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README.md

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@@ -15,7 +15,7 @@ Pilot3 (P3) benchmarks are formed out of problems and data at the population lev
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Each of the problems (P1,P2,P3) informed the implementation of specific benchmarks, so P1B3 would be benchmark three of problem 1.
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At this point, we will refer to a benchmark by it's problem area and benchmark number. So it's natural to talk of the P1B1 benchmark. Inside each benchmark directory, there exists a readme file that contains an overview of the benchmark, a description of the data and expected outcomes along with instructions for running the benchmark code.
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Over time, we will be adding implementations that make use of different tensor frameworks. The primary (baseline) benchmarks are implemented using tensorflow, and are named with '_baseline' in the name, for example p2b1_baseline.py.
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Over time, we will be adding implementations that make use of different tensor frameworks. The primary (baseline) benchmarks are implemented using keras, and are named with '_baseline' in the name, for example p3b1_baseline_keras2.py.
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Implementations that use alternative tensor frameworks, such as mxnet or neon, will have the name of the framework in the name. Examples can be seen in the P1B3 benchmark contribs/ directory, for example:
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p1b3_mxnet.py

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