nttstar opened a new issue #11019: Gluon SoftmaxCELoss does not converge, for large number of classes. URL: https://github.com/apache/incubator-mxnet/issues/11019 ## Description Training with 85K classes failed while I use gluon trainer with SoftmaxCELoss. But it is ok if I defined the same network by gluon but training with symbolic module interface(sym.SoftmaxOutput). ## Error Message: training acc starts from 0.0 to 0.001, but then drop to 0.0 again after about 1K iterations. ## Steps to reproduce 1. checkout latest insightface repo(https://github.com/deepinsight/insightface) 2. Download ms1m dataset from the repo and unzip to ./faces_ms1m 3. Run training script ``insightface/gluon/train.py`` and you can see the training acc changing at about 1.5K iterations. Validation process will start every 2K iterations, depends on --verbose param. The below command works fine: ``` CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train.py --data-dir ./faces_ms1m --network r18 --prefix ./model-r18-test --per-batch-size 128 --lr-steps '10000,20000,3000' --lr 0.1 --ckpt 0 --verbose 2000 --wd 0.0005 --margin-a 0.0 --eval lfw --mode symbol ``` The below command does not converge: ``` CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train.py --data-dir ./faces_ms1m --network r18 --prefix ./model-r18-test --per-batch-size 128 --lr-steps '10000,20000,3000' --lr 0.1 --ckpt 0 --verbose 2000 --wd 0.0005 --margin-a 0.0 --eval lfw --mode gluon ``` ## Environment info (Required) ----------Python Info---------- ('Version :', '2.7.5') ('Compiler :', 'GCC 4.8.5 20150623 (Red Hat 4.8.5-16)') ('Build :', ('default', 'Aug 4 2017 00:39:18')) ('Arch :', ('64bit', 'ELF')) ------------Pip Info----------- ('Version :', '9.0.2') ('Directory :', '/usr/lib/python2.7/site-packages/pip') ----------MXNet Info----------- ('Version :', '1.2.0') ('Directory :', '/usr/lib/python2.7/site-packages/mxnet') ('Commit Hash :', 'f0be910ae5e3fa01e0a9aaf98dbd4616c35be76b') ----------System Info---------- ('Platform :', 'Linux-3.10.0-327.el7.x86_64-x86_64-with-centos-7.4.1708-Core') ('system :', 'Linux') ('node :', 'cdsl-gpu-a04') ('release :', '3.10.0-327.el7.x86_64') ('version :', '#1 SMP Thu Nov 19 22:10:57 UTC 2015') ----------Hardware Info---------- ('machine :', 'x86_64') ('processor :', 'x86_64') Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian CPU(s): 48 On-line CPU(s) list: 0-47 Thread(s) per core: 2 Core(s) per socket: 12 Socket(s): 2 NUMA node(s): 2 Vendor ID: GenuineIntel CPU family: 6 Model: 79 Model name: Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz Stepping: 1 CPU MHz: 2199.914 CPU max MHz: 2900.0000 CPU min MHz: 1200.0000 BogoMIPS: 4400.12 Virtualization: VT-x L1d cache: 32K L1i cache: 32K L2 cache: 256K L3 cache: 30720K NUMA node0 CPU(s): 0-11,24-35 NUMA node1 CPU(s): 12-23,36-47 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc aperfmperf eagerfpu pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch ida arat epb pln pts dtherm tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdseed adx smap xsaveopt cqm_llc cqm_occup_llc ----------Network Test---------- Setting timeout: 10 Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0067 sec, LOAD: 2.4557 sec. Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0062 sec, LOAD: 1.8693 sec. Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.2143 sec, LOAD: 2.3253 sec. Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0067 sec, LOAD: 1.1611 sec. Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.5429 sec, LOAD: 2.8609 sec. Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.0070 sec, LOAD: 1.6273 sec.
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