. It enjoys stronger theoretical converge
guarantees for convex functions and in practice it also consistently works
slightly better than standard momentum."
Ref [2]
"Nesterov’s accelerated gradient (abbrv. NAG; Nesterov, 1983) is a first-order
optimization method which is proven to have a better convergence rate guarantee
than gradient descent for general convex functions with Lipshitz-continuous
derivatives (O(1/T2) versus O(1/T))"
Interface
There are 2 new optimization params for momentum, which apply for both
classification and regression:
{code}
'learning_rate_init = <value>,
learning_rate_policy = <value>,
gamma = <value>,
power = <value>,
iterations_per_step = <value>,
n_iterations = <value>,
n_tries = <value>,
lambda = <value>,
tolerance = <value>,
batch_size = <value>,
n_epochs = <value>,
momentum = <value>,
nesterov_momentum= <value>'
momentum
Default: 0.9. Momentum can help accelerate learning and
avoid local minima when using gradient descent. Value must be in the
range 0 to 1, where 0 means no momentum.
nesterov_momentum
Default: TRUE. Nesterov momentum can provide better results than using
classical momentum alone, due to its look ahead characteristics.
In classical momentum you first correct velocity and step with that
velocity, whereas in Nesterov momentum you first step in the velocity
direction then make a correction to the velocity vector based on
new location.
Nesterov momentum is only used when the 'momentum' parameter is > 0.
{code}
Open questions
1) Does momentum and Nesterov momentum work equally well with and without
mini-batching?
Is there any guidance we need to give to users on this?
Acceptance
[1] Find/create a dataset that can be used to compare the usefulness of
momentum with and without Nesterov, mini-batch, and SGD. This usefulness can be
compared based on convergence, which includes both speed and accuracy.
References
[1] http://cs231n.github.io/neural-networks-3/#sgd
[2] http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf, a link
from previous source.
[3]
http://ruder.io/optimizing-gradient-descent/index.html#gradientdescentoptimizationalgorithms
[4]
http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html
[5] https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
> Add momentum methods to MLP
> ---------------------------
>
> Key: MADLIB-1210
> URL: https://issues.apache.org/jira/browse/MADLIB-1210
> Project: Apache MADlib
> Issue Type: New Feature
> Components: Module: Neural Networks
> Reporter: Frank McQuillan
> Priority: Major
> Fix For: v1.15
>
>
> Story
> As a data scientist,
> I want to use momentum methods in MLP,
> so that I get significantly better convergence behavior.
> Details
> Adding momentum will get the MADlib MLP algorithm closer to state of the art.
> 1) Implement momentum term, default value ~0.9
> Ref [1]:
> "Momentum update is another approach that almost always enjoys better
> converge rates on deep networks."
> 2) Implement Nesterov momentum, default TRUE
> Ref [1]:
> "Nesterov Momentum is a slightly different version of the momentum update
> that has recently been gaining popularity. It enjoys stronger theoretical
> converge guarantees for convex functions and in practice it also consistently
> works slightly better than standard momentum."
> Ref [2]
> "Nesterov’s accelerated gradient (abbrv. NAG; Nesterov, 1983) is a
> first-order optimization method which is proven to have a better convergence
> rate guarantee than gradient descent for general convex functions with
> Lipshitz-continuous derivatives (O(1/T2) versus O(1/T))"
> Interface
> There are 2 new optimization params for momentum, which apply for both
> classification and regression:
> {code}
> 'learning_rate_init = <value>,
> learning_rate_policy = <value>,
> gamma = <value>,
> power = <value>,
> iterations_per_step = <value>,
> n_iterations = <value>,
> n_tries = <value>,
> lambda = <value>,
> tolerance = <value>,
> batch_size = <value>,
> n_epochs = <value>,
> momentum = <value>,
> nesterov_momentum= <value>'
> momentum
> Default: 0.9. Momentum can help accelerate learning and
> avoid local minima when using gradient descent. Value must be in the
> range 0 to 1, where 0 means no momentum.
> nesterov_momentum
> Default: TRUE. Nesterov momentum can provide better results than using
> classical momentum alone, due to its look ahead characteristics.
> In classical momentum you first correct velocity and step with that
> velocity, whereas in Nesterov momentum you first step in the velocity
> direction then make a correction to the velocity vector based on
> new location.
> Nesterov momentum is only used when the 'momentum' parameter is > 0.
> {code}
> Open questions
> 1) Does momentum and Nesterov momentum work equally well with and without
> mini-batching?
> Is there any guidance we need to give to users on this?
> Acceptance
> [1] Use a 2D Rosenbock
> [1] Find/create a dataset that can be used to compare the usefulness of
> momentum with and without Nesterov, mini-batch, and SGD. This usefulness can
> be compared based on convergence, which includes both speed and accuracy.
> References
> [1] http://cs231n.github.io/neural-networks-3/#sgd
> [2] http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf, a
> link from previous source.
> [3]
> http://ruder.io/optimizing-gradient-descent/index.html#gradientdescentoptimizationalgorithms
> [4]
> http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html
> [5] https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
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