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https://issues.apache.org/jira/browse/MAHOUT-364?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12856698#action_12856698
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Ted Dunning commented on MAHOUT-364:
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{quote}
By the way is GPL3 Apache 2 compatible?
{quote}
No.
Apache licenses allow any kind of reuse with only the mildest of conditions.
They are similar to creative commons licenses that allow re-use with
attribution.
GPL licenses require that derived works also be GPL licensed and that source
code be provide for all derived works.
See here: http://www.opensource.org/licenses/gpl-3.0.html and here:
http://www.opensource.org/licenses/apache2.0.php
> [GSOC] Proposal to implement Neural Network with backpropagation learning on
> Hadoop
> -----------------------------------------------------------------------------------
>
> Key: MAHOUT-364
> URL: https://issues.apache.org/jira/browse/MAHOUT-364
> Project: Mahout
> Issue Type: New Feature
> Reporter: Zaid Md. Abdul Wahab Sheikh
>
> Proposal Title: Implement Multi-Layer Perceptrons with backpropagation
> learning on Hadoop (addresses issue Mahout-342)
> Student Name: Zaid Md. Abdul Wahab Sheikh
> Student E-mail: (gmail id) sheikh.zaid
> h2. I. Brief Description
> A feedforward neural network (NN) reveals several degrees of parallelism
> within it such as weight parallelism, node parallelism, network parallelism,
> layer parallelism and training parallelism. However network based parallelism
> requires fine-grained synchronization and communication and thus is not
> suitable for map/reduce based algorithms. On the other hand, training-set
> parallelism is coarse grained. This can be easily exploited on Hadoop which
> can split up the input among different mappers. Each of the mappers will then
> propagate the 'InputSplit' through their own copy of the complete neural
> network.
> The backpropagation algorithm will operate in batch mode. This is because
> updating a common set of parameters after each training example creates a
> bottleneck for parallelization. The overall error gradient vector calculation
> can be parallelized by calculating the gradients from each training vector in
> the Mapper, combining them to get partial batch gradients and then adding
> them in a reducer to get the overall batch gradient.
> In a similiar manner, error function evaluations during line searches (for
> the conjugate gradient and quasi-Newton algorithms) can be efficiently
> parallelized.
> Lastly, to avoid local minima in its error function, we can take advantage of
> training session parallelism to start multiple training sessions in parallel
> with different initial weights (simulated annealing).
> h2. II. Detailed Proposal
> The most important step is to design the base neural network classes in such
> a way that other NN architectures like Hopfield nets, Boltzman machines, SOM
> etc can be easily implemented by deriving from these base classes. For that I
> propose to implement a set of core classes that correspond to basic neural
> network concepts like artificial neuron, neuron layer, neuron connections,
> weight, transfer function, input function, learning rule etc. This
> architecture is inspired from that of the opensource Neuroph neural network
> framework (http://imgur.com/gDIOe.jpg). This design of the base architecture
> allows for great flexibility in deriving newer NNs and learning rules. All
> that needs to be done is to derive from the NeuralNetwork class, provide the
> method for network creation, create a new training method by deriving from
> LearningRule, and then add that learning rule to the network during creation.
> In addition, the API is very intuitive and easy to understand (in comparision
> to other NN frameworks like Encog and JOONE).
> h3. The approach to parallelization in Hadoop:
> In the Driver class:
> - The input parameters are read and the NeuralNetwork with a specified
> LearningRule (training algorithm) created.
> - Initial weight values are randomly generated and written to the FileSystem.
> If number of training sessions (for simulated annealing) is specified,
> multiple sets of initial weight values are generated.
> - Training is started by calling the NeuralNetwork's learn() method. For each
> iteration, every time the error gradient vector needs to be calculated, the
> method submits a Job where the input path to the training-set vectors and
> various key properties (like path to the stored weight values) are set. The
> gradient vectors calculated by the Reducers are written back to an output
> path in the FileSystem.
> - After the JobClient.runJob() returns, the gradient vectors are retrieved
> from the FileSystem and tested to see if the stopping criterion is satisfied.
> The weights are then updated, using the method implemented by the particular
> LearningRule. For line searches, each error function evaluation is again done
> by submitting a job.
> - The NN is trained in iterations until it converges.
> In the Mapper class:
> - Each Mapper is initialized using the configure method, the weights are
> retrieved and the complete NeuralNetwork created.
> - The map function then takes in the training vectors as key/value pairs (the
> key is ignored), runs them through the NN to calculate the outputs and
> backpropagates the errors to find out the error gradients. The error gradient
> vectors are then output as key/value pairs where all the keys are set to a
> common value, such as the training session number (for each training session,
> all keys in the outputs of all the mappers have to be identical).
> In the Combiner class:
> - Iterates through the all individual error gradient vectors output by the
> mapper (since they all have the same key) and adds them up to get a partial
> batch gradient.
> In the Reducer class:
> - There's a single reducer class that will combine all the partial gradients
> from the Mappers to get the overall batch gradient.
> - The final error gradient vector is written back to the FileSystem
> h3. I propose to complete all of the following sub-tasks during GSoC 2010:
> Implementation of the Backpropagation algorithm:
> - Initialization of weights: using the Nguyen-Widrow algorithm to select the
> initial range of starting weight values.
> - Input, transfer and error functions: implement basic input functions like
> WeightedSum and transfer functions like Sigmoid, Gaussian, tanh and linear.
> Implement the sum-of-squares error function.
> - Optimization methods to update the weights: (a) Batch Gradient descent,
> with momentum and a variable learning rate method [2] (b) A Conjugate
> gradient method with Brent's line search.
> Improving generalization:
> - Validating the network to test for overfitting (Early stopping method)
> - Regularization (weight decay method)
> Create examples for:
> - Classification: using the Abalone Data Set from UCI Machine Learning
> Repository
> - Classification, Regression: Breast Cancer Wisconsin (Prognostic) Data Set
> If time permits, also implement:
> - Resilient Backpropagation (RPROP)
> h2. III. Week Plan with list of deliverables
> * (Till May 23rd, community bonding period)
> Brainstorm with my mentor and the Apache Mahout community to come up with the
> most optimal design for an extensible Neural Network framework. Code
> prototypes to identify potential problems and/or investigate new solutions.
> Deliverable: A detailed report or design document on how to implement the
> basic Neural Network framework and the learning algorithms.
> * (May 24th, coding starts) Week 1:
> Deliverables: Basic Neural network classes (Neuron, Connection, Weight,
> Layer, LearningRule, NeuralNetwork etc) and the various input, transfer and
> error functions mentioned previously.
> * (May 31st) Week 2 and Week 3:
> Deliverable: Driver, Mapper, Combiner and Reducer classes with basic
> functonality to run a feedforward Neural Network on Hadoop (no training
> methods yet, weights are generated using Nguyen-Widrow algorithm).
> * (June 14th) Week 4:
> Deliverable: Backpropagation algorithm using standard Batch Gradient descent.
>
> * (June 21st) Week 5:
> Deliverables: Variable learning rate and momentum during Batch Gradient
> descent. Validation tests support. Do some big tests.
> * (June 28th) Week 6:
> Deliverable: Support for Early stopping and Regularization (weight decay)
> during training.
> * (July 5th) Week 7 and Week 8:
> Deliverable: Conjugate gradient method with Brent's line search algorithm.
> * (July 19th) Week 9:
> Deliverable: Write unit tests. Do bigger scale tests for both batch gradient
> descent and conjugate gradient method.
> * (July 26th) Week 10 and Week 11:
> Deliverable: 2 examples of classification and regression on real-world
> datasets from UCI Machine Learning Repository. More tests.
> * (August 9th, tentative 'pencils down' date) Week 12:
> Deliverable: Wind up the work. Scrub code. Improved documentation, tutorials
> (on the wiki) etc.
> * (August 16: Final evaluation)
> h2. IV. Additional Information
> I am a final year Computer Science student at NIT Allahabad (India)
> graduating in May. For my final year project/thesis, I am working on Open
> Domain Question Answering. I participated in GSoC last year for the Apertium
> machine translation system
> (http://google-opensource.blogspot.com/2009/11/apertium-projects-first-google-summer.html).
> I am familiar with the three major opensource Neural Network frameworks in
> Java, JOONE, Encog and Neuroph since I have used them in past projects on
> fingerprint recognition and face recognition (during a summer course on image
> and speech processing). My research interests are machine learning and
> statistical natural language processing and I will be enrolling for a Ph.D.
> next semester(i.e. next fall) in the same institute.
> I have no specific time constraints throughout the GSoC period. I will devote
> a minimum of 6 hours everyday to GSoC.
> Time offset: UTC+5:30 (IST)
> h2. V. References
> [1] Fast parallel off-line training of multilayer perceptrons, S McLoone, GW
> Irwin - IEEE Transactions on Neural Networks, 1997
> [2] Optimization of the backpropagation algorithm for training multilayer
> perceptrons, W. Schiffmann, M. Joost and R. Werner, 1994
> [3] Map-Reduce for Machine Learning on Multicore, Cheng T. Chu, Sang K. Kim,
> Yi A. Lin, et al - in NIPS, 2006
> [4] Neural networks for pattern recognition, CM Bishop - 1995 [BOOK]
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