[GitHub] szha commented on issue #8010: Revert "Many loss functions (#7605)"

[git]

szha commented on issue #8010: Revert "Many loss functions  (#7605)"
URL: https://github.com/apache/incubator-mxnet/pull/8010#issuecomment-331689320
 
 
   Like I commented in the third comment, the naming has been fixed in #8007 
 

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[GitHub] thatindiandude commented on issue #8010: Revert "Many loss functions (#7605)"

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thatindiandude commented on issue #8010: Revert "Many loss functions  (#7605)"
URL: https://github.com/apache/incubator-mxnet/pull/8010#issuecomment-331689293
 
 
   I think rudimentary tests aren't much too ask for. This would expose such
   bugs that currently aren't known.
   
   The idea that you can push untested code because a known bug doesn't exist
   contradicts test-driven development principles.
   
   On Sep 23, 2017 10:51 PM, "Sheng Zha"  wrote:
   
   > Only when there's known bug in the code, at which time we should roll back
   > the part with bugs.
   >
   > ?
   > You are receiving this because you are subscribed to this thread.
   > Reply to this email directly, view it on GitHub
   > 
,
   > or mute the thread
   > 

   > .
   >
   
 

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[GitHub] piiswrong commented on issue #8010: Revert "Many loss functions (#7605)"

[git]

piiswrong commented on issue #8010: Revert "Many loss functions  (#7605)"
URL: https://github.com/apache/incubator-mxnet/pull/8010#issuecomment-331689206
 
 
   For one thing, the names are inconsistent.
 

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[GitHub] szha commented on issue #8010: Revert "Many loss functions (#7605)"

[git]

szha commented on issue #8010: Revert "Many loss functions  (#7605)"
URL: https://github.com/apache/incubator-mxnet/pull/8010#issuecomment-331689054
 
 
   Only when there's known bug in the code, at which time we should roll back 
the part with bugs.
 

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[GitHub] piiswrong commented on issue #8010: Revert "Many loss functions (#7605)"

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piiswrong commented on issue #8010: Revert "Many loss functions  (#7605)"
URL: https://github.com/apache/incubator-mxnet/pull/8010#issuecomment-331688804
 
 
   Roll back is always better than fix forward. This is directly user facing 
top level API. It needs to be tested.
 

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[GitHub] szha commented on issue #8010: Revert "Many loss functions (#7605)"

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szha commented on issue #8010: Revert "Many loss functions  (#7605)"
URL: https://github.com/apache/incubator-mxnet/pull/8010#issuecomment-331688529
 
 
   Has there been known bugs or issues? If not, I'd suggest that a better path 
is forward, by applying the appropriate fixes. #7605 was opened a month ago and 
there has been plenty of time for you and other reviewers to look at and 
comment on that PR.
 

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[GitHub] piiswrong commented on issue #8010: Revert "Many loss functions (#7605)"

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piiswrong commented on issue #8010: Revert "Many loss functions  (#7605)"
URL: https://github.com/apache/incubator-mxnet/pull/8010#issuecomment-331688229
 
 
   This is a large amount of untested user facing code. That's enough ground 
for a veto.
 

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[GitHub] szha commented on issue #8010: Revert "Many loss functions (#7605)"

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szha commented on issue #8010: Revert "Many loss functions  (#7605)"
URL: https://github.com/apache/incubator-mxnet/pull/8010#issuecomment-331687530
 
 
   For the record, I've been helping @smolix cleaning up the naming in #8007, 
cleaning doc in #7914.
 

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[GitHub] szha commented on issue #8010: Revert "Many loss functions (#7605)"

[git]

szha commented on issue #8010: Revert "Many loss functions  (#7605)"
URL: https://github.com/apache/incubator-mxnet/pull/8010#issuecomment-331687376
 
 
   Shouldn't you at least talk to the contributor before reverting this?
 

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[GitHub] piiswrong opened a new pull request #8010: Revert "Many loss functions (#7605)"

[git]

piiswrong opened a new pull request #8010: Revert "Many loss functions  (#7605)"
URL: https://github.com/apache/incubator-mxnet/pull/8010
 
 
   This reverts commit 9d56db66e2e94a8a3d9bf020b9682e91e7baf203.
   
   revert before names/comments/tests are fixed
 

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[GitHub] szha commented on issue #8007: add Loss suffix to losses

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szha commented on issue #8007: add Loss suffix to losses
URL: https://github.com/apache/incubator-mxnet/pull/8007#issuecomment-331687065
 
 
   @mli @smolix 
 

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[GitHub] piiswrong commented on issue #8007: add Loss suffix to losses

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piiswrong commented on issue #8007: add Loss suffix to losses
URL: https://github.com/apache/incubator-mxnet/pull/8007#issuecomment-331687030
 
 
   WTF
   When was the loss PR merged? There is absolutely no test for the code! The 
doc also needs improvement.
   We need to either add tests and improve the doc immediately or revert the 
merge
 

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[GitHub] piiswrong closed pull request #8008: fix elemwise_sum test script

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piiswrong closed pull request #8008: fix elemwise_sum test script
URL: https://github.com/apache/incubator-mxnet/pull/8008
 
 
   
 

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[incubator-mxnet] branch master updated: Fixed broken URL by renaming FAQ folder name to faq (#8009)

[jxie]

This is an automated email from the ASF dual-hosted git repository.

jxie pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git


The following commit(s) were added to refs/heads/master by this push:
 new 6ff309b  Fixed broken URL by renaming FAQ folder name to faq (#8009)
6ff309b is described below

commit 6ff309bd21c5709fb7ae52123abe9de76b73a71b
Author: thinksanky <31976455+thinksa...@users.noreply.github.com>
AuthorDate: Sat Sep 23 21:45:08 2017 -0700

Fixed broken URL by renaming FAQ folder name to faq (#8009)
---
 docs/{FAQ => faq}/bucketing.md   | 0
 docs/{FAQ => faq}/caffe.md   | 0
 docs/{FAQ => faq}/cloud.md   | 0
 docs/{FAQ => faq}/develop_and_hack.md| 0
 docs/{FAQ => faq}/env_var.md | 0
 docs/{FAQ => faq}/faq.md | 0
 docs/{FAQ => faq}/finetune.md| 0
 docs/{FAQ => faq}/index.md   | 0
 docs/{FAQ => faq}/model_parallel_lstm.md | 0
 docs/{FAQ => faq}/multi_devices.md   | 0
 docs/{FAQ => faq}/new_op.md  | 0
 docs/{FAQ => faq}/nnpack.md  | 0
 docs/{FAQ => faq}/perf.md| 0
 docs/{FAQ => faq}/recordio.md| 0
 docs/{FAQ => faq}/s3_integration.md  | 0
 docs/{FAQ => faq}/smart_device.md| 0
 docs/{FAQ => faq}/torch.md   | 0
 docs/{FAQ => faq}/visualize_graph.md | 0
 18 files changed, 0 insertions(+), 0 deletions(-)

diff --git a/docs/FAQ/bucketing.md b/docs/faq/bucketing.md
similarity index 100%
rename from docs/FAQ/bucketing.md
rename to docs/faq/bucketing.md
diff --git a/docs/FAQ/caffe.md b/docs/faq/caffe.md
similarity index 100%
rename from docs/FAQ/caffe.md
rename to docs/faq/caffe.md
diff --git a/docs/FAQ/cloud.md b/docs/faq/cloud.md
similarity index 100%
rename from docs/FAQ/cloud.md
rename to docs/faq/cloud.md
diff --git a/docs/FAQ/develop_and_hack.md b/docs/faq/develop_and_hack.md
similarity index 100%
rename from docs/FAQ/develop_and_hack.md
rename to docs/faq/develop_and_hack.md
diff --git a/docs/FAQ/env_var.md b/docs/faq/env_var.md
similarity index 100%
rename from docs/FAQ/env_var.md
rename to docs/faq/env_var.md
diff --git a/docs/FAQ/faq.md b/docs/faq/faq.md
similarity index 100%
rename from docs/FAQ/faq.md
rename to docs/faq/faq.md
diff --git a/docs/FAQ/finetune.md b/docs/faq/finetune.md
similarity index 100%
rename from docs/FAQ/finetune.md
rename to docs/faq/finetune.md
diff --git a/docs/FAQ/index.md b/docs/faq/index.md
similarity index 100%
rename from docs/FAQ/index.md
rename to docs/faq/index.md
diff --git a/docs/FAQ/model_parallel_lstm.md b/docs/faq/model_parallel_lstm.md
similarity index 100%
rename from docs/FAQ/model_parallel_lstm.md
rename to docs/faq/model_parallel_lstm.md
diff --git a/docs/FAQ/multi_devices.md b/docs/faq/multi_devices.md
similarity index 100%
rename from docs/FAQ/multi_devices.md
rename to docs/faq/multi_devices.md
diff --git a/docs/FAQ/new_op.md b/docs/faq/new_op.md
similarity index 100%
rename from docs/FAQ/new_op.md
rename to docs/faq/new_op.md
diff --git a/docs/FAQ/nnpack.md b/docs/faq/nnpack.md
similarity index 100%
rename from docs/FAQ/nnpack.md
rename to docs/faq/nnpack.md
diff --git a/docs/FAQ/perf.md b/docs/faq/perf.md
similarity index 100%
rename from docs/FAQ/perf.md
rename to docs/faq/perf.md
diff --git a/docs/FAQ/recordio.md b/docs/faq/recordio.md
similarity index 100%
rename from docs/FAQ/recordio.md
rename to docs/faq/recordio.md
diff --git a/docs/FAQ/s3_integration.md b/docs/faq/s3_integration.md
similarity index 100%
rename from docs/FAQ/s3_integration.md
rename to docs/faq/s3_integration.md
diff --git a/docs/FAQ/smart_device.md b/docs/faq/smart_device.md
similarity index 100%
rename from docs/FAQ/smart_device.md
rename to docs/faq/smart_device.md
diff --git a/docs/FAQ/torch.md b/docs/faq/torch.md
similarity index 100%
rename from docs/FAQ/torch.md
rename to docs/faq/torch.md
diff --git a/docs/FAQ/visualize_graph.md b/docs/faq/visualize_graph.md
similarity index 100%
rename from docs/FAQ/visualize_graph.md
rename to docs/faq/visualize_graph.md

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[GitHub] piiswrong closed pull request #8009: Fix faq url branch

[git]

piiswrong closed pull request #8009: Fix faq url branch
URL: https://github.com/apache/incubator-mxnet/pull/8009
 
 
   
 

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[GitHub] qingzhouzhen commented on issue #7957: add densenet

[git]

qingzhouzhen commented on issue #7957: add densenet
URL: https://github.com/apache/incubator-mxnet/pull/7957#issuecomment-331686274
 
 
   The training of 169-layers is done, result as below:
   INFO:root:Epoch[124] Batch [2450]   Speed: 107.01 samples/sec   
accuracy=0.904687   cross-entropy=0.365293  top_k_accuracy_5=0.984219
   INFO:root:Epoch[124] Batch [2300]   Speed: 106.73 samples/sec   
accuracy=0.902656   cross-entropy=0.367414  top_k_accuracy_5=0.985313
   INFO:root:Epoch[124] Batch [2100]   Speed: 106.68 samples/sec   
accuracy=0.897656   cross-entropy=0.385241  top_k_accuracy_5=0.983594
   INFO:root:Epoch[124] Batch [2500]   Speed: 106.51 samples/sec   
accuracy=0.903438   cross-entropy=0.372258  top_k_accuracy_5=0.984219
   INFO:root:Epoch[124] Train-accuracy=0.906250
   INFO:root:Epoch[124] Train-cross-entropy=0.400677
   INFO:root:Epoch[124] Train-top_k_accuracy_5=0.980469
   INFO:root:Epoch[124] Time cost=3106.063
   INFO:root:Saved checkpoint to "densenet-models/densenet-0125.params"
   INFO:root:Epoch[124] Validation-accuracy=0.741744
   INFO:root:Epoch[124] Validation-cross-entropy=1.160414
   INFO:root:Epoch[124] Validation-top_k_accuracy_5=0.911830
   I think the model is consistent with the gluon implementation, with little 
different such as BatchNorm layer, but it seems did not affect the result.
 

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[GitHub] qingzhouzhen commented on issue #7957: add densenet

[git]

qingzhouzhen commented on issue #7957: add densenet
URL: https://github.com/apache/incubator-mxnet/pull/7957#issuecomment-331686274
 
 
   The training of 169-layers is done, result as below:
   INFO:root:Epoch[124] Batch [2450]   Speed: 107.01 samples/sec   
accuracy=0.904687   cross-entropy=0.365293  top_k_accuracy_5=0.984219
   INFO:root:Epoch[124] Batch [2300]   Speed: 106.73 samples/sec   
accuracy=0.902656   cross-entropy=0.367414  top_k_accuracy_5=0.985313
   INFO:root:Epoch[124] Batch [2100]   Speed: 106.68 samples/sec   
accuracy=0.897656   cross-entropy=0.385241  top_k_accuracy_5=0.983594
   INFO:root:Epoch[124] Batch [2500]   Speed: 106.51 samples/sec   
accuracy=0.903438   cross-entropy=0.372258  top_k_accuracy_5=0.984219
   INFO:root:Epoch[124] Train-accuracy=0.906250
   INFO:root:Epoch[124] Train-cross-entropy=0.400677
   INFO:root:Epoch[124] Train-top_k_accuracy_5=0.980469
   INFO:root:Epoch[124] Time cost=3106.063
   INFO:root:Saved checkpoint to "densenet-models/densenet-0125.params"
   INFO:root:Epoch[124] Validation-accuracy=0.741744
   INFO:root:Epoch[124] Validation-cross-entropy=1.160414
   INFO:root:Epoch[124] Validation-top_k_accuracy_5=0.911830
   I think the model is consistent with the gluon implementation, with little 
different such as BatchNorm layer, but it seems did not affect the result. 
@piiswrong 
 

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[GitHub] qingzhouzhen commented on issue #7957: add densenet

[git]

qingzhouzhen commented on issue #7957: add densenet
URL: https://github.com/apache/incubator-mxnet/pull/7957#issuecomment-331020772
 
 
   Ok,I will learn to use gluon, 
   The training of densenet is really slow, the validation of top-1 is above 
71% now(169 layers)
   INFO:root:Epoch[42] Train-accuracy=0.796875
   INFO:root:Epoch[42] Train-cross-entropy=0.746801
   INFO:root:Epoch[42] Train-top_k_accuracy_5=0.955078
   INFO:root:Epoch[42] Time cost=3112.315
   INFO:root:Epoch[42] Validation-accuracy=0.710615
   INFO:root:Epoch[42] Validation-cross-entropy=1.197285
   INFO:root:Epoch[42] Validation-top_k_accuracy_5=0.902948
 

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[GitHub] jiarenyf commented on issue #7989: Update metric without considering the dataBatch.pad ?

[git]

jiarenyf commented on issue #7989: Update metric without considering the 
dataBatch.pad ?
URL: 
https://github.com/apache/incubator-mxnet/issues/7989#issuecomment-331680594
 
 
   ??
 

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[GitHub] jiarenyf commented on issue #7989: Update metric without considering the dataBatch.pad ?

[git]

jiarenyf commented on issue #7989: Update metric without considering the 
dataBatch.pad ?
URL: 
https://github.com/apache/incubator-mxnet/issues/7989#issuecomment-331638212
 
 
   ??
 

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[GitHub] jiarenyf commented on issue #7989: Update metric without considering the dataBatch.pad ?

[git]

jiarenyf commented on issue #7989: Update metric without considering the 
dataBatch.pad ?
URL: 
https://github.com/apache/incubator-mxnet/issues/7989#issuecomment-331602552
 
 
   ??
 

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[GitHub] jiarenyf commented on issue #7989: Update metric without considering the dataBatch.pad ?

[git]

jiarenyf commented on issue #7989: Update metric without considering the 
dataBatch.pad ?
URL: 
https://github.com/apache/incubator-mxnet/issues/7989#issuecomment-331680594
 
 
   ??
 

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[GitHub] thinksanky opened a new pull request #8009: Fix faq url branch

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thinksanky opened a new pull request #8009: Fix faq url branch
URL: https://github.com/apache/incubator-mxnet/pull/8009
 
 
   Fixed the broken URLs for FAQ.
 

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[GitHub] ptrendx commented on issue #7996: Question about Float16

[git]

ptrendx commented on issue #7996: Question about Float16
URL: 
https://github.com/apache/incubator-mxnet/issues/7996#issuecomment-331678105
 
 
   If you choose fp16 dtype then training values storage is fp16, compute 
accuracy is fp32/TensorCore on Volta. By default, there is no fp32 master 
weight, but for SGD optimizer you can set multi_precision=True which will 
result in fp32 master weight. I have a PR #7654 that will make mixed precision 
work with all optimizer (although slower than a dedicated path like in sgd). If 
you use train_imagenet.py script with fp16 then it sets multi_precision option 
to True.
   
   Before there was MSHADOW_USE_PASCAL compile flag that would enable fp16 
compute (what caffe calls true fp16 training) but we decided that (especially 
with Volta) it is not worth the confusion it makes.
 

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[GitHub] janelu9 commented on issue #7999: Is ndarray api designed for users construct networks to predict, and symbol for training?

[git]

janelu9 commented on issue #7999: Is ndarray api designed for users  construct 
networks to predict, and symbol for training?
URL: 
https://github.com/apache/incubator-mxnet/issues/7999#issuecomment-331677540
 
 
   @szha I want to train a word2vec model such as n-gamma , but i don't find 
any example ?I am familiar to use the symbol Api?but the first n-1 words of the 
sentence should be initiated as zeros I think?need I define it as a ndarray 
firstly in the BucketIter and use it as a symbol when training
 

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[GitHub] szha commented on issue #7999: Is ndarray api designed for users construct networks to predict, and symbol for training?

[git]

szha commented on issue #7999: Is ndarray api designed for users  construct 
networks to predict, and symbol for training?
URL: 
https://github.com/apache/incubator-mxnet/issues/7999#issuecomment-331676651
 
 
   Symbolic way is usually faster. Most of the examples were written in 
symbolic way.
   For NDArray, you can use autograd to train in imperative way. 
https://mxnet.incubator.apache.org/versions/master/api/python/autograd.html?highlight=autograd#record-vs-pause
   Yet another alternative is to use the new interface Gluon, which is to use 
imperative way for development and debugging and to use symbolic way for 
large-scale training. There are many step-by-step tutorials on this in 
http://gluon.mxnet.io/ and https://zh.gluon.ai/.
 

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[GitHub] szha commented on issue #7999: Is ndarray api designed for users construct networks to predict, and symbol for training?

[git]

szha commented on issue #7999: Is ndarray api designed for users  construct 
networks to predict, and symbol for training?
URL: 
https://github.com/apache/incubator-mxnet/issues/7999#issuecomment-331676651
 
 
   Symbolic way is usually faster. Most of the examples were written in 
symbolic way.
   For NDArray, you can use autograd to train in imperative way. 
https://mxnet.incubator.apache.org/versions/master/api/python/autograd.html?highlight=autograd#record-vs-pause
   Yet another alternative is to use the new interface Gluon, to use imperative 
way for development and debugging and to use symbolic way for large-scale 
training. There are many step-by-step tutorials on this in 
http://gluon.mxnet.io/ and https://zh.gluon.ai/.
 

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[GitHub] janelu9 commented on issue #7999: Is ndarray api designed for users construct networks to predict, and symbol for training?

[git]

janelu9 commented on issue #7999: Is ndarray api designed for users  construct 
networks to predict, and symbol for training?
URL: 
https://github.com/apache/incubator-mxnet/issues/7999#issuecomment-331676412
 
 
   @szha which one is faster , how to train a ndarray? can you give me a 
example please?
 

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[GitHub] eric-haibin-lin opened a new pull request #8008: fix elemwise_sum test script

[git]

eric-haibin-lin opened a new pull request #8008: fix elemwise_sum test script
URL: https://github.com/apache/incubator-mxnet/pull/8008
 
 
   @sxjscience  I was going to fix it in #7947, but since that PR is not ready 
I am making this separate PR to fix it. I have no idea why the CI didn't catch 
it. 
   The root cause of the problem is that #7577 changed _identity_attr_like_rhs 
op implementation and didn't check storage type inside the operator. These 
problems will be fixed in #7947 soon.
   @piiswrong 
 

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[GitHub] szha opened a new pull request #8007: add Loss suffix to losses

[git]

szha opened a new pull request #8007: add Loss suffix to losses
URL: https://github.com/apache/incubator-mxnet/pull/8007
 
 
   
 

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[GitHub] SumNeuron commented on issue #7943: Request: Visualization for Gluon neural networks

[git]

SumNeuron commented on issue #7943: Request: Visualization for Gluon neural 
networks
URL: 
https://github.com/apache/incubator-mxnet/issues/7943#issuecomment-331669510
 
 
   @szha I just find it weird that it exists in symbol and not in gluon.
   I understand the underlying differences between the two, but the 
visualization is nice regardless
 

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[GitHub] szha commented on issue #7993: The Mulit-task learning example can not run

[git]

szha commented on issue #7993: The Mulit-task learning example can not run
URL: 
https://github.com/apache/incubator-mxnet/issues/7993#issuecomment-331668805
 
 
   #8006
 

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[GitHub] szha opened a new pull request #8006: fix example

[git]

szha opened a new pull request #8006: fix example
URL: https://github.com/apache/incubator-mxnet/pull/8006
 
 
   fixed multi-task learning example which wasn't running.
   removed multiple copies of mnist_iterator and moved away from wget/unzip for 
portability.
 

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[GitHub] szha commented on issue #7993: The Mulit-task learning example can not run

[git]

szha commented on issue #7993: The Mulit-task learning example can not run
URL: 
https://github.com/apache/incubator-mxnet/issues/7993#issuecomment-331667060
 
 
   Working on a fix
 

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[GitHub] szha commented on issue #7999: Is ndarray api designed for users construct networks to predict, and symbol for training?

[git]

szha commented on issue #7999: Is ndarray api designed for users  construct 
networks to predict, and symbol for training?
URL: 
https://github.com/apache/incubator-mxnet/issues/7999#issuecomment-331664568
 
 
   Feel free to reopen for follow up questions.
 

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[GitHub] szha closed issue #7999: Is ndarray api designed for users construct networks to predict, and symbol for training?

[git]

szha closed issue #7999: Is ndarray api designed for users  construct networks 
to predict, and symbol for training?
URL: https://github.com/apache/incubator-mxnet/issues/7999
 
 
   
 

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[GitHub] szha commented on issue #7968: [R] Transfer Learning using VGG-16

[git]

szha commented on issue #7968: [R] Transfer Learning using VGG-16
URL: 
https://github.com/apache/incubator-mxnet/issues/7968#issuecomment-331664534
 
 
   @thirdwing 
 

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[GitHub] szha commented on issue #7943: Request: Visualization for Gluon neural networks

[git]

szha commented on issue #7943: Request: Visualization for Gluon neural networks
URL: 
https://github.com/apache/incubator-mxnet/issues/7943#issuecomment-331664334
 
 
   It's a good idea to have visualization tools. Currently, for standard 
blocks, you can print them directly, though the string representation doesn't 
reflect the computation graph.
 

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[GitHub] eric-haibin-lin commented on a change in pull request #7698: Second order gradient and Subgraph execution

[git]

eric-haibin-lin commented on a change in pull request #7698: Second order 
gradient and Subgraph execution
URL: https://github.com/apache/incubator-mxnet/pull/7698#discussion_r140639796
 
 

 ##
 File path: src/imperative/cached_op.cc
 ##
 @@ -0,0 +1,463 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+#include 
+#include 
+#include "./imperative_utils.h"
+
+namespace mxnet {
+
+Imperative::CachedOp::CachedOp(const nnvm::Symbol& sym) {
+  using namespace nnvm;
+  using namespace imperative;
+  static const std::vector zero_ops{Op::Get("zeros_like"), 
Op::Get("_zeros")};
+  static const auto _copy = Op::Get("_copy");
+
+  // construct forward graph
+  {
+NodeEntryMap dedup_out;
+for (const auto& i : sym.outputs) {
+  if (dedup_out.count(i)) {
+NodePtr copy_node = Node::Create();
+copy_node->attrs.op = _copy;
+copy_node->attrs.name =
+i.node->attrs.name + "_copy" + std::to_string(dedup_out[i]++);
+copy_node->inputs.emplace_back(i);
+if (_copy->attr_parser != nullptr) {
+  _copy->attr_parser(&(copy_node->attrs));
+}
+fwd_graph_.outputs.push_back(NodeEntry{copy_node, 0, 0});
+  } else {
+dedup_out.insert({i, 0});
+fwd_graph_.outputs.push_back(i);
+  }
+}
+const auto& idx = fwd_graph_.indexed_graph();
+CHECK_GE(idx.input_nodes().size(), 1) << "CachedOp requires at least 1 
input";
+
+std::vector ref_count(idx.num_node_entries(), 0);
+for (const auto& i : idx.input_nodes()) ++ref_count[idx.entry_id(i, 0)];
+for (const auto& i : idx.outputs()) ++ref_count[idx.entry_id(i)];
+for (size_t i = 0; i < idx.num_nodes(); ++i) {
+  for (const auto& j : idx[i].inputs) ++ref_count[idx.entry_id(j)];
+}
+
+fwd_graph_.attrs["forward_ref_count"] =
+std::make_shared(std::move(ref_count));
+  }
+
+  // construct backward graph
+  std::vector ograd_entries;
+  {
+ograd_entries.reserve(fwd_graph_.outputs.size());
+for (size_t i = 0; i < fwd_graph_.outputs.size(); ++i) {
+  ograd_entries.emplace_back(NodeEntry{Node::Create(), 0, 0});
+}
+
+std::vector xs;
+std::vector args = sym.ListInputs(Symbol::kReadOnlyArgs);
+xs.reserve(args.size());
+for (const auto& i : args) xs.emplace_back(NodeEntry{i, 0, 0});
+CHECK_GT(xs.size(), 0)
+<< "There are no inputs in computation graph that require gradients.";
+
+grad_graph_ = pass::Gradient(
+fwd_graph_, fwd_graph_.outputs, xs, ograd_entries,
+exec::AggregateGradient, nullptr, nullptr,
+zero_ops, "_copy");
+  }
+
+  // construct full graph
+  {
+size_t num_forward_nodes = fwd_graph_.indexed_graph().num_nodes();
+size_t num_forward_entries = fwd_graph_.indexed_graph().num_node_entries();
+
+full_graph_.outputs = fwd_graph_.outputs;
+curr_grad_req_ = std::vector(grad_graph_.outputs.size(), true);
+for (const auto& i : grad_graph_.outputs) 
full_graph_.outputs.emplace_back(i);
+const auto& idx = full_graph_.indexed_graph();
+
+std::vector ref_count(idx.num_node_entries(), 0);
+for (size_t i = num_forward_nodes; i < idx.num_nodes(); ++i) {
+  for (const auto& j : idx[i].inputs) {
+ ++ref_count[idx.entry_id(j)];
+  }
+}
+
+auto full_ref_count = fwd_graph_.GetAttr("forward_ref_count");
+for (size_t i = 0; i < num_forward_entries; ++i) full_ref_count[i] += 
ref_count[i];
+fwd_graph_.attrs["full_ref_count"] =
+std::make_shared(std::move(full_ref_count));
+
+size_t num_forward_inputs = num_inputs();
+for (uint32_t i = 0; i < ograd_entries.size(); ++i) {
+  if (!idx.exist(ograd_entries[i].node.get())) continue;
+  auto eid = idx.entry_id(ograd_entries[i]);
+  if (ref_count[eid] > 0) {
+bwd_ograd_dep_.push_back(i);
+bwd_input_eid_.push_back(eid);
+  }
+}
+save_inputs_.resize(num_forward_inputs, false);
+for (uint32_t i = 0; i < num_forward_inputs; ++i) {
+  auto eid = idx.entry_id(idx.input_nodes()[i], 0);
+  if (ref_count[eid] > 0) {
+save_inputs_[i] = true;
+bwd_in_dep_.push_back(i);
+bwd_input_eid_.push_back(eid);
+  }
+  

[GitHub] szha closed issue #4791: who can give me a docker container with a ssh sever?so i can train my model with distributed computation

[git]

szha closed issue #4791: who can give me a docker container with a ssh sever?so 
i can train my model with distributed computation
URL: https://github.com/apache/incubator-mxnet/issues/4791
 
 
   
 

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[GitHub] szha closed issue #4726: I found it so slow on train compared with paddle or tensorflow

[git]

szha closed issue #4726: I found it so slow on train compared with paddle or 
tensorflow
URL: https://github.com/apache/incubator-mxnet/issues/4726
 
 
   
 

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[GitHub] szha commented on issue #7999: Is ndarray api designed for users construct networks to predict, and symbol for training?

[git]

szha commented on issue #7999: Is ndarray api designed for users  construct 
networks to predict, and symbol for training?
URL: 
https://github.com/apache/incubator-mxnet/issues/7999#issuecomment-331663218
 
 
   both symbol and ndarray can do both training and prediction.
 

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[incubator-mxnet] branch szha-patch-1 deleted (was d6cb7f0)

[zhasheng]

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zhasheng pushed a change to branch szha-patch-1
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.


 was d6cb7f0  Update conv_rnn_cell.py

The revisions that were on this branch are still contained in
other references; therefore, this change does not discard any commits
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[incubator-mxnet] branch master updated: Update conv_rnn_cell.py (#8002)

[zhasheng]

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zhasheng pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git


The following commit(s) were added to refs/heads/master by this push:
 new 3e1ad58  Update conv_rnn_cell.py (#8002)
3e1ad58 is described below

commit 3e1ad5818adba5c42c86ff5888780d90f6483610
Author: Sheng Zha 
AuthorDate: Sat Sep 23 11:58:20 2017 -0700

Update conv_rnn_cell.py (#8002)
---
 python/mxnet/gluon/contrib/rnn/conv_rnn_cell.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/mxnet/gluon/contrib/rnn/conv_rnn_cell.py 
b/python/mxnet/gluon/contrib/rnn/conv_rnn_cell.py
index f060884..fbe6981 100644
--- a/python/mxnet/gluon/contrib/rnn/conv_rnn_cell.py
+++ b/python/mxnet/gluon/contrib/rnn/conv_rnn_cell.py
@@ -595,7 +595,7 @@ class Conv2DLSTMCell(_ConvLSTMCell):
 Container for weight sharing between cells. Created if None.
 """
 def __init__(self, input_shape, hidden_channels,
- i2h_kernel=(3, 3), h2h_kernel=(3, 3),
+ i2h_kernel, h2h_kernel,
  i2h_pad=(0, 0),
  i2h_dilate=(1, 1), h2h_dilate=(1, 1),
  i2h_weight_initializer=None, h2h_weight_initializer=None,

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[GitHub] szha closed pull request #8002: Update conv_rnn_cell.py

[git]

szha closed pull request #8002: Update conv_rnn_cell.py
URL: https://github.com/apache/incubator-mxnet/pull/8002
 
 
   
 

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[GitHub] piiswrong closed pull request #8003: Update executor_group.py

[git]

piiswrong closed pull request #8003: Update executor_group.py
URL: https://github.com/apache/incubator-mxnet/pull/8003
 
 
   
 

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[incubator-mxnet] branch master updated: Update executor_group.py (#8003)

[jxie]

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jxie pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git


The following commit(s) were added to refs/heads/master by this push:
 new 4aaefa0  Update executor_group.py (#8003)
4aaefa0 is described below

commit 4aaefa04b1329e81c28cfa3ea4376e1a7e169611
Author: Eric Junyuan Xie 
AuthorDate: Sat Sep 23 11:36:33 2017 -0700

Update executor_group.py (#8003)
---
 python/mxnet/module/executor_group.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/mxnet/module/executor_group.py 
b/python/mxnet/module/executor_group.py
index 6dc16ab..65c261b 100755
--- a/python/mxnet/module/executor_group.py
+++ b/python/mxnet/module/executor_group.py
@@ -583,9 +583,9 @@ class DataParallelExecutorGroup(object):
 if islice.stop > valid_stop:
 islice = slice(islice.start, valid_stop)
 oslice = slice(0, islice.stop - islice.start)
-for label, laxis, output, oaxis in \
-zip(labels, self.label_layouts, texec.outputs, 
self.output_layouts):
+for label, laxis in zip(labels, self.label_layouts):
 labels_slice.append(_slice_axis(label, laxis, islice))
+for output, oaxis in zip(texec.outputs, self.output_layouts):
 outputs_slice.append(_slice_axis(output, oaxis, oslice))
 labels_ = OrderedDict(zip(self.label_names, labels_slice))
 preds = OrderedDict(zip(self.output_names, outputs_slice))

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[GitHub] sxjscience commented on issue #7942: Adam optimizer consistent with paper

[git]

sxjscience commented on issue #7942: Adam optimizer consistent with paper
URL: https://github.com/apache/incubator-mxnet/pull/7942#issuecomment-331651717
 
 
   @formath I feel that setting `rho` to be smaller than 1 can gradually 
transform the estimated gradient from a biased estimation to an unbiased 
estimation, which may be helpful in scenarios where the data distribution is 
changing (like in the online learning setting) [1]. However, I've checked the 
Adam paper and haven't found the rho hyper-parameter. Could you help point out 
the section in the paper? Also, we need to better document its usage.
   
   [1] Follow the Moving Leader in Deep Learning, Shuai Zheng & James T. Kwok, 
ICML 2017
 

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[GitHub] sxjscience commented on issue #7942: Adam optimizer consistent with paper

[git]

sxjscience commented on issue #7942: Adam optimizer consistent with paper
URL: https://github.com/apache/incubator-mxnet/pull/7942#issuecomment-331651717
 
 
   @formath I feel that the `rho` has the effect to gradually transform the 
estimated gradient from a biased estimation to an unbiased estimation, which 
may be helpful in scenarios where the data distribution is changing (like in 
the online learning setting) [1]. However, I've checked the Adam paper and 
haven't found the rho hyper-parameter. Could you help point out the section in 
the paper? Also, we need to better document its usage.
   
   [1] Follow the Moving Leader in Deep Learning, Shuai Zheng & James T. Kwok, 
ICML 2017
 

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[GitHub] sxjscience commented on issue #7942: Adam optimizer consistent with paper

[git]

sxjscience commented on issue #7942: Adam optimizer consistent with paper
URL: https://github.com/apache/incubator-mxnet/pull/7942#issuecomment-331651717
 
 
   @formath I feel that the `rho` has the effect to gradually transform the 
estimated gradient from a biased estimation to an unbiased estimated, which may 
have some advantages in the online learning setting where the data distribution 
is changing [1]. However, I've checked the Adam paper and haven't found the rho 
hyper-parameter. Could you help point out the section in the paper? Also, we 
need to better document its usage.
   
   [1] Follow the Moving Leader in Deep Learning, Shuai Zheng & James T. Kwok, 
ICML 2017
 

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[GitHub] sxjscience commented on issue #7942: Adam optimizer consistent with paper

[git]

sxjscience commented on issue #7942: Adam optimizer consistent with paper
URL: https://github.com/apache/incubator-mxnet/pull/7942#issuecomment-331651717
 
 
   @formath I feel that the `rho` has the effect to gradually transform the 
estimated gradient from a biased estimation to an unbiased estimated, which may 
have some advantages in the online learning setting where the data distribution 
is changing [1]. However, I've checked the Adam paper and haven't found the rho 
hyper-parameter. Could you help point out the section in the paper? Also, we 
need to better document its usage.
   
   [1] Follow the Moving Leader in Deep Learning, Shuai Zheng and James T. 
Kwok, ICML 2017
 

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[GitHub] sxjscience commented on issue #7942: Adam optimizer consistent with paper

[git]

sxjscience commented on issue #7942: Adam optimizer consistent with paper
URL: https://github.com/apache/incubator-mxnet/pull/7942#issuecomment-331651717
 
 
   @formath I feel that the `rho` has the effect to gradually transform the 
estimated gradient from a biased estimation to an unbiased estimated, which may 
have some advantages in the online learning setting where the data distribution 
is changing [1]. However, I've checked the Adam paper and haven't found the rho 
hyper-parameter. Could you help point out the section in the paper? Also, we 
need to better document its usage.
   
   [1] Follow the Moving Leader in Deep Learning, Shuai Zheng, James T. Kwok, 
ICML 2017
 

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[GitHub] sxjscience commented on issue #7942: Adam optimizer consistent with paper

[git]

sxjscience commented on issue #7942: Adam optimizer consistent with paper
URL: https://github.com/apache/incubator-mxnet/pull/7942#issuecomment-331651717
 
 
   @formath I feel that the `rho` has the effect to gradually transform the 
estimated gradient from a biased estimation to an unbiased estimated, which may 
have some advantages in the online learning setting where the data distribution 
is changing [1]. However, I've checked the Adam paper and haven't found the rho 
hyper-parameter. Could you help point out the section in the paper? Also, we 
need to better document its usage.
   
   [1] Shuai Zheng, James T. Kwok, Follow the Moving Leader in Deep Learning, 
ICML 2017
 

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[GitHub] sxjscience commented on issue #7942: Adam optimizer consistent with paper

[git]

sxjscience commented on issue #7942: Adam optimizer consistent with paper
URL: https://github.com/apache/incubator-mxnet/pull/7942#issuecomment-331651717
 
 
   @formath I feel that the `rho` has the effect to gradually transform the 
gradient estimator from biased to unbiased, which may have some advantages. 
However, I've checked the Adam paper and haven't found the rho hyper-parameter. 
Could you help point out the section in the paper? Also, we need to better 
document its usage.
 

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[GitHub] sxjscience commented on issue #7942: Adam optimizer consistent with paper

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sxjscience commented on issue #7942: Adam optimizer consistent with paper
URL: https://github.com/apache/incubator-mxnet/pull/7942#issuecomment-331651717
 
 
   @formath I feel that the `rho` has the effect to gradually transform the 
gradient estimator from biased to unbiased, which may have some advantages. 
However, I've checked the Adam paper and haven't found the rho hyper-parameter. 
Could you help point out the section in the paper? Also, we need to better 
document the it's usage.
 

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[GitHub] sxjscience commented on issue #7942: Adam optimizer consistent with paper

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sxjscience commented on issue #7942: Adam optimizer consistent with paper
URL: https://github.com/apache/incubator-mxnet/pull/7942#issuecomment-331651717
 
 
   @formath I feel that the `rho` has the effect to gradually transforms the 
gradient estimator from biased to unbiased, which may have some advantages. 
However, I've checked the Adam paper and haven't found the rho hyper-parameter. 
Could you help point out the section in the paper? Also, we need to better 
document the it's usage.
 

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[GitHub] jiarenyf commented on issue #7989: Update metric without considering the dataBatch.pad ?

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jiarenyf commented on issue #7989: Update metric without considering the 
dataBatch.pad ?
URL: 
https://github.com/apache/incubator-mxnet/issues/7989#issuecomment-331638212
 
 
   ??
 

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[GitHub] edmBernard closed issue #7964: Gradient accumulation of several sample

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edmBernard closed issue #7964: Gradient accumulation of several sample
URL: https://github.com/apache/incubator-mxnet/issues/7964
 
 
   
 

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[GitHub] edmBernard commented on issue #7964: Gradient accumulation of several sample

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edmBernard commented on issue #7964: Gradient accumulation of several sample
URL: 
https://github.com/apache/incubator-mxnet/issues/7964#issuecomment-331624308
 
 
   I was looking for a more regular way :(
   I will test to hack the optimizer thx
 

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[GitHub] zannxD opened a new issue #8004: Make a prediction using mxnet CNN model for text sentence classification

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zannxD opened a new issue #8004: Make a prediction using mxnet CNN model for 
text sentence classification
URL: https://github.com/apache/incubator-mxnet/issues/8004
 
 
   
   Hi I'm a newbie to data science, I followed this 
tutorial?https://mxnet.incubator.apache.org/tutorials/nlp/cnn.html?but I am 
confused over how to make a single prediction using the trained model generated 
by the above mentioned tutorial. Please guide me the right direction to fix 
this. Thanks.
   
   
   
 

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[GitHub] chinakook closed issue #8001: mx.metric.EvalMetric bug

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chinakook closed issue #8001: mx.metric.EvalMetric bug
URL: https://github.com/apache/incubator-mxnet/issues/8001
 
 
   
 

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[GitHub] chinakook commented on issue #8001: mx.metric.EvalMetric bug

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chinakook commented on issue #8001: mx.metric.EvalMetric bug
URL: 
https://github.com/apache/incubator-mxnet/issues/8001#issuecomment-331616327
 
 
   This fix works, thanks very much!
 

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[GitHub] szha commented on issue #7319: [RoadMap] Legacy issue resolution before 1.0 release

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szha commented on issue #7319: [RoadMap] Legacy issue resolution before 1.0 
release
URL: 
https://github.com/apache/incubator-mxnet/issues/7319#issuecomment-331613233
 
 
   I have the impression that many ops don't respect grad_req.
 

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