chrishkchris opened a new pull request #579: hotfix: bugs in autograd.py and also update test case URL: https://github.com/apache/singa/pull/579 I have fixed the bugs in autograd.py and update test case mentioned in issue #576 The results are ok now: ``` ubuntu@ip-172-31-24-48:~/singa/test/python$ python3 test_operation.py ............................................................................................................................. ---------------------------------------------------------------------- Ran 125 tests in 0.624s OK ubuntu@ip-172-31-24-48:~/singa/test/python$ cd .. ubuntu@ip-172-31-24-48:~/singa/test$ cd .. ubuntu@ip-172-31-24-48:~/singa$ cd examples ubuntu@ip-172-31-24-48:~/singa/examples$ cd autograd ubuntu@ip-172-31-24-48:~/singa/examples/autograd$ python3 mlp.py train_data_shape: (400, 2) train_label_shape: (400, 2) training loss = 0.6905591 training loss = 0.5654273 training loss = 0.54077435 training loss = 0.5085985 training loss = 0.42543384 training loss = 0.31906518 training loss = 0.25143874 training loss = 0.20494391 training loss = 0.17236656 training loss = 0.14908642 training loss = 0.13166472 ubuntu@ip-172-31-24-48:~/singa/examples/autograd$ python3 download_mnist.py Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz ubuntu@ip-172-31-24-48:~/singa/examples/autograd$ python3 mnist_cnn.py Starting Epoch 0: Training loss = 582.361877, training accuracy = 0.795024 Evaluation accuracy = 0.945012, Elapsed Time = 2.526412s Starting Epoch 1: Training loss = 232.189026, training accuracy = 0.922142 Evaluation accuracy = 0.957131, Elapsed Time = 2.488854s Starting Epoch 2: Training loss = 163.937531, training accuracy = 0.945804 Evaluation accuracy = 0.973558, Elapsed Time = 2.490621s Starting Epoch 3: Training loss = 137.145462, training accuracy = 0.954592 Evaluation accuracy = 0.972456, Elapsed Time = 2.501071s Starting Epoch 4: Training loss = 116.372910, training accuracy = 0.961229 Evaluation accuracy = 0.972756, Elapsed Time = 2.501080s Starting Epoch 5: Training loss = 103.669510, training accuracy = 0.965331 Evaluation accuracy = 0.974960, Elapsed Time = 2.512660s Starting Epoch 6: Training loss = 95.173775, training accuracy = 0.967499 Evaluation accuracy = 0.973057, Elapsed Time = 2.500494s Starting Epoch 7: Training loss = 84.533409, training accuracy = 0.971551 Evaluation accuracy = 0.983073, Elapsed Time = 2.499969s Starting Epoch 8: Training loss = 80.991859, training accuracy = 0.972936 Evaluation accuracy = 0.979768, Elapsed Time = 2.500226s Starting Epoch 9: Training loss = 74.402122, training accuracy = 0.974536 Evaluation accuracy = 0.984675, Elapsed Time = 2.502320s ubuntu@ip-172-31-24-48:~/singa/examples/autograd$ python3 resnet.py Start intialization............ 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:29<00:00, 3.45it/s] Throughput = 110.192340941104 per second Total=0.29040130853652957, forward=0.09273585081100463, softmax=0.0013292169570922852, backward=0.19633624076843265, sgd=0.009238913059234619 ```
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