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CUDA/OpenCL Sample OCR ProjectThis sample project uses a Optical Character Recognition (OCR) dataset along with a single hidden-layer Mutli-Layer Perceptron (MLP) neural network using the Levenberg-Marquardt (LM) learning algorithm. The Levenberg-Marquardt is the default learning algorithm in NeuroSolutions and is the most powerful form of back-propagation learning available.
Data Set InformationThe Letter Recognition Data Set1 features 20,000 total samples, 16 attributes and one desiredOutput (26 letters from A to Z). The sample project is trained on 17,000 exemplars/samples (3,000 set aside for Cross Validation) for 100 epochs in NeuroSolutions using the CUDA/OpenCL Pro add-on.
Download this project and try for yourself - LetterRecognition.zip
How to Run in NeuroSolutionsAfter downloading the data and extracting it to a writable directory on your computer you can evaluate the performance in NeuroSolutions with both CUDA/OpenCL enabled and disabled through the following steps:
Choosing a Graphics CardOur products support the two major brands of graphics cards: AMD™ & NVIDIA™.
Cost is likely to be the largest contributing factor in deciding on a graphics card. Prices can range from $100 to $3,500 depending on the brand and product line. AMD Radeon™ graphic cards provide the best cost-to-performance value starting at $100 with high end cards competing with NVIDIA Tesla™ cards in terms of performance. NVIDIA GeForce™ graphic cards are often more expensive than AMD Radeon cards and are hindered by NVIDIA throttling (600 Series and later) the double precision performance to help boost the appeal of their Tesla product line. NVIDIA Tesla™ is designed specifically for parallel computing providing extremely fast double precision computational times and boast a price tag starting at roughly $1,000.
Performance is likely to be the next biggest factor in deciding on a graphics card and this is where it can be a bit tricky. Ultimately your data set size and neural network size/structure will dictate how much performance you will gain from a graphics card. A small data set and/or neural network may not require a high end card to provide optimal performance. However, a large data set and/or neural network will benefit from a high end graphics card and may even require more than one graphics card which is currently only supported through NVIDIA CUDA™ graphic cards. A good baseline is the letter recognition data set which through our internal testing has shown that will utilize 95-100% of the GPUs processing on high end AMD Radeon, NVIDIA GeForce and Tesla cards. If your data set is larger than the letter recognition then you will likely want a high end AMD Radeon, NVIDIA GeForce or NVIDIA Tesla card(s).
On the Graphic Cards page, we breakdown the theoratical best performing cards for each price range to help in making an optimal decision. If you are still unsure how to proceed with purchasing a graphics card, please feel free to contact technical support for assistance.
1. Data set courtesy of UCI Machine Learning Repository
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