Add test code and documentation for Style/Aliascop

This PR is add test code and documentation for Style/Aliascop
Added documentation and test cases for cases
that force the use of alias_method at all times, such as below
def foo
  alias :ala :bala
end

def some_obj.foo
  alias :ala :bala
end

included do
  alias :ala :bala
end

Before submitting the PR make sure the following are checked:

[x] The PR relates to only one subject with a clear title and description in grammatically correct, complete sentences.
[x] Wrote good commit messages.
[-] Commit message starts with [Fix #issue-number] (if the related issue exists).
[x] Feature branch is up-to-date with master (if not - rebase it).
[x] Squashed related commits together.
[x] Added tests.
[x] Ran bundle exec rake default. It executes all tests and runs RuboCop on its own code.
[-] Added an entry (file) to the changelog folder named {change_type}_{change_description}.md if the new code introduces user-observable changes. See changelog entry format for details.

@ydah Ping :-)

I updated this PR. Thank you so much.

Thanks!


Xena always finds myAddress 0

Title. I don't understand why this happens (found where it happens, myAddress needs to be at least 0x200?).

Are you running the cheat while you're in game?

Ok nvm, it's my fault.


try trigger rector

@samsonasik It's not perfect yet, but Rector at least triggers and commits :+1:

No, in the past, it trigger and commit, but CI then stop, that require close and re-open to continue


Automated cherry pick of #9531

Cherry pick of #9531 on release-6.3.
/cc  @M-ZubairAhmed
NONE

Will try to auto merge this PR once all tests and checks are passing. This might take up to an hour.


Some color changes

Hi, again!
What do you think of these color changes:
  "workbench.colorCustomizations": {
    "editor.findMatchBackground": "#2b2b2b",
    "editor.lineHighlightBackground": "#383838",
    "editor.selectionBackground": "#2b2b2b",
    "editor.selectionHighlightBackground": "#2b2b2b",
    "editorCursor.foreground": "#ffffef"
  },

I think they match the colors in the Emacs theme a bit better.
And for me personally, I also think that the change makes text a bit easier to read.
Have a great week! :slightly_smiling_face:

Hi. Yes, of course, but for now we will apply all the upgrade suggestions only to the "No Bold" version. I think if we get more feedback over time, we will make it (No Bold) the main one.
"editorCursor.foreground": "#ffffef"

P.S. this color already has the same meaning in the theme.

@jonkri  you can try the update in v.0.3.4.

Seems to work like a charm, thank you! :slightly_smiling_face:


Add known-issues.sh script

Add a script which can be used to generate HTML describing which failures have been observed by the buildbot when running ZFS Test Suite.  Its purpose is to allow developers to quickly assess the prevalence of any observed failures.
Example output: http://build.zfsonlinux.org/known-issues.html
Potential future improvements include, but are not limited to:

Adding columns for first and last observed occurrences.
Ability to dynamically select the range in days.

Once we're happy with the output a link to this page should be added to the CONTRIBUTING.md guidelines.

Addressed review feedback and refreshed example page.  @gmelikov thanks for the sorting fix!


Weatermap slow when some graphs are broken

I am not sure if this is really a bug.
We recently updated from Cacti 0.8.8 to 1.2.24 and also updated the Weatermap plugin. Unfortunately, the performance of the new Cacti on the same hardware was suddenly very poor. The poller was constantly killed after 59 seconds because of max execution time. As soon as the plugin was deactivated, the poller was finished after 7 seconds.
We looked at the plugin code afterwards and found that the foreach in 1 takes forever. If we understand it correctly, this part creates error logs for every broken graph. Unfortunately, we have a few of these. The foreach is executed no matter what the log level in the plugin configuration is (quiet/chatty).
As soon as we commented out these 4 lines, the poller was finished in 43 seconds and all our weathermaps worked again.
As mentioned above, I'm not sure if this is really a bug. After all, the broken graphs are the main cause. But since it took us some time to figure this out, it might be helpful for others. Also, whether this log foreach should be run or not when the weatermap log level is quiet (not cacti log level), I am a bit unsure.

That should only be at the debug level.  Is that how you are running your Cacti.  There will of course be issues running at Debug, especially if your storage is constrained and you have a lot of poller items.

Maybe the request is to have a separate logging function for WeatherMap like we have for boost so that you don't wreck your server if you are debugging other things.  What do you think?


question concerning django_util

Hi,
I am trying to get the tests to work in a package build in OBS (openSUSE Build Servive) but am running out of ideas about whats going on.
The message I get is as follows:
ERROR: Failure: ImproperlyConfigured (Requested setting GOOGLE_OAUTH2_SCOPES, but settings are not configured. You must either define the environment variable DJANGO_SETTINGS_MODULE or call settings.configure() before accessing settings.)
....
Followed by a traceback
It appears that this is triggered by the following code snippets in contrib/django_util/init.py
    self.scopes = getattr(settings_instance, 'GOOGLE_OAUTH2_SCOPES',
                          GOOGLE_OAUTH2_DEFAULT_SCOPES)
    self.request_prefix = getattr(settings_instance,
                                  'GOOGLE_OAUTH2_REQUEST_ATTRIBUTE',
                                  GOOGLE_OAUTH2_REQUEST_ATTRIBUTE)

these are in the __init__method for the OAuth2Settings() class, which in turn is instantiated by
oauth2_settings = OAuth2Settings(django.conf.settings)
on line 352.
This happens with Django 1.9.9 and I take it that at some point an incompatibility was introduced in Django that triggers this issue.
If I go in and turn the instantiation into
oauth2_settings = OAuth2Settings(django.conf.settings.configure())
which should appease django, and apparently it does I end up with the following new message:
ERROR: Failure: ImproperlyConfigured (Must specify either GOOGLE_OAUTH2_CLIENT_SECRETS_JSON, or both GOOGLE_OAUTH2_CLIENT_ID and GOOGLE_OAUTH2_CLIENT_SECRET in settings.py)
Which appears to stem from _get_oauth2_client_id_and_secret() in contrib/django_util/init.py. So this would appear to indicate that during testing a "settings.py" file is used that does not have the proper configuration?
A couple of questions:
Is the change to the instantiation of OAuth2Settings() the proper approach?
How do the tests pick up a settings.py file to make Django happy?

And one more thing, even if I remove tests/contrib/django_util, i.e. do no django testing I get a failure that is django related:
======================================================================
ERROR: Failure: ImproperlyConfigured (Must specify either GOOGLE_OAUTH2_CLIENT_SECRETS_JSON, or both GOOGLE_OAUTH2_CLIENT_ID and GOOGLE_OAUTH2_CLIENT_SECRET in settings.py)
Traceback (most recent call last):
File "/usr/lib/python2.7/site-packages/nose/loader.py", line 418, in loadTestsFromName
addr.filename, addr.module)
File "/usr/lib/python2.7/site-packages/nose/importer.py", line 47, in importFromPath
return self.importFromDir(dir_path, fqname)
File "/usr/lib/python2.7/site-packages/nose/importer.py", line 94, in importFromDir
mod = load_module(part_fqname, fh, filename, desc)
File "/home/abuild/rpmbuild/BUILD/oauth2client-3.0.0/oauth2client/contrib/django_util/init.py", line 351, in
oauth2_settings = OAuth2Settings(django.conf.settings.configure())
File "/home/abuild/rpmbuild/BUILD/oauth2client-3.0.0/oauth2client/contrib/django_util/init.py", line 339, in init
_get_oauth2_client_id_and_secret(settings_instance)
Thus it appears that some other test is pulling in django related functionality. Unfortunately I was unable to pin point this, other ideas?
I am executing the tests with "nosetests"

@rjschwei you can't just run nosetests, you'll need to use tox instead - tox handles installing test dependencies and setting up appropriate environment variables for the tests.

Thanks


Move surface orientation calculation from tracking.singleaxis to new function

[x] Closes #1471
[x] I am familiar with the contributing guidelines
[x] Tests added
[x] Updates entries in docs/sphinx/source/reference for API changes.
[x] Adds description and name entries in the appropriate "what's new" file in docs/sphinx/source/whatsnew for all changes. Includes link to the GitHub Issue with :issue:`num` or this Pull Request with :pull:`num`. Includes contributor name and/or GitHub username (link with :ghuser:`user`).
[x] New code is fully documented. Includes numpydoc compliant docstrings, examples, and comments where necessary.
[x] Pull request is nearly complete and ready for detailed review.
[x] Maintainer: Appropriate GitHub Labels (including remote-data) and Milestone are assigned to the Pull Request and linked Issue.

As a follow up, should we remove https://github.com/pvlib/pvlib-python/blob/master/docs/tutorials/tracking.ipynb? Looks like a lot of work to bring that notebook up to date with the current implementation and the newer features.
I'd also recommend updating the PR title to reflect the use of new equations.

should we remove https://github.com/pvlib/pvlib-python/blob/master/docs/tutorials/tracking.ipynb?

Ugh, maybe.  I hate to delete useful info, even if the specific implementation doesn't match the current pvlib.  If there is effort to spend, I think I'd rather put it into a new User Guide page to replace that notebook instead of updating the notebook itself.  Or maybe improved gallery examples as suggested in https://github.com/pvlib/pvlib-python/pull/1077#pullrequestreview-504939228.

I'd also recommend updating the PR title to reflect the use of new equations.

Good point.  Wish I had done that before the title got used for the squashed commit message.  Oh well!

I agree that a user guide page and/or gallery examples are better than updating the notebook.
I didn't pay much attention to this PR since the title suggested that it was just a matter of moving code from one function to another :)

I didn't pay much attention to this PR since the title suggested that it was just a matter of moving code from one function to another :)

Yep, that's my bad.  Please feel free to point out anything you wish had been done prior to merging and I will happily rectify it!


Terrain not renderer

Currently when running the game it does not render the terrain and some objects like the character's arm
Software information
Rust, from steam
System information

GPU:  Intel(R) HD Graphics 620
Driver: Mesa 19.3.4
Proton 5.0-3

Log files

d3d11.log

Probably a driver bug, the game is known to work.

Okay, I'll check if I can get the apitracer.
I find it strange that for wined3d it works right.
thank you for your attention.

Actually, looking at your log again:
err:   Heap 0: 1024 MB allocated, 667 MB used, 1042 MB allocated (driver), 10768 MB budget (driver), 10768 MB total
err:   Heap 1: 1020 MB allocated, 908 MB used, 1021 MB allocated (driver), 1024 MB budget (driver), 1024 MB total
err:   DxvkMemoryAllocator: Memory allocation failed
err:   DxvkMemoryAllocator: Memory allocation failed
err:     Size:      3840
err:     Alignment: 16
err:     Mem flags: 0x1
err:     Mem types: 0x2

You're just running out of memory for some reason.
I didn't notice this the first time around since debug logging was enabled, please don't do this unless requested since it's mostly useless info.

Hello, I solved my memory allocation problem by forcing the allocation to ram when dxvk is unable to allocate to the supported and adequate memory. The game is running well and without BUGs so far.


Add SparkFun Qwiic Pocket board definition

Description:
Add ESP32-C6-MINI SparkFun Qwiic Pocket board definition
Related issue (if applicable): fixes #
Checklist:

[x] The pull request is done against the latest develop branch
[x] Only relevant files were touched
[x] Only one feature/fix was added per PR, more changes are allowed when changing boards.json
[x] I accept the CLA.

thx


iosevka-bin: 15.3.0 -> 15.3.1

Automatic update generated by nixpkgs-update tools. This update was made based on information from passthru.updateScript.
meta.description for iosevka-bin is: "Slender monospace sans-serif and slab-serif typeface inspired by Pragmata\nPro, M+ and PF DIN Mono, designed to be the ideal font for programming.\n"
meta.homepage for iosevka-bin is: "https://be5invis.github.io/Iosevka"
meta.changelog for iosevka-bin is: ""
Updates performed

Ran passthru.UpdateScript

To inspect upstream changes

Release on GitHub

Compare changes on GitHub

Impact

Checks done (click to expand)

built on NixOS
The tests defined in passthru.tests, if any, passed
found 15.3.1 with grep in /nix/store/ni4bbk0629hgdyxa74rs29j1k8ph24rp-iosevka-bin-15.3.1
found 15.3.1 in filename of file in /nix/store/ni4bbk0629hgdyxa74rs29j1k8ph24rp-iosevka-bin-15.3.1

Rebuild report (if merged into master) (click to expand)

2 total rebuild path(s)

1 package rebuild(s)

1 x86_64-linux rebuild(s)
1 x86_64-darwin rebuild(s)

First fifty rebuilds by attrpath
iosevka-bin

Instructions to test this update (click to expand)

Either download from Cachix:
nix-store -r /nix/store/ni4bbk0629hgdyxa74rs29j1k8ph24rp-iosevka-bin-15.3.1 \
  --option binary-caches 'https://cache.nixos.org/ https://nix-community.cachix.org/' \
  --option trusted-public-keys '
  nix-community.cachix.org-1:mB9FSh9qf2dCimDSUo8Zy7bkq5CX+/rkCWyvRCYg3Fs=
  cache.nixos.org-1:6NCHdD59X431o0gWypbMrAURkbJ16ZPMQFGspcDShjY=
  '

(The Cachix cache is only trusted for this store-path realization.)
For the Cachix download to work, your user must be in the trusted-users list or you can use sudo since root is effectively trusted.
Or, build yourself:
nix-build -A iosevka-bin https://github.com/r-ryantm/nixpkgs/archive/6094a08159b163bda529b304db4999347a19b044.tar.gz

After you've downloaded or built it, look at the files and if there are any, run the binaries:
ls -la /nix/store/ni4bbk0629hgdyxa74rs29j1k8ph24rp-iosevka-bin-15.3.1
ls -la /nix/store/ni4bbk0629hgdyxa74rs29j1k8ph24rp-iosevka-bin-15.3.1/bin

Pre-merge build results
We have automatically built all packages that will get rebuilt due to
this change.
This gives evidence on whether the upgrade will break dependent packages.
Note sometimes packages show up as failed to build independent of the
change, simply because they are already broken on the target branch.
Result of nixpkgs-review run on x86_64-linux 1

1 package built:

iosevka-bin

Maintainer pings
cc @cstrahan for testing.

This is already too old.


JetBrains: Automatic repo recognition is not working after removing the repo and restarting the IDE

Cody Version
v5.5.6-nightly
IDE Information
IntelliJ IDEA 2023.3.3 (Community Edition)
Build #IC-233.14015.106, built on January 25, 2024
Runtime version: 17.0.9+7-b1087.11 aarch64
VM: OpenJDK 64-Bit Server VM by JetBrains s.r.o.
macOS 14.3
GC: G1 Young Generation, G1 Old Generation
Memory: 2048M
Cores: 8
Metal Rendering is ON
Registry:
ide.experimental.ui=true
Non-Bundled Plugins:
idea.plugin.protoeditor (233.13135.65)
DevKit (233.14015.152)
gherkin (233.11799.165)
cucumber-java (233.11799.196)
PythonCore (233.14015.106)
com.sourcegraph.jetbrains (5.5.6-nightly)
Kotlin: 233.14015.106-IJ
Describe the bug
Platform: MacOS
Steps to reproduce:

Have Cody plugin installed in your JB IntelliJ IDEA editor
Login to Cody with Enterprise user
Open project with enabled Git VCS. This repository must be publicly available on GitHub.
Open to Cody tool window.
Click on repository button to open Context Selection dialog.
Remove the repo and re-launch the IDE

Observed behavior:
Repo will not be re-added automatically.
Expected behavior
Repo should be automatically recognised and added.
Additional context
Video link:
https://drive.google.com/file/d/1X_fpQV7BvpgflTZBhW-SatXVZONYvM5g/view?usp=drive_link

For now, this is basically working as intended. The repo will be implicitly selected and used for context, even if it is not displayed in that list. We are tracking displaying it in the list in #1322 .


Articles need to designate an image that is used by meta tags and eventually the mobile apps

Each article should have a designated image that is used for:

the 2buntu Android and Ubuntu Touch apps
integration into the API
countless "embedded data" protocols (like Open Graph)

This can be done in one of two ways and I'm open to discussion of the merits and disadvantages of each:

by adding a model field
by having the Markdown parser flag the first embedded image as the article's "canonical" image

Update: the expanded summaries are showing up now, but I'm getting tired of seeing the category images over and over again. I'm going to add an "image" field to the Article model that is optional. If specified, the image will be used in place of the category image. Otherwise the category image will be used as a fallback.
The new "image" field will be a foreign key to the Image model, so the image will have to be uploaded through the existing image upload button. There are a few bits of JavaScript that will need to be completed before this works. I'll reference this issue in commits.


Response Not Being Compressed

SUMMARY
The st2 large results seems to not be compressed when I requested for a .tar.gz via st2web.
STACKSTORM VERSION
Since commit b0dea78.
Steps to reproduce the problem

Create a st2 execution with a large result output.
Try downloading both variants (compressed and uncompressed) outputs.
Check sizes and they are the same.

Expected Results
The compressed blob size should be smaller.
Actual Results
The compressed size is the same.

On initial glance I thought it was here:
https://github.com/StackStorm/st2/blob/b9aec99fc49507d0b5b199caa2b1c27f7f5e13a6/st2api/st2api/controllers/v1/actionexecutions.py#L463-L469
however, it looks like this is being passed back properly based on the python docs here.

I am pretty sure this is due to us using an older st2web version.


[Discussion] 'viewYearFirst' prop for DatePicker component

I've modified the DatePicker component to meet a need for a project and wanted to contribute the changes back to the core project (:
The modification - an additional prop viewYearFirst={true} which opens the datepicker directly to the year display.
I just wanted to check in to see if the prop name was appropriate and whether or not any core developers could spot any gotchas before I roll this up into a PR.
Thanks!

Duplicate of #4949. The last intent PR was #6420.

However, I would rather put any effort into improving the DatePicker for the next branch.


depth2cov fails silently

Hi,
I am in the process of computing the different coverages for my assembly and I have succeeded for 3 out of the 4 sets: corrected low mapq, alt filtered low mapq and corrected high mapq. For the last set (alt filtered high mapq), it seems that depth2cov fails silently: the tools prints the length of each contig, then Coverting depth to cov and stops immediately. No .cov file has been output. I have recomputed the .depth file just to be sure it wasn't a corruption issue but same problem.
Any idea on how I could solve this?
Thanks,
Guillaume

Turns out the issue was in my command line, I had used the -d argument instead of the -o... Since -o is the output filename which does not exist yet, depth2cov would try to open it but fail and exit immediately.


Text and button clicks not working well for device, but working fine in emulator(AVD).

From<EMAIL_ADDRESS>on December 14, 2011 00:54:38
What steps will reproduce the problem? 1. Connect the device.
2. My application is having a button "OK" in the popup window.
3. execute solo.clickonbutton("OK"); What is the expected output? What do you see instead? "OK" button should have been clicked. And the server reponse should correctly show up. Click do happens but the server response does not correctly show up. However, this works fine in emulator(AVD), AVD has same app, same robotium code. Server response comes fine as expected when this robotium code [ solo.clickonbutton("OK") ]  is executed on AVD but does not come fine when it is executed on Device. What version of the product are you using? On what operating system? robotium-solo-3.0.jar on Windows 7. Please provide any additional information below. Its working fine in emulator(AVD)(2.2, 2.3)
But same is not working in device(2.2, 2.3)
Attachment: screenshot.png
Original issue: http://code.google.com/p/robotium/issues/detail?id=195

From<EMAIL_ADDRESS>on December 14, 2011 03:59:48
As long as clickOnButton() correctly clicks the button then it is not a Robotium issue. It seems as if your application under test has issues when running on real device. Maybe your device is not properly configured...
Status: Invalid


feat(parcom): init

Look at parcom/example.ts for a full Conventional Commit parser as sample code.
TODO:

[ ] Attempt to identify a simpler language for initial implementation.
Conventional Commits works fine, but requires a lot of plumbing. Ideally an init PR should be much smaller than this.

[ ] Readme entry

[ ] Add description to primary commit's body

[ ] Add missing tests

[ ] Add JSDoc examples to all functions

[x] Reconsider circular relationship with not() and ignore()

[ ] Export type Language

[ ] Add location support

[ ] Revise return types to avoid returning full strings every time

[ ] Add mechanism for custom error messages

[ ] Consider adding regex utils module before merging this

[ ] Add additional array methods to SequenceParser

[ ] Add support for named capture groups in regex() parser or a related parser

[ ] Test the utility parsers (ex. whitespace)

Hey thanks! This is still in draft - please don't feel the need to review. Of course you're free to look and weigh in if you choose. Just know that it may radically change out from under you.

Using this PR as a workplace to get the last bits of this organized. A new PR will be opened when it's ready, and this will be closed. The new branch is init-parser.

All checklist items completed in #107


Write a contributing guide

We need to make it very obvious how people can contribute to this repository (i.e. a CONTRIBUTING.md). This should be some kind of guide (in this repo) that (at least):

Links to the style guide
Explains the CI/testing philosophy
Makes it clear who the maintainers are

@arfon - do you think this is closed now? Or alternatively, we might close this and make a second issue along the lines of "improve contributing guide" if what's there is only partly sufficient?

@eteq yeah, I think this can be closed?

Makes it clear who the maintainers are

We should make this a priority ☝


Support "uuid" type

We should explicitly call out when an upstream table or a transformation has a uuid column.
For upstream, this would allow us to enable partitioning by that column.
For now, the expectation is:

Add new type of uuid to the schema for table designs.
When upstream shows a uuid column, then assign that type automatically.

This is now supported with something like this:
        {
            "name": "public_id",
            "sql_type": "varchar(36)",
            "type": "uuid",
            "expression": "\"public_id\"::varchar(36)",
            "source_sql_type": "uuid"
        },


schema second and third enum is consolidated

OAS3 spec:
parameters:
    - name: operation
      in: path
      required: true
      schema:
          type: string
          enum: [ add, update delete ]

parsed as:
public enum Operation {
    ADD("add"),
    UPDATE_DELETE("update delete");

should be parsed as
public enum Operation {
    ADD("add"),
    UPDATE("update"),
    DELETE("delete");

typo


Through 808 acquisition platform (netty long-term connection), after parsing, call the service through dubbo, do the business logic processing of the instruction, strange that the service exists, but it reports No provider available for the service

jdk version: 1.8
协议： GRPC
问题复现：
使用dubbo 3.1.6, grpc 1.54.1,protobuf version 3.21.7,启动服务端成功之后，启动consumer报错：
Failed to check the status of the service com.bh.dubbo.model.DubboDemoServiceGrpc___IDemoService. No provider available for the service com.bh.dubbo.model.DubboDemoServiceGrpc___IDemoService from the url consumer://<IP_ADDRESS>/com.bh.dubbo.model.DubboDemoServiceGrpc___IDemoService?application=dubbo-admin&background=false&dubbo=2.0.2&generic=true&interface=com.bh.dubbo.model.DubboDemoServiceGrpc___IDemoService&logger=slf4j&pid=26004&qos.port=32222&register.ip=<IP_ADDRESS>&release=3.1.6&side=consumer&sticky=false&timestamp=1691290215726&unloadClusterRelated=false to the consumer <IP_ADDRESS> use dubbo version 3.1.6

@1728581964
Try it in the latest 3.2.x version.

No news is good news. Please feel free to create a new issue if you have any question.


tests: added unit tests for optimistic concurrency

when using mutations
added integration tests

Closes #31

Replaced by #60


Don't use a separate schema for elementary tests (elementary__tests)

Request from users, the use case is if you don't use elementary tests or if you have permission issues and are limited in creating new schemas.
We encountered an issue with dbt that prevents that, in a nutshell - they create a cached schema at the run start and use it later as default. Only if we create a new schema (that is not in cache) we can actually access tables we create during the run.

Started working on a PR here but stopped working on it for the time being. If anyone would like to continue, this should be a good place to start.

https://github.com/elementary-data/dbt-data-reliability/pull/90

Merged a PR with a fix to master, will be included in the next release :)


Update scrabble_score_tests.erl

Trim whitespace and add ?checkScore macro.
Apparently I'm on a macro kick lately. The extra spaces were bugging me, too.

Nice!

Thanks.
On Sunday, 14 February 2016, Katrina Owen<EMAIL_ADDRESS>wrote:

Merged #89 https://github.com/exercism/xerlang/pull/89.
—
Reply to this email directly or view it on GitHub
https://github.com/exercism/xerlang/pull/89#event-549695543.


Why the result is different for two time of run?

The result for where differ from 0.71547322170763572 to 0.68685429283933019

Loading from original dataset
Loading data from data/train_tok.jsonl
Loading data from data/train_tok.tables.jsonl
Loading data from data/dev_tok.jsonl
Loading data from data/dev_tok.tables.jsonl
Loading data from data/test_tok.jsonl
Loading data from data/test_tok.tables.jsonl
Loading word embedding from glove/glove.42B.300d.txt
Using fixed embedding
Using column attention on aggregator predicting
Using column attention on selection predicting
Using column attention on where predicting
Init dev acc_qm: 0.0
  breakdown on (agg, sel, where): [ 0.15223845  0.17230733  0.        ]
Epoch 1 @ 2017-12-08 08:03:19.389506
 Loss = 3.21229583377
 Train acc_qm: 0.230715996806
   breakdown result: [ 0.90048798  0.82761068  0.29097684]
 Dev acc_qm: 0.21778886118
   breakdown result: [ 0.8953806   0.8153426   0.28215176]
 Best val acc = (0.89538059612872578, 0.81534259589122438, 0.28215176344852155), on epoch (1, 1, 1) individually
Epoch 2 @ 2017-12-08 08:15:41.960671
 Loss = 1.98041075968
 Train acc_qm: 0.345648123503
   breakdown result: [ 0.90646793  0.87788129  0.41357466]
 Dev acc_qm: 0.320270751692
   breakdown result: [ 0.89692436  0.86545541  0.3927087 ]
 Best val acc = (0.89692435577722363, 0.8654554090963068, 0.3927087044294027), on epoch (2, 2, 2) individually
Epoch 3 @ 2017-12-08 08:27:35.860856
 Loss = 1.66030278297
 Train acc_qm: 0.416041167598
   breakdown result: [ 0.9118623   0.90080738  0.48882974]
 Dev acc_qm: 0.379646122788
   breakdown result: [ 0.89739936  0.88101176  0.45683411]
 Best val acc = (0.89739935874599219, 0.88101175632347706, 0.45683410521315759), on epoch (3, 3, 3) individually
Epoch 4 @ 2017-12-08 08:40:40.119478
 Loss = 1.44785996712
 Train acc_qm: 0.472486913317
   breakdown result: [ 0.91402715  0.91613876  0.54745808]
 Dev acc_qm: 0.425127657048
   breakdown result: [ 0.89229308  0.88849305  0.5122907 ]
 Best val acc = (0.89739935874599219, 0.88849305308158177, 0.51229070181688641), on epoch (3, 4, 4) individually
Epoch 5 @ 2017-12-08 08:53:33.571671
 Loss = 1.3011851821
 Train acc_qm: 0.506343713956
   breakdown result: [ 0.91754059  0.92669683  0.57913229]
 Dev acc_qm: 0.443534022088
   breakdown result: [ 0.89502434  0.89359933  0.53057832]
 Best val acc = (0.89739935874599219, 0.89359933499584376, 0.5305783161144757), on epoch (3, 5, 5) individually
Epoch 6 @ 2017-12-08 09:05:54.464445
 Loss = 1.18214633257
 Train acc_qm: 0.54511578387
   breakdown result: [ 0.92368024  0.93461095  0.61707036]
 Dev acc_qm: 0.463484146776
   breakdown result: [ 0.88968056  0.89858687  0.55812849]
 Best val acc = (0.89739935874599219, 0.8985868661679135, 0.55812848830305184), on epoch (3, 6, 6) individually
Epoch 7 @ 2017-12-08 09:18:16.035162
 Loss = 1.0879037351
 Train acc_qm: 0.571821488777
   breakdown result: [ 0.92877296  0.94193949  0.64059977]
 Dev acc_qm: 0.479634247714
   breakdown result: [ 0.89074932  0.89929937  0.57119107]
 Best val acc = (0.89739935874599219, 0.89929937062106635, 0.57119106994418711), on epoch (3, 7, 7) individually
Epoch 8 @ 2017-12-08 09:30:39.136529
 Loss = 1.01297582561
 Train acc_qm: 0.596699494277
   breakdown result: [ 0.93471742  0.94818561  0.66276284]
 Dev acc_qm: 0.500059375371
   breakdown result: [ 0.89264933  0.90214939  0.59553497]
 Best val acc = (0.89739935874599219, 0.90214938843367776, 0.59553497209357553), on epoch (3, 8, 8) individually
Epoch 9 @ 2017-12-08 09:43:01.621578
 Loss = 0.938494441101
 Train acc_qm: 0.625853961494
   breakdown result: [ 0.94295094  0.95192973  0.68769408]
 Dev acc_qm: 0.506709416934
   breakdown result: [ 0.88576179  0.90369315  0.6077663 ]
 Best val acc = (0.89739935874599219, 0.90369314808217549, 0.60776629853936592), on epoch (3, 9, 9) individually
Epoch 10 @ 2017-12-08 09:55:32.724853
 Loss = 0.877387988135
 Train acc_qm: 0.641575725313
   breakdown result: [ 0.94958744  0.95613521  0.69809245]
 Dev acc_qm: 0.508609428809
   breakdown result: [ 0.88219926  0.9035744   0.61204133]
 Best val acc = (0.89739935874599219, 0.90369314808217549, 0.61204132525828281), on epoch (3, 9, 10) individually
Epoch 11 @ 2017-12-08 10:07:45.627209
 Loss = 0.821675243159
 Train acc_qm: 0.671990062994
   breakdown result: [ 0.95342028  0.9589921   0.7274776 ]
 Dev acc_qm: 0.520247001544
   breakdown result: [ 0.87733048  0.9040494   0.62961644]
 Best val acc = (0.89739935874599219, 0.90404940030875192, 0.62961643510271936), on epoch (3, 11, 11) individually
Epoch 12 @ 2017-12-08 10:20:13.346996
 Loss = 0.771607955189
 Train acc_qm: 0.692644840742
   breakdown result: [ 0.96026972  0.96206193  0.74263153]
 Dev acc_qm: 0.529747060919
   breakdown result: [ 0.88409928  0.90321815  0.63638523]
 Best val acc = (0.89739935874599219, 0.90404940030875192, 0.63638522740767134), on epoch (3, 11, 12) individually
Epoch 13 @ 2017-12-08 10:32:46.019931
 Loss = 0.728819368
 Train acc_qm: 0.708029456126
   breakdown result: [ 0.96605448  0.96451069  0.75313637]
 Dev acc_qm: 0.527847049044
   breakdown result: [ 0.8792305   0.9045244   0.63971025]
 Best val acc = (0.89739935874599219, 0.90452440327752048, 0.6397102481890512), on epoch (3, 13, 13) individually
Epoch 14 @ 2017-12-08 10:44:22.739046
 Loss = 0.696527105084
 Train acc_qm: 0.718143909147
   breakdown result: [ 0.96667554  0.96676426  0.76223938]
 Dev acc_qm: 0.534734592091
   breakdown result: [ 0.87388671  0.90535566  0.64861655]
 Best val acc = (0.89739935874599219, 0.90535565847286548, 0.64861655385346162), on epoch (3, 14, 14) individually
Epoch 15 @ 2017-12-08 10:56:12.697323
 Loss = 0.662762617242
 Train acc_qm: 0.731558867891
   breakdown result: [ 0.9729394   0.96846775  0.77077455]
 Dev acc_qm: 0.536040850255
   breakdown result: [ 0.87804299  0.90464315  0.65158532]
 Best val acc = (0.89739935874599219, 0.90535565847286548, 0.65158532240826506), on epoch (3, 14, 15) individually
Epoch 16 @ 2017-12-08 11:08:16.999158
 Loss = 0.629406735292
 Train acc_qm: 0.740431195102
   breakdown result: [ 0.97375566  0.97020673  0.77859995]
 Dev acc_qm: 0.542928393302
   breakdown result: [ 0.880418    0.90559316  0.65704786]
 Best val acc = (0.89739935874599219, 0.90559315995724976, 0.65704785654910347), on epoch (3, 16, 16) individually
Epoch 17 @ 2017-12-08 11:20:25.210987
 Loss = 0.608731487824
 Train acc_qm: 0.759577677225
   breakdown result: [ 0.97895484  0.9724603   0.7928489 ]
 Dev acc_qm: 0.551597197482
   breakdown result: [ 0.87163045  0.90951193  0.67117919]
 Best val acc = (0.89739935874599219, 0.90951193444959033, 0.67117919486996791), on epoch (3, 17, 17) individually
Epoch 18 @ 2017-12-08 11:32:27.597155
 Loss = 0.581795603114
 Train acc_qm: 0.767474048443
   breakdown result: [ 0.98214888  0.97375566  0.79829651]
 Dev acc_qm: 0.546490915568
   breakdown result: [ 0.86901793  0.90974944  0.66844793]
 Best val acc = (0.89739935874599219, 0.90974943593397462, 0.67117919486996791), on epoch (3, 18, 17) individually
Epoch 19 @ 2017-12-08 11:44:33.055308
 Loss = 0.561099567629
 Train acc_qm: 0.772513530299
   breakdown result: [ 0.98365717  0.97473161  0.8018277 ]
 Dev acc_qm: 0.544472152951
   breakdown result: [ 0.86925543  0.90927443  0.66892293]
 Best val acc = (0.89739935874599219, 0.90974943593397462, 0.67117919486996791), on epoch (3, 18, 17) individually
Epoch 20 @ 2017-12-08 11:56:53.542262
 Loss = 0.537216782594
 Train acc_qm: 0.781687516636
   breakdown result: [ 0.9847396   0.97572531  0.80906752]
 Dev acc_qm: 0.549103431896
   breakdown result: [ 0.87056169  0.90998694  0.67034794]
 Best val acc = (0.89739935874599219, 0.9099869374183589, 0.67117919486996791), on epoch (3, 20, 17) individually
Epoch 21 @ 2017-12-08 12:09:06.669041
 Loss = 0.523966215621
 Train acc_qm: 0.78832401739
   breakdown result: [ 0.98654955  0.97678999  0.81373436]
 Dev acc_qm: 0.550884693029
   breakdown result: [ 0.87400546  0.90713692  0.67082294]
 Best val acc = (0.89739935874599219, 0.9099869374183589, 0.67117919486996791), on epoch (3, 20, 17) individually
Epoch 22 @ 2017-12-08 12:21:12.533871
 Loss = 0.504479485911
 Train acc_qm: 0.797639960962
   breakdown result: [ 0.98534292  0.97796114  0.82391979]
 Dev acc_qm: 0.564303526897
   breakdown result: [ 0.87578672  0.90915568  0.68044175]
 Best val acc = (0.89739935874599219, 0.9099869374183589, 0.68044175276095475), on epoch (3, 20, 22) individually
Epoch 23 @ 2017-12-08 12:33:31.812659
 Loss = 0.493005867633
 Train acc_qm: 0.806973649188
   breakdown result: [ 0.98812883  0.97872416  0.82989974]
 Dev acc_qm: 0.565609785061
   breakdown result: [ 0.87863674  0.90808693  0.68127301]
 Best val acc = (0.89739935874599219, 0.9099869374183589, 0.68127300795629975), on epoch (3, 20, 23) individually
Epoch 24 @ 2017-12-08 12:46:07.735437
 Loss = 0.481936032432
 Train acc_qm: 0.807683435365
   breakdown result: [ 0.98958389  0.97961139  0.82894153]
 Dev acc_qm: 0.559315995725
   breakdown result: [ 0.86842418  0.90868068  0.68412303]
 Best val acc = (0.89739935874599219, 0.9099869374183589, 0.68412302576891104), on epoch (3, 20, 24) individually
Epoch 25 @ 2017-12-08 12:58:14.331356
 Loss = 0.466980496212
 Train acc_qm: 0.813432703398
   breakdown result: [ 0.99118091  0.97991305  0.83334221]
 Dev acc_qm: 0.561097256858
   breakdown result: [ 0.8715117   0.91010569  0.68400428]
 Best val acc = (0.89739935874599219, 0.91010568816055104, 0.68412302576891104), on epoch (3, 25, 24) individually
Epoch 26 @ 2017-12-08 13:10:31.517826
 Loss = 0.455328330514
 Train acc_qm: 0.823884304853
   breakdown result: [ 0.99048887  0.98138586  0.84372283]
 Dev acc_qm: 0.561334758342
   breakdown result: [ 0.86878043  0.90844318  0.68697304]
 Best val acc = (0.89739935874599219, 0.91010568816055104, 0.68697304358152234), on epoch (3, 25, 26) individually
Epoch 27 @ 2017-12-08 13:22:52.371494
 Loss = 0.44173109771
 Train acc_qm: 0.826528258362
   breakdown result: [ 0.99213912  0.98143909  0.8444681 ]
 Dev acc_qm: 0.567866049163
   breakdown result: [ 0.87733048  0.90725567  0.68590429]
 Best val acc = (0.89739935874599219, 0.91010568816055104, 0.68697304358152234), on epoch (3, 25, 26) individually
Epoch 28 @ 2017-12-08 13:34:56.189310
 Loss = 0.440050315135
 Train acc_qm: 0.83474403336
   breakdown result: [ 0.99286665  0.9827522   0.85183214]
 Dev acc_qm: 0.57202232514
   breakdown result: [ 0.87578672  0.90974944  0.68958556]
 Best val acc = (0.89739935874599219, 0.91010568816055104, 0.68958555990974946), on epoch (3, 25, 28) individually
Epoch 29 @ 2017-12-08 13:47:00.479572
 Loss = 0.423800450912
 Train acc_qm: 0.835861946589
   breakdown result: [ 0.9925295   0.9829119   0.85307426]
 Dev acc_qm: 0.577009856312
   breakdown result: [ 0.88172426  0.90986819  0.69231683]
 Best val acc = (0.89739935874599219, 0.91010568816055104, 0.69231682698016861), on epoch (3, 25, 29) individually
Epoch 30 @ 2017-12-08 13:58:31.308974
 Loss = 0.41658635386
 Train acc_qm: 0.838381687517
   breakdown result: [ 0.99405554  0.98257475  0.85456481]
 Dev acc_qm: 0.56608478803
   breakdown result: [ 0.87626173  0.90642442  0.68721055]
 Best val acc = (0.89739935874599219, 0.91010568816055104, 0.69231682698016861), on epoch (3, 25, 29) individually
Epoch 31 @ 2017-12-08 14:10:27.808481
 Loss = 0.408999198766
 Train acc_qm: 0.839446366782
   breakdown result: [ 0.99417975  0.98429598  0.85389052]
 Dev acc_qm: 0.569528559553
   breakdown result: [ 0.87602423  0.90796817  0.69112932]
 Best val acc = (0.89739935874599219, 0.91010568816055104, 0.69231682698016861), on epoch (3, 25, 29) individually
Epoch 32 @ 2017-12-08 14:22:08.125598
 Loss = 0.402487462721
 Train acc_qm: 0.84657971786
   breakdown result: [ 0.99597196  0.98397658  0.86010114]
 Dev acc_qm: 0.571191069944
   breakdown result: [ 0.87578672  0.90903693  0.69374184]
 Best val acc = (0.89739935874599219, 0.91010568816055104, 0.69374183588647431), on epoch (3, 25, 32) individually
Epoch 33 @ 2017-12-08 14:34:35.443946
 Loss = 0.396298603021
 Train acc_qm: 0.849063969479
   breakdown result: [ 0.99536865  0.98466862  0.86233697]
 Dev acc_qm: 0.573209832561
   breakdown result: [ 0.87329296  0.9120057   0.69504809]
 Best val acc = (0.89739935874599219, 0.9120057000356252, 0.69504809405058776), on epoch (3, 33, 33) individually
Epoch 34 @ 2017-12-08 14:46:36.713208
 Loss = 0.38937834563
 Train acc_qm: 0.846863632331
   breakdown result: [ 0.99386035  0.98461538  0.86157395]
 Dev acc_qm: 0.567509796936
   breakdown result: [ 0.86996794  0.90689942  0.69243558]
 Best val acc = (0.89739935874599219, 0.9120057000356252, 0.69504809405058776), on epoch (3, 33, 33) individually
Epoch 35 @ 2017-12-08 14:58:31.813504
 Loss = 0.386230991929
 Train acc_qm: 0.855700470233
   breakdown result: [ 0.99609618  0.98562683  0.86826369]
 Dev acc_qm: 0.575941099632
   breakdown result: [ 0.87234295  0.91034319  0.69730436]
 Best val acc = (0.89739935874599219, 0.9120057000356252, 0.69730435815223846), on epoch (3, 33, 35) individually
Epoch 36 @ 2017-12-08 15:10:56.130682
 Loss = 0.373649094337
 Train acc_qm: 0.858912252684
   breakdown result: [ 0.99496052  0.98608819  0.87195457]
 Dev acc_qm: 0.572378577366
   breakdown result: [ 0.87448047  0.91022444  0.69160432]
 Best val acc = (0.89739935874599219, 0.9120057000356252, 0.69730435815223846), on epoch (3, 33, 35) individually
Epoch 37 @ 2017-12-08 15:22:38.885352
 Loss = 0.370346855161
 Train acc_qm: 0.86150297223
   breakdown result: [ 0.99441043  0.98608819  0.87520185]
 Dev acc_qm: 0.574159838499
   breakdown result: [ 0.86889918  0.9122432   0.69908562]
 Best val acc = (0.89739935874599219, 0.91224320152000948, 0.69908561928512059), on epoch (3, 37, 37) individually
Epoch 38 @ 2017-12-08 15:34:43.598205
 Loss = 0.369440284557
 Train acc_qm: 0.8591784225
   breakdown result: [ 0.99595422  0.98708189  0.87064147]
 Dev acc_qm: 0.571666072913
   breakdown result: [ 0.87293671  0.91069944  0.6962356 ]
 Best val acc = (0.89739935874599219, 0.91224320152000948, 0.69908561928512059), on epoch (3, 37, 37) individually
Epoch 39 @ 2017-12-08 15:46:55.459057
 Loss = 0.358522504788
 Train acc_qm: 0.865353562239
   breakdown result: [ 0.99529767  0.98686896  0.87777482]
 Dev acc_qm: 0.575228595179
   breakdown result: [ 0.87483672  0.91295571  0.69326683]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.69908561928512059), on epoch (3, 39, 37) individually
Epoch 40 @ 2017-12-08 15:58:40.005033
 Loss = 0.354712518596
 Train acc_qm: 0.866329518233
   breakdown result: [ 0.99700115  0.98715287  0.87681661]
 Dev acc_qm: 0.5688160551
   breakdown result: [ 0.8712742   0.91010569  0.69492934]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.69908561928512059), on epoch (3, 39, 37) individually
Epoch 41 @ 2017-12-08 16:10:49.632433
 Loss = 0.353093212864
 Train acc_qm: 0.868671812616
   breakdown result: [ 0.99710762  0.98747227  0.87898146]
 Dev acc_qm: 0.575941099632
   breakdown result: [ 0.87163045  0.91164945  0.69825436]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.69908561928512059), on epoch (3, 39, 37) individually
Epoch 42 @ 2017-12-08 16:23:04.415265
 Loss = 0.344996594372
 Train acc_qm: 0.871138319581
   breakdown result: [ 0.99687694  0.98763198  0.88153669]
 Dev acc_qm: 0.570241064007
   breakdown result: [ 0.8715117   0.90737442  0.6959981 ]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.69908561928512059), on epoch (3, 39, 37) individually
Epoch 43 @ 2017-12-08 16:35:23.385493
 Loss = 0.341099480926
 Train acc_qm: 0.878058734806
   breakdown result: [ 0.99625588  0.98775619  0.88914914]
 Dev acc_qm: 0.579147369671
   breakdown result: [ 0.87626173  0.90618691  0.70383565]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.70383564897280604), on epoch (3, 39, 43) individually
Epoch 44 @ 2017-12-08 16:47:38.111310
 Loss = 0.33810743823
 Train acc_qm: 0.879229881998
   breakdown result: [ 0.99636235  0.98743678  0.89058646]
 Dev acc_qm: 0.583184894906
   breakdown result: [ 0.87578672  0.90832443  0.70514191]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.7051419071369196), on epoch (3, 39, 44) individually
Epoch 45 @ 2017-12-08 16:59:33.278019
 Loss = 0.334115387462
 Train acc_qm: 0.879904178866
   breakdown result: [ 0.99753349  0.98867891  0.8889717 ]
 Dev acc_qm: 0.574872342952
   breakdown result: [ 0.87673673  0.90927443  0.69789811]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.7051419071369196), on epoch (3, 39, 44) individually
Epoch 46 @ 2017-12-08 17:11:26.489369
 Loss = 0.330075835962
 Train acc_qm: 0.88300949339
   breakdown result: [ 0.99684145  0.9888741   0.89289327]
 Dev acc_qm: 0.581522384515
   breakdown result: [ 0.87448047  0.91022444  0.70490441]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.7051419071369196), on epoch (3, 39, 44) individually
Epoch 47 @ 2017-12-08 17:23:38.008012
 Loss = 0.332440037514
 Train acc_qm: 0.884801703487
   breakdown result: [ 0.99574128  0.98914027  0.89553722]
 Dev acc_qm: 0.580097375609
   breakdown result: [ 0.87590547  0.91117444  0.70110438]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.7051419071369196), on epoch (3, 39, 44) individually
Epoch 48 @ 2017-12-08 17:36:12.171259
 Loss = 0.322234305718
 Train acc_qm: 0.879123414072
   breakdown result: [ 0.99765771  0.98896282  0.88808446]
 Dev acc_qm: 0.577841111507
   breakdown result: [ 0.87483672  0.91093694  0.69766061]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.7051419071369196), on epoch (3, 39, 44) individually
Epoch 49 @ 2017-12-08 17:48:35.024018
 Loss = 0.315666351034
 Train acc_qm: 0.884553278325
   breakdown result: [ 0.99737379  0.98947742  0.89342561]
 Dev acc_qm: 0.573803586272
   breakdown result: [ 0.86901793  0.91081819  0.69956062]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.7051419071369196), on epoch (3, 39, 44) individually
Epoch 50 @ 2017-12-08 18:00:55.697502
 Loss = 0.31650647863
 Train acc_qm: 0.888332889717
   breakdown result: [ 0.99714311  0.98965487  0.8975956 ]
 Dev acc_qm: 0.578316114476
   breakdown result: [ 0.87056169  0.90868068  0.70727942]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.70727942049637815), on epoch (3, 39, 50) individually
Epoch 51 @ 2017-12-08 18:13:21.076750
 Loss = 0.311738086015
 Train acc_qm: 0.888137698518
   breakdown result: [ 0.99808358  0.98958389  0.89670837]
 Dev acc_qm: 0.58069112932
   breakdown result: [ 0.87400546  0.91105569  0.70502316]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.70727942049637815), on epoch (3, 39, 50) individually
Epoch 52 @ 2017-12-08 18:25:41.275722
 Loss = 0.307366009412
 Train acc_qm: 0.894099902404
   breakdown result: [ 0.99783515  0.98928223  0.90263508]
 Dev acc_qm: 0.580334877093
   breakdown result: [ 0.87163045  0.91164945  0.70502316]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.70727942049637815), on epoch (3, 39, 50) individually
Epoch 53 @ 2017-12-08 18:37:34.377615
 Loss = 0.307626993312
 Train acc_qm: 0.893106201757
   breakdown result: [ 0.99794162  0.98953065  0.90139296]
 Dev acc_qm: 0.58342239639
   breakdown result: [ 0.87543047  0.9115307   0.70573566]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.70727942049637815), on epoch (3, 39, 50) individually
Epoch 54 @ 2017-12-08 18:49:24.562589
 Loss = 0.307716887565
 Train acc_qm: 0.897347174164
   breakdown result: [ 0.99778192  0.99034691  0.90526129]
 Dev acc_qm: 0.585441159007
   breakdown result: [ 0.87590547  0.90963069  0.71036694]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71036693979337373), on epoch (3, 39, 54) individually
Epoch 55 @ 2017-12-08 19:01:04.844705
 Loss = 0.304543448529
 Train acc_qm: 0.895750155266
   breakdown result: [ 0.99746251  0.98961938  0.90469346]
 Dev acc_qm: 0.588766179789
   breakdown result: [ 0.87958675  0.91034319  0.70787317]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71036693979337373), on epoch (3, 39, 54) individually
Epoch 56 @ 2017-12-08 19:12:54.145455
 Loss = 0.304357943226
 Train acc_qm: 0.897915003105
   breakdown result: [ 0.99787064  0.9902227   0.9057404 ]
 Dev acc_qm: 0.581641135257
   breakdown result: [ 0.87079919  0.91022444  0.70644817]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71036693979337373), on epoch (3, 39, 54) individually
Epoch 57 @ 2017-12-08 19:24:05.294193
 Loss = 0.296769426261
 Train acc_qm: 0.89993789371
   breakdown result: [ 0.99783515  0.99032916  0.90813592]
 Dev acc_qm: 0.581403633773
   breakdown result: [ 0.87697423  0.90808693  0.70537941]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71036693979337373), on epoch (3, 39, 54) individually
Epoch 58 @ 2017-12-08 19:35:56.115887
 Loss = 0.297517987546
 Train acc_qm: 0.901215508828
   breakdown result: [ 0.99755124  0.99079052  0.90902316]
 Dev acc_qm: 0.586628666429
   breakdown result: [ 0.87459922  0.90868068  0.70941693]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71036693979337373), on epoch (3, 39, 54) individually
Epoch 59 @ 2017-12-08 19:48:24.677635
 Loss = 0.29532235601
 Train acc_qm: 0.899032916334
   breakdown result: [ 0.99614941  0.99119865  0.90790524]
 Dev acc_qm: 0.584016150101
   breakdown result: [ 0.8806555   0.90986819  0.70252939]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71036693979337373), on epoch (3, 39, 54) individually
Epoch 60 @ 2017-12-08 20:00:04.917135
 Loss = 0.291019300689
 Train acc_qm: 0.901943039659
   breakdown result: [ 0.99721409  0.99050661  0.91024754]
 Dev acc_qm: 0.584728654554
   breakdown result: [ 0.87305546  0.90939318  0.71107944]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71107944424652658), on epoch (3, 39, 60) individually
Epoch 61 @ 2017-12-08 20:11:41.684163
 Loss = 0.288649563282
 Train acc_qm: 0.896353473516
   breakdown result: [ 0.99845622  0.99057759  0.90350457]
 Dev acc_qm: 0.576297351858
   breakdown result: [ 0.87376796  0.90891818  0.69991687]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71107944424652658), on epoch (3, 39, 60) individually
Epoch 62 @ 2017-12-08 20:23:40.310859
 Loss = 0.285716223965
 Train acc_qm: 0.90298997427
   breakdown result: [ 0.99772868  0.99087925  0.91062018]
 Dev acc_qm: 0.584609903812
   breakdown result: [ 0.8715117   0.9115307   0.71024819]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71107944424652658), on epoch (3, 39, 60) individually
Epoch 63 @ 2017-12-08 20:35:34.298180
 Loss = 0.281283839237
 Train acc_qm: 0.905314524
   breakdown result: [ 0.99744477  0.99112767  0.9132109 ]
 Dev acc_qm: 0.59209120057
   breakdown result: [ 0.87851799  0.9120057   0.71309821]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71309820686379288), on epoch (3, 39, 63) individually
Epoch 64 @ 2017-12-08 20:48:00.613130
 Loss = 0.28673263988
 Train acc_qm: 0.900292786798
   breakdown result: [ 0.9981723   0.99134061  0.90712448]
 Dev acc_qm: 0.577128607054
   breakdown result: [ 0.87483672  0.90761192  0.69991687]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71309820686379288), on epoch (3, 39, 63) individually
Epoch 65 @ 2017-12-08 21:00:32.179205
 Loss = 0.278939199775
 Train acc_qm: 0.909892644841
   breakdown result: [ 0.99826102  0.99155354  0.91656463]
 Dev acc_qm: 0.584847405296
   breakdown result: [ 0.87875549  0.90630566  0.70811068]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71309820686379288), on epoch (3, 39, 63) individually
Epoch 66 @ 2017-12-08 21:12:48.931260
 Loss = 0.272643052077
 Train acc_qm: 0.910176559312
   breakdown result: [ 0.99776417  0.99162452  0.91736314]
 Dev acc_qm: 0.585916161976
   breakdown result: [ 0.87626173  0.90749317  0.70727942]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71309820686379288), on epoch (3, 39, 63) individually
Epoch 67 @ 2017-12-08 21:24:55.392340
 Loss = 0.273700154891
 Train acc_qm: 0.90884571023
   breakdown result: [ 0.99744477  0.99180197  0.91599681]
 Dev acc_qm: 0.577722360765
   breakdown result: [ 0.86759292  0.90820568  0.70573566]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71309820686379288), on epoch (3, 39, 63) individually
Epoch 68 @ 2017-12-08 21:37:04.771344
 Loss = 0.27588878568
 Train acc_qm: 0.909999112767
   breakdown result: [ 0.99850945  0.99167776  0.91672434]
 Dev acc_qm: 0.584966156038
   breakdown result: [ 0.86984919  0.90856193  0.7114357 ]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71309820686379288), on epoch (3, 39, 63) individually
Epoch 69 @ 2017-12-08 21:49:13.827454
 Loss = 0.268777689378
 Train acc_qm: 0.911862301482
   breakdown result: [ 0.9970189   0.99187295  0.91951025]
 Dev acc_qm: 0.589953687211
   breakdown result: [ 0.8801805   0.9112932   0.70858568]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71309820686379288), on epoch (3, 39, 63) individually
Epoch 70 @ 2017-12-08 22:01:22.957638
 Loss = 0.273635487654
 Train acc_qm: 0.908792476267
   breakdown result: [ 0.99772868  0.99178423  0.91592583]
 Dev acc_qm: 0.586747417171
   breakdown result: [ 0.87329296  0.91117444  0.70977319]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71309820686379288), on epoch (3, 39, 63) individually
Epoch 71 @ 2017-12-08 22:13:50.352358
 Loss = 0.27024436027
 Train acc_qm: 0.908029456126
   breakdown result: [ 0.99797711  0.9918552   0.9146837 ]
 Dev acc_qm: 0.578909868187
   breakdown result: [ 0.87163045  0.90903693  0.70252939]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71309820686379288), on epoch (3, 39, 63) individually
Epoch 72 @ 2017-12-08 22:25:56.260660
 Loss = 0.263102421188
 Train acc_qm: 0.913831958123
   breakdown result: [ 0.99847396  0.99199716  0.92034425]
 Dev acc_qm: 0.581284883031
   breakdown result: [ 0.86913668  0.9115307   0.70894193]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71309820686379288), on epoch (3, 39, 63) individually
Epoch 73 @ 2017-12-08 22:38:07.724272
 Loss = 0.263063042113
 Train acc_qm: 0.914293319138
   breakdown result: [ 0.99792388  0.99160678  0.92160412]
 Dev acc_qm: 0.581166132288
   breakdown result: [ 0.86676167  0.90974944  0.70822943]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71309820686379288), on epoch (3, 39, 63) individually
Epoch 74 @ 2017-12-08 22:50:40.978064
 Loss = 0.267440260712
 Train acc_qm: 0.911702599592
   breakdown result: [ 0.99772868  0.99219235  0.91860527]
 Dev acc_qm: 0.577009856312
   breakdown result: [ 0.86676167  0.90678067  0.70573566]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71309820686379288), on epoch (3, 39, 63) individually
Epoch 75 @ 2017-12-08 23:02:51.079381
 Loss = 0.260674261419
 Train acc_qm: 0.917505101588
   breakdown result: [ 0.99831426  0.99277793  0.92348505]
 Dev acc_qm: 0.581284883031
   breakdown result: [ 0.86604916  0.9127182   0.70906068]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71309820686379288), on epoch (3, 39, 63) individually
Epoch 76 @ 2017-12-08 23:15:11.965736
 Loss = 0.260468381899
 Train acc_qm: 0.910921834797
   breakdown result: [ 0.99788839  0.99240529  0.91752285]
 Dev acc_qm: 0.57855361596
   breakdown result: [ 0.86878043  0.91081819  0.70585441]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71309820686379288), on epoch (3, 39, 63) individually
Epoch 77 @ 2017-12-08 23:27:24.740179
 Loss = 0.255772571216
 Train acc_qm: 0.919705438737
   breakdown result: [ 0.99856268  0.99290214  0.92536598]
 Dev acc_qm: 0.581759885999
   breakdown result: [ 0.86961169  0.91022444  0.70775442]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71309820686379288), on epoch (3, 39, 63) individually
Epoch 78 @ 2017-12-08 23:39:24.252531
 Loss = 0.257981396281
 Train acc_qm: 0.917132463845
   breakdown result: [ 0.99783515  0.99258273  0.92380445]
 Dev acc_qm: 0.582472390452
   breakdown result: [ 0.87281796  0.91010569  0.70431065]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71309820686379288), on epoch (3, 39, 63) individually
Epoch 79 @ 2017-12-08 23:51:39.145159
 Loss = 0.256690186295
 Train acc_qm: 0.917309910389
   breakdown result: [ 0.99811907  0.99270695  0.92343182]
 Dev acc_qm: 0.578316114476
   breakdown result: [ 0.86616791  0.90868068  0.70537941]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71309820686379288), on epoch (3, 39, 63) individually
Epoch 80 @ 2017-12-09 00:04:00.372934
 Loss = 0.257567503664
 Train acc_qm: 0.917700292787
   breakdown result: [ 0.99875787  0.99281341  0.92311241]
 Dev acc_qm: 0.582709891937
   breakdown result: [ 0.87649923  0.90832443  0.70858568]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71309820686379288), on epoch (3, 39, 63) individually
Epoch 81 @ 2017-12-09 00:16:08.149454
 Loss = 0.249723121098
 Train acc_qm: 0.920574926803
   breakdown result: [ 0.99902404  0.99263597  0.92588058]
 Dev acc_qm: 0.584609903812
   breakdown result: [ 0.87364921  0.90547441  0.71476072]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71476071725448287), on epoch (3, 39, 81) individually
Epoch 82 @ 2017-12-09 00:28:41.624274
 Loss = 0.253320880119
 Train acc_qm: 0.922029988466
   breakdown result: [ 0.99763996  0.99277793  0.92861326]
 Dev acc_qm: 0.584728654554
   breakdown result: [ 0.87008669  0.90856193  0.71392946]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71476071725448287), on epoch (3, 39, 81) individually
Epoch 83 @ 2017-12-09 00:41:14.651998
 Loss = 0.253118861011
 Train acc_qm: 0.921373436252
   breakdown result: [ 0.99847396  0.99293763  0.92705173]
 Dev acc_qm: 0.587934924593
   breakdown result: [ 0.87400546  0.91081819  0.71309821]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71476071725448287), on epoch (3, 39, 81) individually
Epoch 84 @ 2017-12-09 00:53:29.140328
 Loss = 0.246028220915
 Train acc_qm: 0.916582379558
   breakdown result: [ 0.99799485  0.99263597  0.92311241]
 Dev acc_qm: 0.583303645648
   breakdown result: [ 0.87044294  0.91081819  0.71072319]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71476071725448287), on epoch (3, 39, 81) individually
Epoch 85 @ 2017-12-09 01:05:45.550753
 Loss = 0.247552559316
 Train acc_qm: 0.921834797267
   breakdown result: [ 0.99815456  0.99295537  0.92815189]
 Dev acc_qm: 0.585322408265
   breakdown result: [ 0.87329296  0.90868068  0.71155445]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71476071725448287), on epoch (3, 39, 81) individually
Epoch 86 @ 2017-12-09 01:18:06.489472
 Loss = 0.246847617095
 Train acc_qm: 0.919084375832
   breakdown result: [ 0.99842073  0.99316831  0.92479815]
 Dev acc_qm: 0.577128607054
   breakdown result: [ 0.87186795  0.90606816  0.70573566]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71476071725448287), on epoch (3, 39, 81) individually
Epoch 87 @ 2017-12-09 01:30:29.576054
 Loss = 0.244726871299
 Train acc_qm: 0.924798154556
   breakdown result: [ 0.99787064  0.99322154  0.931133  ]
 Dev acc_qm: 0.583066144163
   breakdown result: [ 0.86486166  0.91069944  0.71535447]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71535447096544358), on epoch (3, 39, 87) individually
Epoch 88 @ 2017-12-09 01:42:33.127599
 Loss = 0.245876292237
 Train acc_qm: 0.92437228285
   breakdown result: [ 0.99740928  0.99370065  0.93092006]
 Dev acc_qm: 0.587341170882
   breakdown result: [ 0.87293671  0.90974944  0.71404821]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71535447096544358), on epoch (3, 39, 87) individually
Epoch 89 @ 2017-12-09 01:55:03.438906
 Loss = 0.245186813348
 Train acc_qm: 0.924727175938
   breakdown result: [ 0.99888209  0.9935232   0.92985538]
 Dev acc_qm: 0.580097375609
   breakdown result: [ 0.87234295  0.90606816  0.70953568]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71535447096544358), on epoch (3, 39, 87) individually
Epoch 90 @ 2017-12-09 02:07:33.223923
 Loss = 0.243712493365
 Train acc_qm: 0.923875432526
   breakdown result: [ 0.99811907  0.99316831  0.92974891]
 Dev acc_qm: 0.585441159007
   breakdown result: [ 0.87566797  0.90820568  0.7121482 ]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71535447096544358), on epoch (3, 39, 87) individually
Epoch 91 @ 2017-12-09 02:19:44.895721
 Loss = 0.240347322677
 Train acc_qm: 0.92453198474
   breakdown result: [ 0.998332    0.99339899  0.93022802]
 Dev acc_qm: 0.585322408265
   breakdown result: [ 0.87412421  0.90879943  0.71226695]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71535447096544358), on epoch (3, 39, 87) individually
Epoch 92 @ 2017-12-09 02:32:12.341693
 Loss = 0.235853886101
 Train acc_qm: 0.925419217461
   breakdown result: [ 0.99838524  0.99331027  0.93136368]
 Dev acc_qm: 0.583778648617
   breakdown result: [ 0.8719867   0.90796817  0.71107944]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71535447096544358), on epoch (3, 39, 87) individually
Epoch 93 @ 2017-12-09 02:44:42.528380
 Loss = 0.239358642598
 Train acc_qm: 0.928293851477
   breakdown result: [ 0.99875787  0.99350546  0.93340431]
 Dev acc_qm: 0.588766179789
   breakdown result: [ 0.87578672  0.90784942  0.71321696]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71535447096544358), on epoch (3, 39, 87) individually
Epoch 94 @ 2017-12-09 02:57:31.589509
 Loss = 0.23880656935
 Train acc_qm: 0.9263774288
   breakdown result: [ 0.99847396  0.99309733  0.93196699]
 Dev acc_qm: 0.588528678304
   breakdown result: [ 0.87436171  0.90986819  0.7114357 ]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71535447096544358), on epoch (3, 39, 87) individually
Epoch 95 @ 2017-12-09 03:10:10.316699
 Loss = 0.235746347457
 Train acc_qm: 0.926892023778
   breakdown result: [ 0.99826102  0.99311507  0.93271227]
 Dev acc_qm: 0.586272414203
   breakdown result: [ 0.87507422  0.90939318  0.71084194]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71535447096544358), on epoch (3, 39, 87) individually
Epoch 96 @ 2017-12-09 03:22:12.777112
 Loss = 0.238582876495
 Train acc_qm: 0.922544583444
   breakdown result: [ 0.9984917   0.99382486  0.92781475]
 Dev acc_qm: 0.581759885999
   breakdown result: [ 0.87459922  0.90713692  0.70609191]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71535447096544358), on epoch (3, 39, 87) individually
Epoch 97 @ 2017-12-09 03:34:55.684770
 Loss = 0.237662542974
 Train acc_qm: 0.931310442729
   breakdown result: [ 0.9981723   0.99366516  0.93704197]
 Dev acc_qm: 0.586034912718
   breakdown result: [ 0.8726992   0.90879943  0.71048569]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71535447096544358), on epoch (3, 39, 87) individually
Epoch 98 @ 2017-12-09 03:47:22.054692
 Loss = 0.231235259346
 Train acc_qm: 0.926111258983
   breakdown result: [ 0.99907728  0.99400231  0.93068938]
 Dev acc_qm: 0.583541147132
   breakdown result: [ 0.87293671  0.90974944  0.70894193]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71535447096544358), on epoch (3, 39, 87) individually
Epoch 99 @ 2017-12-09 03:59:44.654281
 Loss = 0.228769769118
 Train acc_qm: 0.930973294295
   breakdown result: [ 0.99792388  0.99362967  0.93695324]
 Dev acc_qm: 0.583184894906
   breakdown result: [ 0.8633179   0.90879943  0.71250445]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71535447096544358), on epoch (3, 39, 87) individually
Epoch 100 @ 2017-12-09 04:11:48.956663
 Loss = 0.226331731582
 Train acc_qm: 0.931132996185
   breakdown result: [ 0.99799485  0.99357644  0.93704197]
 Dev acc_qm: 0.592922455765
   breakdown result: [ 0.87448047  0.90903693  0.71547322]
 Best val acc = (0.89739935874599219, 0.91295570597316233, 0.71547322170763572), on epoch (3, 39, 100) individually

Loading from original dataset
Loading data from data/train_tok.jsonl
Loading data from data/train_tok.tables.jsonl
Loading data from data/dev_tok.jsonl
Loading data from data/dev_tok.tables.jsonl
Loading data from data/test_tok.jsonl
Loading data from data/test_tok.tables.jsonl
Loading word embedding from glove/glove.42B.300d.txt
Using fixed embedding
Using column attention on aggregator predicting
Using column attention on selection predicting
Using column attention on where predicting
Init dev acc_qm: 0.0
  breakdown on (agg, sel, where): [ 0.05605035  0.16636979  0.        ]
Epoch 1 @ 2017-12-08 08:49:28.603583
 Loss = 3.18159007739
 Train acc_qm: 0.221453287197
   breakdown result: [ 0.89906841  0.82310354  0.28052524]
 Dev acc_qm: 0.215888849305
   breakdown result: [ 0.89205558  0.81795511  0.28167676]
 Best val acc = (0.89205557534734592, 0.81795511221945139, 0.28167676047975299), on epoch (1, 1, 1) individually
Epoch 2 @ 2017-12-08 09:00:35.831190
 Loss = 1.96937653412
 Train acc_qm: 0.355514151362
   breakdown result: [ 0.90755035  0.87587614  0.42500222]
 Dev acc_qm: 0.328108300677
   breakdown result: [ 0.89834936  0.8635554   0.39959625]
 Best val acc = (0.89834936468352922, 0.86355539722123265, 0.39959624747654671), on epoch (2, 2, 2) individually
Epoch 3 @ 2017-12-08 09:12:29.354855
 Loss = 1.66989595533
 Train acc_qm: 0.416271848106
   breakdown result: [ 0.91298021  0.89511135  0.48922012]
 Dev acc_qm: 0.377389858687
   breakdown result: [ 0.90001188  0.87353046  0.45790286]
 Best val acc = (0.9000118750742192, 0.87353045956537223, 0.45790286189288681), on epoch (3, 3, 3) individually
Epoch 4 @ 2017-12-08 09:24:03.544659
 Loss = 1.47290945794
 Train acc_qm: 0.46086416467
   breakdown result: [ 0.91624523  0.91429332  0.53166534]
 Dev acc_qm: 0.413133832086
   breakdown result: [ 0.89763686  0.88932431  0.49305308]
 Best val acc = (0.9000118750742192, 0.88932430827692677, 0.49305308158175987), on epoch (3, 4, 4) individually
Epoch 5 @ 2017-12-08 09:36:14.982989
 Loss = 1.32430725078
 Train acc_qm: 0.496122793009
   breakdown result: [ 0.92197675  0.92490462  0.56577056]
 Dev acc_qm: 0.430352689704
   breakdown result: [ 0.89371809  0.89146182  0.51905949]
 Best val acc = (0.9000118750742192, 0.89146182163638521, 0.51905949412183827), on epoch (3, 5, 5) individually
Epoch 6 @ 2017-12-08 09:48:30.032766
 Loss = 1.21172801404
 Train acc_qm: 0.529837636412
   breakdown result: [ 0.92605803  0.93233963  0.59870464]
 Dev acc_qm: 0.453034081463
   breakdown result: [ 0.89811186  0.89514309  0.54031588]
 Best val acc = (0.9000118750742192, 0.8951430946443415, 0.54031587697423111), on epoch (3, 6, 6) individually
Epoch 7 @ 2017-12-08 10:00:37.211384
 Loss = 1.12105150425
 Train acc_qm: 0.552053943749
   breakdown result: [ 0.93090232  0.93897613  0.61850767]
 Dev acc_qm: 0.460040375252
   breakdown result: [ 0.89241183  0.89823061  0.55373471]
 Best val acc = (0.9000118750742192, 0.89823061394133719, 0.55373471084194281), on epoch (3, 7, 7) individually
Epoch 8 @ 2017-12-08 10:12:34.572722
 Loss = 1.04428532918
 Train acc_qm: 0.579948540502
   breakdown result: [ 0.93603052  0.94508029  0.64413096]
 Dev acc_qm: 0.476427977675
   breakdown result: [ 0.89229308  0.90001188  0.57035981]
 Best val acc = (0.9000118750742192, 0.9000118750742192, 0.57035981474884223), on epoch (3, 8, 8) individually
Epoch 9 @ 2017-12-08 10:24:32.292508
 Loss = 0.968033904609
 Train acc_qm: 0.602360039038
   breakdown result: [ 0.93943749  0.95024399  0.66490995]
 Dev acc_qm: 0.485571784824
   breakdown result: [ 0.89193682  0.90214939  0.58235364]
 Best val acc = (0.9000118750742192, 0.90214938843367776, 0.58235363971024823), on epoch (3, 9, 9) individually
Epoch 10 @ 2017-12-08 10:36:30.895065
 Loss = 0.908425142774
 Train acc_qm: 0.628639872238
   breakdown result: [ 0.94658859  0.95294118  0.68790702]
 Dev acc_qm: 0.502671891699
   breakdown result: [ 0.89015556  0.90096188  0.60491628]
 Best val acc = (0.9000118750742192, 0.90214938843367776, 0.60491628072675452), on epoch (3, 9, 10) individually
Epoch 11 @ 2017-12-08 10:48:09.265829
 Loss = 0.855092918363
 Train acc_qm: 0.644077721586
   breakdown result: [ 0.9546979   0.95691598  0.69690356]
 Dev acc_qm: 0.497684360527
   breakdown result: [ 0.87851799  0.90523691  0.60277877]
 Best val acc = (0.9000118750742192, 0.90523690773067333, 0.60491628072675452), on epoch (3, 11, 10) individually
Epoch 12 @ 2017-12-08 10:59:30.239845
 Loss = 0.803127822238
 Train acc_qm: 0.666276284269
   breakdown result: [ 0.96142312  0.96129891  0.71439979]
 Dev acc_qm: 0.503740648379
   breakdown result: [ 0.87448047  0.90191189  0.61453509]
 Best val acc = (0.9000118750742192, 0.90523690773067333, 0.61453509084431779), on epoch (3, 11, 12) individually
Epoch 13 @ 2017-12-08 11:11:23.890899
 Loss = 0.761353050768
 Train acc_qm: 0.682352941176
   breakdown result: [ 0.96225712  0.96353474  0.72925206]
 Dev acc_qm: 0.516090725567
   breakdown result: [ 0.88564304  0.9042869   0.61940387]
 Best val acc = (0.9000118750742192, 0.90523690773067333, 0.61940387127419549), on epoch (3, 11, 13) individually
Epoch 14 @ 2017-12-08 11:23:17.167576
 Loss = 0.730321122331
 Train acc_qm: 0.693833732588
   breakdown result: [ 0.96791766  0.96580605  0.73565788]
 Dev acc_qm: 0.510984443653
   breakdown result: [ 0.86984919  0.90713692  0.62106638]
 Best val acc = (0.9000118750742192, 0.90713691960574749, 0.62106638166488537), on epoch (3, 14, 14) individually
Epoch 15 @ 2017-12-08 11:35:15.687686
 Loss = 0.691219090722
 Train acc_qm: 0.71129447254
   breakdown result: [ 0.97359595  0.96742081  0.74925029]
 Dev acc_qm: 0.517278232989
   breakdown result: [ 0.87519297  0.90143688  0.63175395]
 Best val acc = (0.9000118750742192, 0.90713691960574749, 0.63175394846217792), on epoch (3, 14, 15) individually
Epoch 16 @ 2017-12-08 11:47:06.772769
 Loss = 0.668988342024
 Train acc_qm: 0.722988199805
   breakdown result: [ 0.97496229  0.96978085  0.75948895]
 Dev acc_qm: 0.52654079088
   breakdown result: [ 0.87471797  0.90547441  0.64042275]
 Best val acc = (0.9000118750742192, 0.90713691960574749, 0.64042275264220405), on epoch (3, 14, 16) individually
Epoch 17 @ 2017-12-08 11:58:52.596818
 Loss = 0.638444191796
 Train acc_qm: 0.736119244078
   breakdown result: [ 0.97820956  0.97082779  0.7697986 ]
 Dev acc_qm: 0.527728298302
   breakdown result: [ 0.86866168  0.90820568  0.64184776]
 Best val acc = (0.9000118750742192, 0.90820567628547677, 0.64184776154850964), on epoch (3, 17, 17) individually
Epoch 18 @ 2017-12-08 12:10:36.851114
 Loss = 0.609811642797
 Train acc_qm: 0.744228551149
   breakdown result: [ 0.98220211  0.97249579  0.77379115]
 Dev acc_qm: 0.52654079088
   breakdown result: [ 0.87091794  0.90559316  0.6395915 ]
 Best val acc = (0.9000118750742192, 0.90820567628547677, 0.64184776154850964), on epoch (3, 17, 17) individually
Epoch 19 @ 2017-12-08 12:22:24.758639
 Loss = 0.592986375304
 Train acc_qm: 0.749463224204
   breakdown result: [ 0.98289415  0.97370242  0.77792565]
 Dev acc_qm: 0.538890868068
   breakdown result: [ 0.87495547  0.90737442  0.64576654]
 Best val acc = (0.9000118750742192, 0.90820567628547677, 0.64576653604085021), on epoch (3, 17, 19) individually
Epoch 20 @ 2017-12-08 12:34:12.846992
 Loss = 0.574018853377
 Train acc_qm: 0.76032295271
   breakdown result: [ 0.98346198  0.97567208  0.78731257]
 Dev acc_qm: 0.529390808693
   breakdown result: [ 0.86723667  0.90868068  0.64541028]
 Best val acc = (0.9000118750742192, 0.90868067925424534, 0.64576653604085021), on epoch (3, 20, 19) individually
Epoch 21 @ 2017-12-08 12:46:02.364478
 Loss = 0.555446748737
 Train acc_qm: 0.76509626475
   breakdown result: [ 0.98734806  0.97616893  0.78894508]
 Dev acc_qm: 0.531647072794
   breakdown result: [ 0.87353046  0.90618691  0.64267902]
 Best val acc = (0.9000118750742192, 0.90868067925424534, 0.64576653604085021), on epoch (3, 20, 19) individually
Epoch 22 @ 2017-12-08 12:57:53.376997
 Loss = 0.537902158564
 Train acc_qm: 0.778156330405
   breakdown result: [ 0.98811108  0.97719812  0.80047911]
 Dev acc_qm: 0.53900961881
   breakdown result: [ 0.87317421  0.90594941  0.65384159]
 Best val acc = (0.9000118750742192, 0.90868067925424534, 0.65384158650991564), on epoch (3, 20, 22) individually
Epoch 23 @ 2017-12-08 13:09:35.583132
 Loss = 0.523635108575
 Train acc_qm: 0.780427646172
   breakdown result: [ 0.98532517  0.97806761  0.80473782]
 Dev acc_qm: 0.547797173732
   breakdown result: [ 0.87958675  0.90832443  0.65514784]
 Best val acc = (0.9000118750742192, 0.90868067925424534, 0.6551478446740292), on epoch (3, 20, 23) individually
Epoch 24 @ 2017-12-08 13:21:14.604814
 Loss = 0.504709056464
 Train acc_qm: 0.789921036288
   breakdown result: [ 0.98965487  0.97948718  0.81027415]
 Dev acc_qm: 0.539959624748
   breakdown result: [ 0.86889918  0.90808693  0.65657285]
 Best val acc = (0.9000118750742192, 0.90868067925424534, 0.6565728535803349), on epoch (3, 20, 24) individually
Epoch 25 @ 2017-12-08 13:32:43.568091
 Loss = 0.496179655026
 Train acc_qm: 0.792085884127
   breakdown result: [ 0.99109218  0.97959365  0.81171147]
 Dev acc_qm: 0.543640897756
   breakdown result: [ 0.87697423  0.90796817  0.65692911]
 Best val acc = (0.9000118750742192, 0.90868067925424534, 0.65692910580691133), on epoch (3, 20, 25) individually
Epoch 26 @ 2017-12-08 13:44:35.667850
 Loss = 0.484074264709
 Train acc_qm: 0.801721231479
   breakdown result: [ 0.99222784  0.98099548  0.81944814]
 Dev acc_qm: 0.542215888849
   breakdown result: [ 0.87424296  0.91010569  0.65467284]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.65692910580691133), on epoch (3, 26, 25) individually
Epoch 27 @ 2017-12-08 13:56:19.924348
 Loss = 0.473590099628
 Train acc_qm: 0.805181439091
   breakdown result: [ 0.99316831  0.98172301  0.82164848]
 Dev acc_qm: 0.542572141076
   breakdown result: [ 0.87400546  0.90820568  0.65764161]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.65764161026006418), on epoch (3, 26, 27) individually
Epoch 28 @ 2017-12-08 14:07:50.083307
 Loss = 0.468107026444
 Train acc_qm: 0.805287907018
   breakdown result: [ 0.9928489   0.98195369  0.82191465]
 Dev acc_qm: 0.54494715592
   breakdown result: [ 0.87531172  0.90618691  0.65894787]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.65894786842417763), on epoch (3, 26, 28) individually
Epoch 29 @ 2017-12-08 14:19:26.007528
 Loss = 0.451346294976
 Train acc_qm: 0.814621595244
   breakdown result: [ 0.99350546  0.98269896  0.83037885]
 Dev acc_qm: 0.544828405178
   breakdown result: [ 0.87578672  0.90630566  0.65764161]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.65894786842417763), on epoch (3, 26, 28) individually
Epoch 30 @ 2017-12-08 14:30:59.144430
 Loss = 0.444318710115
 Train acc_qm: 0.815437849348
   breakdown result: [ 0.99359418  0.98269896  0.83098217]
 Dev acc_qm: 0.546372164826
   breakdown result: [ 0.87887424  0.90749317  0.66001663]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.66001662510390691), on epoch (3, 26, 30) individually
Epoch 31 @ 2017-12-08 14:42:33.287898
 Loss = 0.433974149069
 Train acc_qm: 0.821950137521
   breakdown result: [ 0.99387809  0.98285866  0.8376719 ]
 Dev acc_qm: 0.547559672248
   breakdown result: [ 0.88089301  0.9038119   0.65882912]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.66001662510390691), on epoch (3, 26, 30) individually
Epoch 32 @ 2017-12-08 14:53:58.620013
 Loss = 0.427743175097
 Train acc_qm: 0.82443438914
   breakdown result: [ 0.99247627  0.98365717  0.84081271]
 Dev acc_qm: 0.554090963069
   breakdown result: [ 0.87614298  0.90594941  0.66666667]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.66666666666666663), on epoch (3, 26, 32) individually
Epoch 33 @ 2017-12-08 15:05:39.801723
 Loss = 0.424038975259
 Train acc_qm: 0.827450980392
   breakdown result: [ 0.99322154  0.98385236  0.84262266]
 Dev acc_qm: 0.543165894787
   breakdown result: [ 0.87044294  0.90535566  0.65906662]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.66666666666666663), on epoch (3, 26, 32) individually
Epoch 34 @ 2017-12-08 15:17:10.117346
 Loss = 0.411503393988
 Train acc_qm: 0.833945523911
   breakdown result: [ 0.99510248  0.98530743  0.84702333]
 Dev acc_qm: 0.542097138107
   breakdown result: [ 0.8722242   0.90191189  0.6644104 ]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.66666666666666663), on epoch (3, 26, 32) individually
Epoch 35 @ 2017-12-08 15:28:31.012296
 Loss = 0.405614521676
 Train acc_qm: 0.8328985893
   breakdown result: [ 0.99520894  0.98566232  0.84571023]
 Dev acc_qm: 0.55254720342
   breakdown result: [ 0.87281796  0.90701817  0.66726042]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.66726042037762734), on epoch (3, 26, 35) individually
Epoch 36 @ 2017-12-08 15:39:54.928850
 Loss = 0.395326342068
 Train acc_qm: 0.838363942862
   breakdown result: [ 0.99620264  0.98576879  0.85037707]
 Dev acc_qm: 0.543165894787
   breakdown result: [ 0.86628666  0.9042869   0.66310414]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.66726042037762734), on epoch (3, 26, 35) individually
Epoch 37 @ 2017-12-08 15:51:35.328628
 Loss = 0.395906970471
 Train acc_qm: 0.840209386922
   breakdown result: [ 0.99598971  0.98619466  0.85204507]
 Dev acc_qm: 0.545897161857
   breakdown result: [ 0.87008669  0.9038119   0.66654792]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.66726042037762734), on epoch (3, 26, 35) individually
Epoch 38 @ 2017-12-08 16:02:34.368645
 Loss = 0.38886618687
 Train acc_qm: 0.845177890161
   breakdown result: [ 0.99439269  0.98653181  0.85816698]
 Dev acc_qm: 0.547559672248
   breakdown result: [ 0.86284289  0.90535566  0.67379171]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.67379171119819503), on epoch (3, 26, 38) individually
Epoch 39 @ 2017-12-08 16:14:17.552982
 Loss = 0.385973627199
 Train acc_qm: 0.847999290214
   breakdown result: [ 0.9965043   0.98736581  0.8584154 ]
 Dev acc_qm: 0.550884693029
   breakdown result: [ 0.86937418  0.90369315  0.67117919]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.67379171119819503), on epoch (3, 26, 38) individually
Epoch 40 @ 2017-12-08 16:25:57.002177
 Loss = 0.378901253916
 Train acc_qm: 0.851778901606
   breakdown result: [ 0.99620264  0.98759649  0.86246118]
 Dev acc_qm: 0.551597197482
   breakdown result: [ 0.87364921  0.90559316  0.66880418]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.67379171119819503), on epoch (3, 26, 38) individually
Epoch 41 @ 2017-12-08 16:37:35.651212
 Loss = 0.373460210223
 Train acc_qm: 0.854529323041
   breakdown result: [ 0.99623813  0.98715287  0.86558424]
 Dev acc_qm: 0.553140957131
   breakdown result: [ 0.88077425  0.90321815  0.66844793]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.67379171119819503), on epoch (3, 26, 38) individually
Epoch 42 @ 2017-12-08 16:49:15.582763
 Loss = 0.367452798962
 Train acc_qm: 0.858841274066
   breakdown result: [ 0.99600745  0.98759649  0.86971875]
 Dev acc_qm: 0.556584728655
   breakdown result: [ 0.87210545  0.90321815  0.67628548]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.6762854767842299), on epoch (3, 26, 42) individually
Epoch 43 @ 2017-12-08 17:00:37.083162
 Loss = 0.361625125452
 Train acc_qm: 0.858592848904
   breakdown result: [ 0.99611392  0.98786266  0.8692219 ]
 Dev acc_qm: 0.558009737561
   breakdown result: [ 0.87875549  0.90725567  0.67509797]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.6762854767842299), on epoch (3, 26, 42) individually
Epoch 44 @ 2017-12-08 17:12:09.483649
 Loss = 0.363394550728
 Train acc_qm: 0.858752550794
   breakdown result: [ 0.99710762  0.98791589  0.86851211]
 Dev acc_qm: 0.55254720342
   breakdown result: [ 0.87531172  0.9035744   0.67391046]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.6762854767842299), on epoch (3, 26, 42) individually
Epoch 45 @ 2017-12-08 17:23:59.192367
 Loss = 0.355886637168
 Train acc_qm: 0.863011267856
   breakdown result: [ 0.99588324  0.98846597  0.87371129]
 Dev acc_qm: 0.560266001663
   breakdown result: [ 0.87721173  0.9047619   0.67723548]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.67723548272176703), on epoch (3, 26, 45) individually
Epoch 46 @ 2017-12-08 17:35:31.543167
 Loss = 0.350177147515
 Train acc_qm: 0.865300328276
   breakdown result: [ 0.99707213  0.98862568  0.87472274]
 Dev acc_qm: 0.549222182639
   breakdown result: [ 0.87008669  0.90535566  0.67129795]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.67723548272176703), on epoch (3, 26, 45) individually
Epoch 47 @ 2017-12-08 17:47:19.939297
 Loss = 0.346293343027
 Train acc_qm: 0.872114275574
   breakdown result: [ 0.99751575  0.98935321  0.8808269 ]
 Dev acc_qm: 0.558603491272
   breakdown result: [ 0.87638048  0.9038119   0.67759173]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.67759173494834346), on epoch (3, 26, 47) individually
Epoch 48 @ 2017-12-08 17:59:17.368085
 Loss = 0.344049304885
 Train acc_qm: 0.869630023955
   breakdown result: [ 0.99728507  0.98901606  0.87871529]
 Dev acc_qm: 0.55634722717
   breakdown result: [ 0.87329296  0.90321815  0.67581047]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.67759173494834346), on epoch (3, 26, 47) individually
Epoch 49 @ 2017-12-08 18:11:12.061101
 Loss = 0.339612448482
 Train acc_qm: 0.868707301925
   breakdown result: [ 0.9971786   0.98958389  0.87788129]
 Dev acc_qm: 0.553734710842
   breakdown result: [ 0.8726992   0.9045244   0.67248545]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.67759173494834346), on epoch (3, 26, 47) individually
Epoch 50 @ 2017-12-08 18:22:57.235433
 Loss = 0.336746010177
 Train acc_qm: 0.875734185077
   breakdown result: [ 0.99694792  0.98979682  0.88423387]
 Dev acc_qm: 0.558247239045
   breakdown result: [ 0.87400546  0.90488066  0.67854174]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.67854174088588048), on epoch (3, 26, 50) individually
Epoch 51 @ 2017-12-08 18:34:19.904683
 Loss = 0.329627581547
 Train acc_qm: 0.87795226688
   breakdown result: [ 0.99769319  0.9893887   0.88639872]
 Dev acc_qm: 0.559197244983
   breakdown result: [ 0.87744923  0.90262439  0.67866049]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.67866049162807263), on epoch (3, 26, 51) individually
Epoch 52 @ 2017-12-08 18:45:57.102277
 Loss = 0.326754097183
 Train acc_qm: 0.874296868068
   breakdown result: [ 0.99739154  0.98979682  0.88300949]
 Dev acc_qm: 0.556940980881
   breakdown result: [ 0.8801805   0.90369315  0.67284171]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.67866049162807263), on epoch (3, 26, 51) individually
Epoch 53 @ 2017-12-08 18:57:42.325871
 Loss = 0.32647272312
 Train acc_qm: 0.873746783781
   breakdown result: [ 0.996664    0.98967261  0.88274332]
 Dev acc_qm: 0.553259707873
   breakdown result: [ 0.87388671  0.90571191  0.67581047]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.67866049162807263), on epoch (3, 26, 51) individually
Epoch 54 @ 2017-12-08 19:09:22.494179
 Loss = 0.32543973811
 Train acc_qm: 0.880365539881
   breakdown result: [ 0.99776417  0.99004525  0.88842161]
 Dev acc_qm: 0.558128488303
   breakdown result: [ 0.87899299  0.90226814  0.67759173]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.67866049162807263), on epoch (3, 26, 51) individually
Epoch 55 @ 2017-12-08 19:20:51.987078
 Loss = 0.320021852778
 Train acc_qm: 0.878821754946
   breakdown result: [ 0.99646881  0.99071955  0.8873037 ]
 Dev acc_qm: 0.556822230139
   breakdown result: [ 0.86652417  0.90499941  0.67842299]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.67866049162807263), on epoch (3, 26, 51) individually
Epoch 56 @ 2017-12-08 19:32:57.118536
 Loss = 0.319326929194
 Train acc_qm: 0.88332889717
   breakdown result: [ 0.99799485  0.99041789  0.89097684]
 Dev acc_qm: 0.559909749436
   breakdown result: [ 0.87424296  0.90499941  0.68186676]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68186676166726046), on epoch (3, 26, 56) individually
Epoch 57 @ 2017-12-08 19:45:11.735345
 Loss = 0.313572577848
 Train acc_qm: 0.883843492148
   breakdown result: [ 0.99723183  0.99013397  0.89234318]
 Dev acc_qm: 0.559434746467
   breakdown result: [ 0.87210545  0.90345565  0.67830424]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68186676166726046), on epoch (3, 26, 56) individually
Epoch 58 @ 2017-12-08 19:57:14.376392
 Loss = 0.311894422271
 Train acc_qm: 0.886842338745
   breakdown result: [ 0.99769319  0.99036465  0.8947742 ]
 Dev acc_qm: 0.561928512053
   breakdown result: [ 0.87448047  0.9038119   0.68210426]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68210426315164474), on epoch (3, 26, 58) individually
Epoch 59 @ 2017-12-08 20:09:09.179873
 Loss = 0.315827244705
 Train acc_qm: 0.886469701003
   breakdown result: [ 0.99758673  0.99071955  0.8939402 ]
 Dev acc_qm: 0.560978506116
   breakdown result: [ 0.87685548  0.9040494   0.67972925]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68210426315164474), on epoch (3, 26, 58) individually
Epoch 60 @ 2017-12-08 20:21:05.554051
 Loss = 0.307100530407
 Train acc_qm: 0.88577765948
   breakdown result: [ 0.99746251  0.99059533  0.89340786]
 Dev acc_qm: 0.559078494241
   breakdown result: [ 0.87079919  0.9040494   0.68210426]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68210426315164474), on epoch (3, 26, 58) individually
Epoch 61 @ 2017-12-08 20:33:09.092624
 Loss = 0.30082371457
 Train acc_qm: 0.887569869577
   breakdown result: [ 0.99710762  0.99103895  0.89573241]
 Dev acc_qm: 0.554447215295
   breakdown result: [ 0.86486166  0.9042869   0.68091676]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68210426315164474), on epoch (3, 26, 58) individually
Epoch 62 @ 2017-12-08 20:45:20.917026
 Loss = 0.301456267987
 Train acc_qm: 0.888332889717
   breakdown result: [ 0.9978529   0.99141159  0.89516458]
 Dev acc_qm: 0.5612160076
   breakdown result: [ 0.87543047  0.90309939  0.68281677]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68281676760479748), on epoch (3, 26, 62) individually
Epoch 63 @ 2017-12-08 20:57:17.731827
 Loss = 0.307662096047
 Train acc_qm: 0.88654067962
   breakdown result: [ 0.99693017  0.99142933  0.8944548 ]
 Dev acc_qm: 0.554684716779
   breakdown result: [ 0.87424296  0.9045244   0.67367296]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68281676760479748), on epoch (3, 26, 62) individually
Epoch 64 @ 2017-12-08 21:09:04.327718
 Loss = 0.296020062847
 Train acc_qm: 0.894525774111
   breakdown result: [ 0.99772868  0.9913761   0.90164138]
 Dev acc_qm: 0.561097256858
   breakdown result: [ 0.87721173  0.90499941  0.68139176]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68281676760479748), on epoch (3, 26, 62) individually
Epoch 65 @ 2017-12-08 21:20:11.771941
 Loss = 0.294738672628
 Train acc_qm: 0.895909857156
   breakdown result: [ 0.99831426  0.9918552   0.90249312]
 Dev acc_qm: 0.56228476428
   breakdown result: [ 0.87590547  0.90618691  0.68222301]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68281676760479748), on epoch (3, 26, 62) individually
Epoch 66 @ 2017-12-08 21:31:57.787654
 Loss = 0.2915578106
 Train acc_qm: 0.892059267146
   breakdown result: [ 0.99740928  0.99132286  0.89977819]
 Dev acc_qm: 0.555753473459
   breakdown result: [ 0.86901793  0.9040494   0.67925425]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68281676760479748), on epoch (3, 26, 62) individually
Epoch 67 @ 2017-12-08 21:43:40.507936
 Loss = 0.294576538899
 Train acc_qm: 0.897719811907
   breakdown result: [ 0.99792388  0.99197942  0.9045515 ]
 Dev acc_qm: 0.556822230139
   breakdown result: [ 0.86830543  0.9035744   0.68174801]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68281676760479748), on epoch (3, 26, 62) individually
Epoch 68 @ 2017-12-08 21:55:22.161161
 Loss = 0.291191575478
 Train acc_qm: 0.897702067252
   breakdown result: [ 0.99779966  0.99183746  0.90471121]
 Dev acc_qm: 0.556703479397
   breakdown result: [ 0.87091794  0.90203064  0.67996675]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68281676760479748), on epoch (3, 26, 62) individually
Epoch 69 @ 2017-12-08 22:07:05.754858
 Loss = 0.284932967089
 Train acc_qm: 0.900452488688
   breakdown result: [ 0.99813681  0.99197942  0.90715997]
 Dev acc_qm: 0.559790998694
   breakdown result: [ 0.87412421  0.90488066  0.68198551]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68281676760479748), on epoch (3, 26, 62) individually
Epoch 70 @ 2017-12-08 22:18:54.336061
 Loss = 0.287435966502
 Train acc_qm: 0.895785644575
   breakdown result: [ 0.99744477  0.99249401  0.90263508]
 Dev acc_qm: 0.560503503147
   breakdown result: [ 0.87353046  0.90309939  0.68127301]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68281676760479748), on epoch (3, 26, 62) individually
Epoch 71 @ 2017-12-08 22:30:24.739639
 Loss = 0.282685032955
 Train acc_qm: 0.900044361636
   breakdown result: [ 0.9988466   0.99190844  0.90597108]
 Dev acc_qm: 0.559434746467
   breakdown result: [ 0.87507422  0.90203064  0.67771049]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68281676760479748), on epoch (3, 26, 62) individually
Epoch 72 @ 2017-12-08 22:42:17.584115
 Loss = 0.281276474695
 Train acc_qm: 0.901730103806
   breakdown result: [ 0.99753349  0.99263597  0.90854405]
 Dev acc_qm: 0.561809761311
   breakdown result: [ 0.87364921  0.90630566  0.68246052]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68281676760479748), on epoch (3, 26, 62) individually
Epoch 73 @ 2017-12-08 22:54:00.387758
 Loss = 0.281478569265
 Train acc_qm: 0.897276195546
   breakdown result: [ 0.99769319  0.99291988  0.90364653]
 Dev acc_qm: 0.557653485334
   breakdown result: [ 0.87412421  0.90571191  0.67592922]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68281676760479748), on epoch (3, 26, 62) individually
Epoch 74 @ 2017-12-08 23:05:56.343431
 Loss = 0.273349549873
 Train acc_qm: 0.902759293763
   breakdown result: [ 0.99772868  0.99247627  0.90944903]
 Dev acc_qm: 0.560859755373
   breakdown result: [ 0.86996794  0.90298064  0.68566679]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68566678541740889), on epoch (3, 26, 74) individually
Epoch 75 @ 2017-12-08 23:17:39.275578
 Loss = 0.274667795299
 Train acc_qm: 0.900204063526
   breakdown result: [ 0.99753349  0.99281341  0.90700027]
 Dev acc_qm: 0.557059731623
   breakdown result: [ 0.86806793  0.90440565  0.67901674]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68566678541740889), on epoch (3, 26, 74) individually
Epoch 76 @ 2017-12-08 23:29:14.166575
 Loss = 0.276893112249
 Train acc_qm: 0.906787330317
   breakdown result: [ 0.99822553  0.99318605  0.91264307]
 Dev acc_qm: 0.564303526897
   breakdown result: [ 0.87756798  0.90535566  0.68364802]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68566678541740889), on epoch (3, 26, 74) individually
Epoch 77 @ 2017-12-08 23:40:47.831438
 Loss = 0.269381489821
 Train acc_qm: 0.904604737823
   breakdown result: [ 0.99797711  0.99323929  0.91074439]
 Dev acc_qm: 0.560503503147
   breakdown result: [ 0.86783042  0.9047619   0.68364802]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68566678541740889), on epoch (3, 26, 74) individually
Epoch 78 @ 2017-12-08 23:52:29.816467
 Loss = 0.274306918696
 Train acc_qm: 0.904090142844
   breakdown result: [ 0.9971786   0.99274244  0.91090409]
 Dev acc_qm: 0.566203538772
   breakdown result: [ 0.88101176  0.90547441  0.68269802]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68566678541740889), on epoch (3, 26, 74) individually
Epoch 79 @ 2017-12-09 00:04:30.913354
 Loss = 0.269621844134
 Train acc_qm: 0.902848017035
   breakdown result: [ 0.99712537  0.9930441   0.90952001]
 Dev acc_qm: 0.559909749436
   breakdown result: [ 0.86973044  0.90618691  0.68269802]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68566678541740889), on epoch (3, 26, 74) individually
Epoch 80 @ 2017-12-09 00:15:56.267026
 Loss = 0.269170713844
 Train acc_qm: 0.908135924053
   breakdown result: [ 0.99870464  0.99323929  0.91349481]
 Dev acc_qm: 0.564659779124
   breakdown result: [ 0.87614298  0.9042869   0.68578554]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68578553615960103), on epoch (3, 26, 80) individually
Epoch 81 @ 2017-12-09 00:27:43.839444
 Loss = 0.263365805397
 Train acc_qm: 0.906432437228
   breakdown result: [ 0.99804809  0.99316831  0.91227043]
 Dev acc_qm: 0.561453509084
   breakdown result: [ 0.87341171  0.9045244   0.68151051]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68578553615960103), on epoch (3, 26, 80) individually
Epoch 82 @ 2017-12-09 00:39:23.236448
 Loss = 0.265230411574
 Train acc_qm: 0.90744388253
   breakdown result: [ 0.99875787  0.99359418  0.91241239]
 Dev acc_qm: 0.562522265764
   breakdown result: [ 0.87459922  0.90416815  0.68317302]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68578553615960103), on epoch (3, 26, 80) individually
Epoch 83 @ 2017-12-09 00:51:10.336046
 Loss = 0.264371699742
 Train acc_qm: 0.910354005856
   breakdown result: [ 0.99820779  0.99346997  0.91620974]
 Dev acc_qm: 0.563709773186
   breakdown result: [ 0.87008669  0.90630566  0.68495428]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68578553615960103), on epoch (3, 26, 80) individually
Epoch 84 @ 2017-12-09 01:02:57.774466
 Loss = 0.265580277597
 Train acc_qm: 0.906663117736
   breakdown result: [ 0.99819005  0.9933635   0.91207524]
 Dev acc_qm: 0.564422277639
   breakdown result: [ 0.87448047  0.90345565  0.68602304]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68602303764398531), on epoch (3, 26, 84) individually
Epoch 85 @ 2017-12-09 01:14:59.139892
 Loss = 0.264482853855
 Train acc_qm: 0.911400940467
   breakdown result: [ 0.99861592  0.99345222  0.9163517 ]
 Dev acc_qm: 0.565609785061
   breakdown result: [ 0.87602423  0.90345565  0.68685429]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68685429283933019), on epoch (3, 26, 85) individually
Epoch 86 @ 2017-12-09 01:26:50.514159
 Loss = 0.258750381882
 Train acc_qm: 0.91205749268
   breakdown result: [ 0.99854494  0.99309733  0.91755834]
 Dev acc_qm: 0.560266001663
   breakdown result: [ 0.86878043  0.90369315  0.68578554]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68685429283933019), on epoch (3, 26, 85) individually
Epoch 87 @ 2017-12-09 01:38:32.381796
 Loss = 0.256071149917
 Train acc_qm: 0.910602431018
   breakdown result: [ 0.99842073  0.99325703  0.91622749]
 Dev acc_qm: 0.564659779124
   breakdown result: [ 0.87281796  0.90416815  0.68507303]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68685429283933019), on epoch (3, 26, 85) individually
Epoch 88 @ 2017-12-09 01:50:33.102865
 Loss = 0.260383604582
 Train acc_qm: 0.914807914116
   breakdown result: [ 0.99842073  0.9938426   0.9203265 ]
 Dev acc_qm: 0.564541028381
   breakdown result: [ 0.86925543  0.9042869   0.68566679]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68685429283933019), on epoch (3, 26, 85) individually
Epoch 89 @ 2017-12-09 02:02:42.477491
 Loss = 0.252876423042
 Train acc_qm: 0.912394641114
   breakdown result: [ 0.99822553  0.9938426   0.91752285]
 Dev acc_qm: 0.562878517991
   breakdown result: [ 0.87733048  0.9047619   0.68151051]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68685429283933019), on epoch (3, 26, 85) individually
Epoch 90 @ 2017-12-09 02:14:37.933578
 Loss = 0.255141329756
 Train acc_qm: 0.9145417443
   breakdown result: [ 0.99847396  0.99405554  0.91968769]
 Dev acc_qm: 0.556822230139
   breakdown result: [ 0.87436171  0.90167439  0.67877924]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68685429283933019), on epoch (3, 26, 85) individually
Epoch 91 @ 2017-12-09 02:26:18.090596
 Loss = 0.254245977165
 Train acc_qm: 0.915233785822
   breakdown result: [ 0.99808358  0.99423299  0.92053944]
 Dev acc_qm: 0.56335352096
   breakdown result: [ 0.87638048  0.90416815  0.68364802]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68685429283933019), on epoch (3, 26, 85) individually
Epoch 92 @ 2017-12-09 02:38:24.908764
 Loss = 0.251698012296
 Train acc_qm: 0.916280720433
   breakdown result: [ 0.99790613  0.99405554  0.92172833]
 Dev acc_qm: 0.561691010569
   breakdown result: [ 0.87163045  0.90143688  0.68471678]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68685429283933019), on epoch (3, 26, 85) individually
Epoch 93 @ 2017-12-09 02:50:27.989307
 Loss = 0.249176882936
 Train acc_qm: 0.915943571999
   breakdown result: [ 0.99838524  0.99355869  0.92119599]
 Dev acc_qm: 0.561928512053
   breakdown result: [ 0.87210545  0.9035744   0.68554803]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68685429283933019), on epoch (3, 26, 85) individually
Epoch 94 @ 2017-12-09 03:02:32.518325
 Loss = 0.254283299775
 Train acc_qm: 0.914683701535
   breakdown result: [ 0.99874013  0.99402005  0.91943927]
 Dev acc_qm: 0.557297233108
   breakdown result: [ 0.87744923  0.90238689  0.67794799]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68685429283933019), on epoch (3, 26, 85) individually
Epoch 95 @ 2017-12-09 03:14:45.784490
 Loss = 0.249746574522
 Train acc_qm: 0.918693993434
   breakdown result: [ 0.99836749  0.99361192  0.92421258]
 Dev acc_qm: 0.558959743498
   breakdown result: [ 0.87554922  0.90036813  0.6805605 ]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68685429283933019), on epoch (3, 26, 85) individually
Epoch 96 @ 2017-12-09 03:26:44.020391
 Loss = 0.250146426723
 Train acc_qm: 0.914506254991
   breakdown result: [ 0.99843847  0.99437494  0.9193328 ]
 Dev acc_qm: 0.558959743498
   breakdown result: [ 0.87602423  0.90250564  0.680798  ]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68685429283933019), on epoch (3, 26, 85) individually
Epoch 97 @ 2017-12-09 03:38:51.682335
 Loss = 0.249061628026
 Train acc_qm: 0.916262975779
   breakdown result: [ 0.99765771  0.99346997  0.92217195]
 Dev acc_qm: 0.564184776155
   breakdown result: [ 0.88113051  0.9040494   0.68305427]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68685429283933019), on epoch (3, 26, 85) individually
Epoch 98 @ 2017-12-09 03:50:40.854052
 Loss = 0.246728437775
 Train acc_qm: 0.921142755745
   breakdown result: [ 0.99858043  0.99417975  0.92600479]
 Dev acc_qm: 0.560266001663
   breakdown result: [ 0.8719867   0.90238689  0.68376677]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68685429283933019), on epoch (3, 26, 85) individually
Epoch 99 @ 2017-12-09 04:02:21.009090
 Loss = 0.243704730781
 Train acc_qm: 0.918321355692
   breakdown result: [ 0.99829651  0.99394907  0.92348505]
 Dev acc_qm: 0.560266001663
   breakdown result: [ 0.8717492  0.9038119  0.680798 ]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68685429283933019), on epoch (3, 26, 85) individually
Epoch 100 @ 2017-12-09 04:14:26.526097
 Loss = 0.24290933758
 Train acc_qm: 0.914080383285
   breakdown result: [ 0.99898855  0.99412652  0.91874723]
 Dev acc_qm: 0.562166013538
   breakdown result: [ 0.87780549  0.90440565  0.68139176]
 Best val acc = (0.9000118750742192, 0.91010568816055104, 0.68685429283933019), on epoch (3, 26, 85) individually

Hi, we also observed that the final result may fluctuate by around 1~2% due to the randomness because the dev set and test set is relatively small (only around 10000 cases). In addition, our final model is chosen as the models that perform the best on dev acc_qm in each part in the process of training, so the dev acc_qm may fluctuate more fiercely.