yifeim opened a new issue #10045: Despite so many declarations, Module is still confused by extra names in DataIter URL: https://github.com/apache/incubator-mxnet/issues/10045 ## Description I provide data_names to module declaration, module binding, and DataIter. Still, my module will just use whichever data_name that pleases it. Please at least drop errors when there are extra names in DataIter. ## Environment info (Required) ----------Python Info---------- Version : 3.6.4 Compiler : GCC 7.2.0 Build : ('default', 'Jan 16 2018 18:10:19') Arch : ('64bit', '') ------------Pip Info----------- Version : 9.0.1 Directory : /home/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/pip ----------MXNet Info----------- Version : 1.0.0 Directory : /home/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet Commit Hash : 9ef196909ec7bf9cdda66d5b97c92793109798e1 ----------System Info---------- Platform : Linux-4.4.0-1052-aws-x86_64-with-debian-stretch-sid system : Linux node : ip-172-31-0-77 release : 4.4.0-1052-aws version : #61-Ubuntu SMP Mon Feb 12 23:05:58 UTC 2018 ----------Hardware Info---------- machine : x86_64 processor : x86_64 ----------Network Test---------- Setting timeout: 10 Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0024 sec, LOAD: 0.5823 sec. Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.1722 sec, LOAD: 0.0692 sec. Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.1271 sec, LOAD: 0.2519 sec. Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0490 sec, LOAD: 0.7062 sec. Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0177 sec, LOAD: 0.2762 sec. Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0095 sec, LOAD: 0.0591 sec. Package used (Python/R/Scala/Julia): Python ## Error Message: ## Minimum reproducible example ``` import mxnet as mx train_iter = mx.io.NDArrayIter({ 'i0' : mx.nd.zeros(5,), 'i1' : mx.nd.ones(5,), }, batch_size=5) net = mx.sym.var('i1') mod = mx.module.Module(net, ['i1'], None) mod.bind([('i1', (5,))], None) mod.init_params() mod.predict(train_iter) ## expected output: [1,1,1,1,1]; actual output [0,0,0,0,0] ```
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