ZHEQIUSHUI opened a new issue #19430: URL: https://github.com/apache/incubator-mxnet/issues/19430
## Description (A clear and concise description of what the bug is.) ``` [14:35:41] F:\BaiduNetdiskDownload\incubator-mxnet\src\io\iter_mnist.cc:110: MNISTIter: load 60000 images, shuffle=1, shape=(16,784) [14:35:41] F:\BaiduNetdiskDownload\incubator-mxnet\src\io\iter_mnist.cc:110: MNISTIter: load 10000 images, shuffle=1, shape=(16,784) [14:35:41] F:\BaiduNetdiskDownload\incubator-mxnet\src\executor\graph_executor.cc:2061: Subgraph backend MKLDNN is activated. [14:35:41] F:\Code\mxnet_classify_train\train.cpp:126: Epoch: 0 [14:35:42] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 1 Train Accuracy: 0.125 Train Loss: 2.30259 [14:35:42] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 2 Train Accuracy: 0.1875 Train Loss: 2.30146 [14:35:42] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 3 Train Accuracy: 0.1875 Train Loss: 2.30162 [14:35:42] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 4 Train Accuracy: 0.171875 Train Loss: 2.30361 [14:35:43] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 5 Train Accuracy: 0.15 Train Loss: 2.30592 [14:35:43] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 6 Train Accuracy: 0.135417 Train Loss: 2.30718 [14:35:43] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 7 Train Accuracy: 0.142857 Train Loss: 2.30081 [14:35:43] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 8 Train Accuracy: 0.132813 Train Loss: 2.30486 [14:35:43] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 9 Train Accuracy: 0.145833 Train Loss: 2.3007 [14:35:44] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 10 Train Accuracy: 0.13125 Train Loss: 2.31614 [14:35:44] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 11 Train Accuracy: 0.119318 Train Loss: 2.3079 [14:35:44] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 12 Train Accuracy: 0.114583 Train Loss: 2.30001 [14:35:44] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 13 Train Accuracy: 0.105769 Train Loss: 2.30863 [14:35:44] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 14 Train Accuracy: 0.102679 Train Loss: 2.3078 [14:35:45] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 15 Train Accuracy: 0.1125 Train Loss: 2.29704 [14:35:45] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 16 Train Accuracy: 0.109375 Train Loss: 2.30283 [14:35:45] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 17 Train Accuracy: 0.117647 Train Loss: 2.2879 [14:35:45] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 18 Train Accuracy: 0.111111 Train Loss: 2.30765 [14:35:46] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 19 Train Accuracy: 0.108553 Train Loss: 2.29667 [14:35:46] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 20 Train Accuracy: 0.10625 Train Loss: 2.30143 [14:35:46] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 21 Train Accuracy: 0.104167 Train Loss: 2.31742 [14:35:46] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 22 Train Accuracy: 0.107955 Train Loss: 2.3004 [14:35:46] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 23 Train Accuracy: 0.105978 Train Loss: 2.30668 [14:35:47] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 24 Train Accuracy: 0.104167 Train Loss: 2.3081 [14:35:47] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 25 Train Accuracy: 0.105 Train Loss: 2.31609 [14:35:47] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 26 Train Accuracy: 0.103365 Train Loss: 2.2924 [14:35:47] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 27 Train Accuracy: 0.101852 Train Loss: 2.29345 [14:35:47] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 28 Train Accuracy: 0.100446 Train Loss: 2.32269 [14:35:48] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 29 Train Accuracy: 0.0969828 Train Loss: 2.30808 [14:35:48] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 30 Train Accuracy: 0.0958333 Train Loss: 2.29545 [14:35:48] F:\Code\mxnet_classify_train\train.cpp:148: EPOCH: 0 ITER: 31 Train Accuracy: 0.0947581 Train Loss: 2.32122 ``` ## What have you tried to solve it? 1.change lr 2.add LRScheduler ## Environment windows x64 cpp mxnet-v1.7.x compile with mkl (no cuda) ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
