chrishkchris edited a comment on issue #535: SINGA-490 Optimize performance of stochastic gradient descent (SGD) URL: https://github.com/apache/incubator-singa/pull/535#issuecomment-532723250 Next, I further improve the time performance by using in-place elementwise multiply in many autograd.py functions such as ReLU. Here is the result: ``` ubuntu@ip-172-31-39-137:~/incubator-singa/examples/autograd$ python3 mnist_cnn.py Starting Epoch 0: Training loss = 585.555542, training accuracy = 0.793390 Evaluation accuracy = 0.939303, Elapsed Time = 4.206943s Starting Epoch 1: Training loss = 234.893921, training accuracy = 0.922409 Evaluation accuracy = 0.955729, Elapsed Time = 4.101450s Starting Epoch 2: Training loss = 169.515244, training accuracy = 0.943286 Evaluation accuracy = 0.970252, Elapsed Time = 4.104907s Starting Epoch 3: Training loss = 136.331894, training accuracy = 0.954442 Evaluation accuracy = 0.968450, Elapsed Time = 4.115959s Starting Epoch 4: Training loss = 118.268318, training accuracy = 0.960512 Evaluation accuracy = 0.971755, Elapsed Time = 4.117009s Starting Epoch 5: Training loss = 104.006439, training accuracy = 0.965732 Evaluation accuracy = 0.978866, Elapsed Time = 4.117350s Starting Epoch 6: Training loss = 93.860809, training accuracy = 0.969067 Evaluation accuracy = 0.977464, Elapsed Time = 4.106471s Starting Epoch 7: Training loss = 88.009178, training accuracy = 0.970251 Evaluation accuracy = 0.982873, Elapsed Time = 4.116037s Starting Epoch 8: Training loss = 81.978348, training accuracy = 0.972802 Evaluation accuracy = 0.983974, Elapsed Time = 4.121274s Starting Epoch 9: Training loss = 75.998878, training accuracy = 0.974103 Evaluation accuracy = 0.982272, Elapsed Time = 4.122591s ```
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