jiarenyf commented on issue #8315: There is a bug in metric.py
URL:
https://github.com/apache/incubator-mxnet/issues/8315#issuecomment-338111435
By the way, I found that nearly no one answer issue questions for a long
time ... Is this framework being given up ...
jiarenyf commented on issue #8315: There is a bug in metric.py
URL:
https://github.com/apache/incubator-mxnet/issues/8315#issuecomment-33844
... The `output_names` is None (default), or a list when you new a metric
class with a arg called output_names ... It can not be a int ...
liuzhi136 commented on issue #8341: Training error always fluctuates and
doesn't decrease.
URL:
https://github.com/apache/incubator-mxnet/issues/8341#issuecomment-338108545
@szha Do you have any idea about this?
This is an
jiarenyf commented on issue #8347: CTC Example Problem
URL:
https://github.com/apache/incubator-mxnet/issues/8347#issuecomment-338108475
??
This is an automated message from the Apache Git Service.
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zheng-da commented on issue #8354: How to add NNVM operator with auxiliary
states
URL:
https://github.com/apache/incubator-mxnet/issues/8354#issuecomment-338100362
You can use nnvm::FMutateInputs to specify auxiliary states. The link below
shows an example.
ZiyueHuang commented on issue #8338: master branch cannot build on centos 7
with cuda-8.0
URL:
https://github.com/apache/incubator-mxnet/issues/8338#issuecomment-338100092
@mseeger Yes, if I checkout to 9f97dac76e43b2ca0acb09a4ff96d416e9edea60, the
one just before your commit. It can
shadowleaves opened a new issue #4045: inferred shape error with FullyConnected
layer
URL: https://github.com/apache/incubator-mxnet/issues/4045
very simple program that starts with two input nodes (batch_size=100,
n_inputs=2) and then maps it to a layer of 3 hidden nodes via a 2x3 weight
yuewu001 opened a new issue #8354: How to add NNVM operator with auxiliary
states
URL: https://github.com/apache/incubator-mxnet/issues/8354
When adding new operators with auxiliary states, how to set the attributes
with NNVM_REGISTER_OP ?
chinakook commented on issue #8335: Performance of MXNet on Windows is lower
than that on Linux by 15%-20%
URL:
https://github.com/apache/incubator-mxnet/issues/8335#issuecomment-338080017
But the cpu performance of Windows is also lower. Our customers use Windows
so I cannot give up.
rahul003 commented on a change in pull request #8342: [WIP] 2bit gradient
compression
URL: https://github.com/apache/incubator-mxnet/pull/8342#discussion_r145859932
##
File path: src/kvstore/comm.h
##
@@ -79,8 +79,35 @@ class Comm {
return pinned_ctx_;
}
+ /**
cjolivier01 commented on a change in pull request #8340: Fill optimizations
URL: https://github.com/apache/incubator-mxnet/pull/8340#discussion_r145845639
##
File path: src/operator/tensor/init_op.h
##
@@ -164,19 +164,38 @@ inline bool InitStorageType(const
cjolivier01 commented on a change in pull request #8340: Fill optimizations
URL: https://github.com/apache/incubator-mxnet/pull/8340#discussion_r145845639
##
File path: src/operator/tensor/init_op.h
##
@@ -164,19 +164,38 @@ inline bool InitStorageType(const
nickeleres commented on issue #8350: Incorrect implied shape inside loss
function
URL:
https://github.com/apache/incubator-mxnet/issues/8350#issuecomment-338058033
I had to set `sparse_label=False` in my loss function, which now looks like:
cjolivier01 commented on a change in pull request #8340: Fill optimizations
URL: https://github.com/apache/incubator-mxnet/pull/8340#discussion_r145845639
##
File path: src/operator/tensor/init_op.h
##
@@ -164,19 +164,38 @@ inline bool InitStorageType(const
cjolivier01 commented on a change in pull request #8340: Fill optimizations
URL: https://github.com/apache/incubator-mxnet/pull/8340#discussion_r145845639
##
File path: src/operator/tensor/init_op.h
##
@@ -164,19 +164,38 @@ inline bool InitStorageType(const
cjolivier01 commented on a change in pull request #8340: Fill optimizations
URL: https://github.com/apache/incubator-mxnet/pull/8340#discussion_r145845639
##
File path: src/operator/tensor/init_op.h
##
@@ -164,19 +164,38 @@ inline bool InitStorageType(const
louisfeng commented on a change in pull request #7931: MKL-DNN integration:
request for reviews
URL: https://github.com/apache/incubator-mxnet/pull/7931#discussion_r145845602
##
File path: src/operator/mkl/mkldnn_elemwise_sum-inl.h
##
@@ -0,0 +1,233 @@
nickeleres commented on issue #8350: Incorrect implied shape inside loss
function
URL:
https://github.com/apache/incubator-mxnet/issues/8350#issuecomment-338061064
Awesome, thank you so much
This is an automated message
zhreshold commented on issue #8350: Incorrect implied shape inside loss function
URL:
https://github.com/apache/incubator-mxnet/issues/8350#issuecomment-338060914
gluon.loss.SoftmaxCrossEntropyLoss(sparse_label=False)
This
nickeleres commented on issue #8350: Incorrect implied shape inside loss
function
URL:
https://github.com/apache/incubator-mxnet/issues/8350#issuecomment-338060702
Ok. So what is the explicit correct loss function for one-hot labels?
louisfeng commented on a change in pull request #7931: MKL-DNN integration:
request for reviews
URL: https://github.com/apache/incubator-mxnet/pull/7931#discussion_r145844506
##
File path: src/operator/mkl/mkldnn_elemwise_sum-inl.h
##
@@ -0,0 +1,233 @@
zhreshold commented on issue #8350: Incorrect implied shape inside loss function
URL:
https://github.com/apache/incubator-mxnet/issues/8350#issuecomment-338060442
@nickeleres Just to mention that what I meant is sparse_label=False,
from_logits is used if log_softmax is applied prior to
szha commented on issue #7931: MKL-DNN integration: request for reviews
URL: https://github.com/apache/incubator-mxnet/pull/7931#issuecomment-338059265
@ykim362 BTW is the fix in mklml_lnx_2018.0.20170908.tgz? Does it make sense
to upgrade the library for mkl2017 use case? Many people are
zhreshold commented on issue #8348: mxnet.gluon.data.vision.ImageRecordDataset
key error
URL:
https://github.com/apache/incubator-mxnet/issues/8348#issuecomment-338058462
Should be fixed in #8353
This is an automated
ykim362 commented on issue #7931: MKL-DNN integration: request for reviews
URL: https://github.com/apache/incubator-mxnet/pull/7931#issuecomment-338058204
@szha Sure, I am looking into it.
This is an automated message from
nickeleres commented on issue #8350: Incorrect implied shape inside loss
function
URL:
https://github.com/apache/incubator-mxnet/issues/8350#issuecomment-338058033
I had to set `sparse_label=False` in my loss function, which now looks like:
nickeleres closed issue #8350: Incorrect implied shape inside loss function
URL: https://github.com/apache/incubator-mxnet/issues/8350
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szha commented on issue #7931: MKL-DNN integration: request for reviews
URL: https://github.com/apache/incubator-mxnet/pull/7931#issuecomment-338057682
@ykim362 could you verify if #8196 is fixed?
This is an automated message
zheng-da commented on a change in pull request #7931: MKL-DNN integration:
request for reviews
URL: https://github.com/apache/incubator-mxnet/pull/7931#discussion_r145817839
##
File path: src/operator/mkl/mkldnn_elemwise_sum-inl.h
##
@@ -0,0 +1,233 @@
zheng-da commented on a change in pull request #7931: MKL-DNN integration:
request for reviews
URL: https://github.com/apache/incubator-mxnet/pull/7931#discussion_r145819255
##
File path: src/operator/tensor/elemwise_binary_op_basic.cc
##
@@ -23,11 +23,22 @@
*/
zheng-da commented on a change in pull request #7931: MKL-DNN integration:
request for reviews
URL: https://github.com/apache/incubator-mxnet/pull/7931#discussion_r145817991
##
File path: src/operator/mkl/mkldnn_elemwise_sum-inl.h
##
@@ -0,0 +1,233 @@
nickeleres commented on issue #8350: Incorrect implied shape inside loss
function
URL:
https://github.com/apache/incubator-mxnet/issues/8350#issuecomment-338056483
This is my new loss function:
`softmax_cross_entropy =
gluon.loss.SoftmaxCrossEntropyLoss(from_logits=True)`
nickeleres commented on issue #8350: Incorrect implied shape inside loss
function
URL:
https://github.com/apache/incubator-mxnet/issues/8350#issuecomment-338056483
This is my new loss function:
`softmax_cross_entropy =
gluon.loss.SoftmaxCrossEntropyLoss(from_logits=True)`
nickeleres commented on issue #8350: Incorrect implied shape inside loss
function
URL:
https://github.com/apache/incubator-mxnet/issues/8350#issuecomment-338056483
This is my new loss function:
`softmax_cross_entropy =
gluon.loss.SoftmaxCrossEntropyLoss(from_logits=True)`
zhreshold commented on issue #8350: Incorrect implied shape inside loss function
URL:
https://github.com/apache/incubator-mxnet/issues/8350#issuecomment-338055150
You can use gluon.loss.SoftmaxCrossEntropyLoss, where you can specify
`from_logits=True` to use one_hot labels.
nickeleres commented on issue #8350: Incorrect implied shape inside loss
function
URL:
https://github.com/apache/incubator-mxnet/issues/8350#issuecomment-338052781
No custom loss function
`loss = softmax_cross_entropy(output, label)`
It looks like the
nickeleres commented on issue #8350: Incorrect implied shape inside loss
function
URL:
https://github.com/apache/incubator-mxnet/issues/8350#issuecomment-338054520
When I decrease the batch size to 1, the training labels are simply integers
**(which is actually the 0th entry in the
nickeleres commented on issue #8350: Incorrect implied shape inside loss
function
URL:
https://github.com/apache/incubator-mxnet/issues/8350#issuecomment-338054520
When I decrease the batch size to 1, the training labels are simply integers
**(the 0th entry in the one-hot array for each
cjolivier01 commented on issue #8340: Fill optimizations
URL: https://github.com/apache/incubator-mxnet/pull/8340#issuecomment-338053030
Ok @szha and I spoke offline. I added the new operator _full to be used for
ndarray.full() function.
It is tested in test_ndarray.test_outputs()
nickeleres commented on issue #8350: Incorrect implied shape inside loss
function
URL:
https://github.com/apache/incubator-mxnet/issues/8350#issuecomment-338052781
No custom loss function
`loss = softmax_cross_entropy(output, label)`
It looks like the
nickeleres commented on issue #8350: Incorrect implied shape inside loss
function
URL:
https://github.com/apache/incubator-mxnet/issues/8350#issuecomment-338052781
No custom loss function
`loss = softmax_cross_entropy(output, label)`
It looks like the
zhreshold commented on issue #8350: Incorrect implied shape inside loss function
URL:
https://github.com/apache/incubator-mxnet/issues/8350#issuecomment-338051045
Please post your custom loss function
This is an automated
zhreshold closed issue #8310: Bug in ./example/
URL: https://github.com/apache/incubator-mxnet/issues/8310
This is an automated message from the Apache Git Service.
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nickeleres commented on issue #8350: Incorrect implied shape inside loss
function
URL:
https://github.com/apache/incubator-mxnet/issues/8350#issuecomment-338049715
When I reshaped my label to (32, 1), as the error message stated, I got the
training to run, but the alignment between the
zhreshold opened a new pull request #8352: fix using default mean pixels
URL: https://github.com/apache/incubator-mxnet/pull/8352
## Description ##
(Brief description on what this PR is about)
## Checklist ##
### Essentials ###
- [x] Passed code style checking (`make lint`)
cjolivier01 commented on a change in pull request #8342: [WIP] 2bit gradient
compression
URL: https://github.com/apache/incubator-mxnet/pull/8342#discussion_r145832223
##
File path: src/ndarray/ndarray.cc
##
@@ -558,6 +558,101 @@ void CopyFromTo(const NDArray& from, const
cjolivier01 commented on a change in pull request #8342: [WIP] 2bit gradient
compression
URL: https://github.com/apache/incubator-mxnet/pull/8342#discussion_r145833515
##
File path: src/operator/contrib/two_bit_quantize-inl.h
##
@@ -0,0 +1,340 @@
+/*
+ * Licensed to the
cjolivier01 commented on a change in pull request #8342: [WIP] 2bit gradient
compression
URL: https://github.com/apache/incubator-mxnet/pull/8342#discussion_r145833486
##
File path: src/operator/contrib/two_bit_quantize-inl.h
##
@@ -0,0 +1,340 @@
+/*
+ * Licensed to the
cjolivier01 commented on a change in pull request #8342: [WIP] 2bit gradient
compression
URL: https://github.com/apache/incubator-mxnet/pull/8342#discussion_r145831418
##
File path: src/ndarray/ndarray.cc
##
@@ -558,6 +558,101 @@ void CopyFromTo(const NDArray& from, const
cjolivier01 commented on a change in pull request #8342: [WIP] 2bit gradient
compression
URL: https://github.com/apache/incubator-mxnet/pull/8342#discussion_r145832475
##
File path: src/ndarray/ndarray.cc
##
@@ -558,6 +558,101 @@ void CopyFromTo(const NDArray& from, const
cjolivier01 commented on a change in pull request #8342: [WIP] 2bit gradient
compression
URL: https://github.com/apache/incubator-mxnet/pull/8342#discussion_r145833204
##
File path: src/operator/contrib/two_bit_quantize-inl.h
##
@@ -0,0 +1,340 @@
+/*
+ * Licensed to the
cjolivier01 commented on a change in pull request #8342: [WIP] 2bit gradient
compression
URL: https://github.com/apache/incubator-mxnet/pull/8342#discussion_r145833629
##
File path: src/operator/contrib/two_bit_quantize-inl.h
##
@@ -0,0 +1,340 @@
+/*
+ * Licensed to the
cjolivier01 commented on a change in pull request #8342: [WIP] 2bit gradient
compression
URL: https://github.com/apache/incubator-mxnet/pull/8342#discussion_r145832626
##
File path: src/ndarray/ndarray_function.cc
##
@@ -183,5 +184,22 @@ void
cjolivier01 commented on a change in pull request #8342: [WIP] 2bit gradient
compression
URL: https://github.com/apache/incubator-mxnet/pull/8342#discussion_r145831125
##
File path: src/ndarray/ndarray.cc
##
@@ -558,6 +558,101 @@ void CopyFromTo(const NDArray& from, const
cjolivier01 commented on a change in pull request #8342: [WIP] 2bit gradient
compression
URL: https://github.com/apache/incubator-mxnet/pull/8342#discussion_r145831333
##
File path: src/ndarray/ndarray.cc
##
@@ -558,6 +558,101 @@ void CopyFromTo(const NDArray& from, const
cjolivier01 commented on a change in pull request #8342: [WIP] 2bit gradient
compression
URL: https://github.com/apache/incubator-mxnet/pull/8342#discussion_r145832799
##
File path: src/operator/contrib/two_bit_quantize-inl.h
##
@@ -0,0 +1,340 @@
+/*
+ * Licensed to the
cjolivier01 commented on a change in pull request #8342: [WIP] 2bit gradient
compression
URL: https://github.com/apache/incubator-mxnet/pull/8342#discussion_r145832433
##
File path: src/ndarray/ndarray.cc
##
@@ -558,6 +558,101 @@ void CopyFromTo(const NDArray& from, const
cjolivier01 commented on a change in pull request #8342: [WIP] 2bit gradient
compression
URL: https://github.com/apache/incubator-mxnet/pull/8342#discussion_r145832653
##
File path: src/ndarray/ndarray_function.cu
##
@@ -202,5 +203,22 @@ void
cjolivier01 commented on a change in pull request #8342: [WIP] 2bit gradient
compression
URL: https://github.com/apache/incubator-mxnet/pull/8342#discussion_r145832885
##
File path: src/operator/contrib/two_bit_quantize-inl.h
##
@@ -0,0 +1,340 @@
+/*
+ * Licensed to the
cjolivier01 commented on a change in pull request #8342: [WIP] 2bit gradient
compression
URL: https://github.com/apache/incubator-mxnet/pull/8342#discussion_r145832353
##
File path: src/ndarray/ndarray.cc
##
@@ -558,6 +558,101 @@ void CopyFromTo(const NDArray& from, const
cjolivier01 commented on a change in pull request #8342: [WIP] 2bit gradient
compression
URL: https://github.com/apache/incubator-mxnet/pull/8342#discussion_r145831668
##
File path: src/ndarray/ndarray.cc
##
@@ -558,6 +558,101 @@ void CopyFromTo(const NDArray& from, const
kpot commented on issue #8337: mx.autograd.grad works or fails depending on use
of slices
URL:
https://github.com/apache/incubator-mxnet/issues/8337#issuecomment-338047855
@piiswrong Is there any better way to get the graph/symbol from autograd?
Because the method I use seems logical to
cjolivier01 commented on a change in pull request #8342: [WIP] 2bit gradient
compression
URL: https://github.com/apache/incubator-mxnet/pull/8342#discussion_r145830977
##
File path: src/kvstore/kvstore_local.h
##
@@ -135,6 +135,13 @@ class KVStoreLocal : public KVStore {
cjolivier01 commented on a change in pull request #8342: [WIP] 2bit gradient
compression
URL: https://github.com/apache/incubator-mxnet/pull/8342#discussion_r145830977
##
File path: src/kvstore/kvstore_local.h
##
@@ -135,6 +135,13 @@ class KVStoreLocal : public KVStore {
cjolivier01 commented on a change in pull request #8342: [WIP] 2bit gradient
compression
URL: https://github.com/apache/incubator-mxnet/pull/8342#discussion_r145830772
##
File path: src/kvstore/kvstore_local.h
##
@@ -135,6 +135,13 @@ class KVStoreLocal : public KVStore {
cjolivier01 commented on a change in pull request #8342: [WIP] 2bit gradient
compression
URL: https://github.com/apache/incubator-mxnet/pull/8342#discussion_r145830615
##
File path: src/kvstore/kvstore_dist_server.h
##
@@ -428,20 +468,42 @@ class KVStoreDistServer {
}
cjolivier01 commented on a change in pull request #8342: [WIP] 2bit gradient
compression
URL: https://github.com/apache/incubator-mxnet/pull/8342#discussion_r145830311
##
File path: src/kvstore/comm.h
##
@@ -79,8 +79,35 @@ class Comm {
return pinned_ctx_;
}
+
piiswrong commented on issue #8337: mx.autograd.grad works or fails depending
on use of slices
URL:
https://github.com/apache/incubator-mxnet/issues/8337#issuecomment-338043714
Looks like it might be a bug. I'll look into it.
But getting the symbol from a autograd NDArray and
piiswrong commented on issue #8312: Gradient function not returning enough
gradients
URL:
https://github.com/apache/incubator-mxnet/issues/8312#issuecomment-338040865
Fixed here https://github.com/apache/incubator-mxnet/pull/8322
rahul003 commented on a change in pull request #8342: [WIP] 2bit gradient
compression
URL: https://github.com/apache/incubator-mxnet/pull/8342#discussion_r145826758
##
File path: python/mxnet/kvstore.py
##
@@ -349,6 +349,101 @@ def row_sparse_pull(self, key, out=None,
cjolivier01 opened a new pull request #8351: Allow test to converge
URL: https://github.com/apache/incubator-mxnet/pull/8351
## Description ##
(Brief description on what this PR is about)
## Checklist ##
### Essentials ###
- [ ] Passed code style checking (`make lint`)
-
reminisce commented on issue #8292: mx.nd.array indexing broken in armv7 /
raspberrypi / jessie 8.0 (5 dimensional tensor)
URL:
https://github.com/apache/incubator-mxnet/issues/8292#issuecomment-338034856
@larroy Yes, there is a high chance that something went wrong in the op's
backend
cjolivier01 commented on issue #8340: Fill optimizations
URL: https://github.com/apache/incubator-mxnet/pull/8340#issuecomment-338025849
'full' on the radar? that struct isn't used. if someone wants to use it,
they can add it. I am not going to add an unused kernel.
cjolivier01 commented on issue #8340: Fill optimizations
URL: https://github.com/apache/incubator-mxnet/pull/8340#issuecomment-338025849
'full' on the radar? that struct isn't used. if someone wants to use it,
they can add it. I am not going to add an unused kernel.
benqua commented on issue #8297: [scala] Make accuracy idependant of output
size (fix #8226)
URL: https://github.com/apache/incubator-mxnet/pull/8297#issuecomment-338018171
@javelinjs ok, I updated the PR as you suggested.
(I should maybe have done a new one because the comment thread
nickeleres commented on issue #8350: Incorrect implied shape inside loss
function
URL:
https://github.com/apache/incubator-mxnet/issues/8350#issuecomment-338004788
@pluskid closed a similar unresolved issue
https://github.com/apache/incubator-mxnet/issues/880
ZiyueHuang commented on a change in pull request #8259: check_format of ndrray,
mainly for csr
URL: https://github.com/apache/incubator-mxnet/pull/8259#discussion_r145793132
##
File path: tests/python/unittest/test_sparse_ndarray.py
##
@@ -647,7 +647,20 @@ def
nickeleres opened a new issue #8350: Implied shape when calculating loss is
incorrect
URL: https://github.com/apache/incubator-mxnet/issues/8350
Ive seen this brought up in a couple other issues, but it hasnt been
resolved as far as I know.
The data I am feeding into my loss
nickeleres commented on issue #4045: inferred shape error with FullyConnected
layer
URL:
https://github.com/apache/incubator-mxnet/issues/4045#issuecomment-337983895
I am having the same issue, where it thinks my weight matrix should be shape
(N, 1)
larroy commented on issue #8292: mx.nd.array indexing broken in armv7 /
raspberrypi / jessie 8.0 (5 dimensional tensor)
URL:
https://github.com/apache/incubator-mxnet/issues/8292#issuecomment-337971985
I think the problem must be in array slicing inside the native lib. I have
debugged
larroy commented on issue #8292: mx.nd.array indexing broken in armv7 /
raspberrypi / jessie 8.0 (5 dimensional tensor)
URL:
https://github.com/apache/incubator-mxnet/issues/8292#issuecomment-337971985
I think the problem must be in array slicing inside the native lib. I have
debugged
wzhang1 commented on issue #8335: Performance of MXNet on Windows is lower
than that on Linux by 15%-20%
URL:
https://github.com/apache/incubator-mxnet/issues/8335#issuecomment-337967220
I've seen some cudnn example slower in windows than linux, then gave up
windows, is this a mxnet
tdomhan commented on issue #8334: Bugfix: Python 3 compatiblity during
optimizer serialization.
URL: https://github.com/apache/incubator-mxnet/pull/8334#issuecomment-337964938
sounds good. I updated the PR.
This is an
cjolivier01 commented on issue #8343: [CMAKE] Cmake changes, upgrade training
test so it converge
URL: https://github.com/apache/incubator-mxnet/pull/8343#issuecomment-337963496
Triggered a rebuild attempt due to CI problems
ShownX opened a new issue #8349: [New features] bincounts
URL: https://github.com/apache/incubator-mxnet/issues/8349
Ask for bincount function of ndarray, right now I only can use function from
numpy, from #8193
This is an
mseeger commented on issue #8323: clean up math operators
URL: https://github.com/apache/incubator-mxnet/pull/8323#issuecomment-337948535
Hi Eric, I did some test to confirm that this solution works.
Please do merge it in. Otherwise, if you are too busy, I can take it over.
qiliux opened a new issue #8348: mxnet.gluon.data.vision.ImageRecordDataset key
error
URL: https://github.com/apache/incubator-mxnet/issues/8348
Note: Providing complete information in the most concise form is the best
way to get help. This issue template serves as the checklist for
larroy commented on issue #8231: [MXNet 0.11.0 + RPi 3 + Python 2.7] ndarray
unit test fails
URL:
https://github.com/apache/incubator-mxnet/issues/8231#issuecomment-337896318
Duplicate of https://github.com/apache/incubator-mxnet/issues/8292
agataradys commented on issue #8291: Import error in SSD example
URL:
https://github.com/apache/incubator-mxnet/issues/8291#issuecomment-337885455
@edmBernard Thank you, this solution worked. Are you going to fix this
example to work on both Python versions? Do you still support Python2?
novioleo commented on issue #8151: Amalgamation for android arm64 was built
successfully but failed to run in device
URL:
https://github.com/apache/incubator-mxnet/issues/8151#issuecomment-337869571
@zhenglaizhang you have to also add indexedRecordIOSplitter into
mxnet_predict0.cc
mseeger commented on issue #8338: master branch cannot build on centos 7 with
cuda-8.0
URL:
https://github.com/apache/incubator-mxnet/issues/8338#issuecomment-337862092
The error messages seem to depend on mshadow_op.h only through
smooth_l1_gradient. And that code is really independent
jiarenyf commented on issue #8347: CTC Example Problem
URL:
https://github.com/apache/incubator-mxnet/issues/8347#issuecomment-337843827
@pluskid @thinxer
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gongqiang commented on issue #8189: Feed forward pass memory leaks (using htop)
URL:
https://github.com/apache/incubator-mxnet/issues/8189#issuecomment-337843053
thanks for the info, but my memory still keep increasing when speed is too
fast(like 140 sample/s) ^_^ @kazizzad
jiarenyf opened a new issue #8347: CTC Example Problem
URL: https://github.com/apache/incubator-mxnet/issues/8347
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kazizzad commented on issue #8189: Feed forward pass memory leaks (using htop)
URL:
https://github.com/apache/incubator-mxnet/issues/8189#issuecomment-337834849
Hey, are you still running it on the jupyter notebook? if yes,
use this
jupyter nbconvert --to script
javelinjs commented on issue #8297: [scala] Make accuracy idependant of output
size (fix #8226)
URL: https://github.com/apache/incubator-mxnet/pull/8297#issuecomment-337832673
We can convert it back to Float when calling `EvalMetric.get`
zhenglaizhang commented on issue #8151: Amalgamation for android arm64 was
built successfully but failed to run in device
URL:
https://github.com/apache/incubator-mxnet/issues/8151#issuecomment-337827859
@larroy Hi,sorry for late response, thanks for the help, with the help from
other
zhenglaizhang commented on issue #4783: [v0.9.3] Amalgamation for Android broken
URL:
https://github.com/apache/incubator-mxnet/issues/4783#issuecomment-337826814
@novioleo yeah, thanks for the info, i succeeded in building the jni so.
gongqiang commented on issue #8189: Feed forward pass memory leaks (using htop)
URL:
https://github.com/apache/incubator-mxnet/issues/8189#issuecomment-337824849
I've got the same problem during training; when there is no 'time gap'
between each mod.forward, backward, update; memory usage
zhreshold commented on issue #8325: Fix typo in gluon l1loss docstring
URL: https://github.com/apache/incubator-mxnet/pull/8325#issuecomment-337814203
Please rebase to master to fix the CI
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