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Subject [GitHub] [singa] chrishkchris commented on issue #588: [WIP] SINGA-505 SoftMaxBackward using CUDNN
Date Fri, 31 Jan 2020 09:32:03 GMT
chrishkchris commented on issue #588: [WIP] SINGA-505 SoftMaxBackward using CUDNN
URL: https://github.com/apache/singa/pull/588#issuecomment-580657211
 
 
   I have tried the CUDNN SoftMax backward by modifying the mnist example:
   
   ```
   ubuntu@ip-172-31-24-48:~/singa/examples/autograd$ python3 mnist_cnn.py
   Starting Epoch 0:
   Training loss = 580.753418, training accuracy = 0.797208
   Evaluation accuracy = 0.939002, Elapsed Time = 2.541010s
   Starting Epoch 1:
   Training loss = 229.544083, training accuracy = 0.924260
   Evaluation accuracy = 0.958133, Elapsed Time = 2.517823s
   Starting Epoch 2:
   Training loss = 165.276779, training accuracy = 0.945454
   Evaluation accuracy = 0.974559, Elapsed Time = 2.523124s
   Starting Epoch 3:
   Training loss = 134.344086, training accuracy = 0.955593
   Evaluation accuracy = 0.974159, Elapsed Time = 2.523674s
   Starting Epoch 4:
   Training loss = 115.716629, training accuracy = 0.961730
   Evaluation accuracy = 0.979367, Elapsed Time = 2.522673s
   Starting Epoch 5:
   Training loss = 104.472374, training accuracy = 0.964831
   Evaluation accuracy = 0.978265, Elapsed Time = 2.535722s
   Starting Epoch 6:
   Training loss = 95.322929, training accuracy = 0.968283
   Evaluation accuracy = 0.984575, Elapsed Time = 2.525273s
   Starting Epoch 7:
   Training loss = 88.591621, training accuracy = 0.970184
   Evaluation accuracy = 0.981571, Elapsed Time = 2.526018s
   Starting Epoch 8:
   Training loss = 83.001053, training accuracy = 0.971685
   Evaluation accuracy = 0.981571, Elapsed Time = 2.525087s
   Starting Epoch 9:
   Training loss = 76.832161, training accuracy = 0.974653
   Evaluation accuracy = 0.979267, Elapsed Time = 2.527286s
   
   ```

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