Tensorboard Projector - cosine distance "Nearest points in the original space" not correct

Environment information (required)

--- check: autoidentify
INFO: diagnose_tensorboard.py version 393931f9685bd7e0f3898d7dcdf28819fef54c43

--- check: general
INFO: sys.version_info: sys.version_info(major=3, minor=6, micro=8, releaselevel='final', serial=0)
INFO: os.name: nt
INFO: os.uname(): N/A
INFO: sys.getwindowsversion(): sys.getwindowsversion(major=10, minor=0, build=17763, platform=2, service_pack='')

--- check: package_management
INFO: has conda-meta: False
INFO: $VIRTUAL_ENV: None

--- check: installed_packages
INFO: installed: tensorboard==1.13.1
INFO: installed: tensorflow-gpu==1.13.1
INFO: installed: tensorflow==1.14.0
WARNING: conflicting installations: ['tensorflow', 'tensorflow-gpu']
INFO: installed: tensorflow-estimator==1.13.0

--- check: tensorboard_python_version
INFO: tensorboard.version.VERSION: '1.13.1'

--- check: tensorflow_python_version
INFO: tensorflow.__version__: '1.13.1'
INFO: tensorflow.__git_version__: "b'v1.13.1-0-g6612da8951'"

--- check: tensorboard_binary_path
INFO: which tensorboard: b'F:\\Desktop\\Thesis\\Python3.6\\Scripts\\tensorboard.exe\r\n'

--- check: readable_fqdn
INFO: socket.getfqdn(): 'DESKTOP-LD8UUFN.home'

--- check: stat_tensorboardinfo
INFO: directory: C:\Users\josch\AppData\Local\Temp\.tensorboard-info
INFO: os.stat(...): os.stat_result(st_mode=16895, st_ino=61361544923004089, st_dev=3506408066, st_nlink=1, st_uid=0, st_gid=0, st_size=24576, st_atime=1562950451, st_mtime=1562950451, st_ctime=1560964117)
INFO: mode: 0o40777

--- check: source_trees_without_genfiles
INFO: tensorboard_roots (1): ['F:\\Python3.6\\lib\\site-packages']; bad_roots (0): []

--- check: full_pip_freeze
INFO: pip freeze --all:
absl-py==0.7.1
astor==0.8.0
attrs==19.1.0
backcall==0.1.0
bleach==3.1.0
boto==2.49.0
boto3==1.9.171
botocore==1.12.171
certifi==2019.6.16
chardet==3.0.4
colorama==0.4.1
cycler==0.10.0
decorator==4.4.0
defusedxml==0.6.0
docutils==0.14
entrypoints==0.3
gast==0.2.2
gensim==3.7.3
google-pasta==0.1.7
grpcio==1.21.1
h5py==2.9.0
idna==2.8
ipykernel==5.1.1
ipython==7.5.0
ipython-genutils==0.2.0
ipywidgets==7.4.2
jedi==0.13.3
Jinja2==2.10.1
jmespath==0.9.4
joblib==0.13.2
jsonschema==3.0.1
jupyter==1.0.0
jupyter-client==5.2.4
jupyter-console==6.0.0
jupyter-core==4.5.0
Keras-Applications==1.0.8
Keras-Preprocessing==1.1.0
kiwisolver==1.1.0
Markdown==3.1.1
MarkupSafe==1.1.1
matplotlib==3.1.0
mistune==0.8.4
mock==3.0.5
nbconvert==5.5.0
nbformat==4.4.0
notebook==5.7.8
numpy==1.16.4
pandas==0.24.2
pandocfilters==1.4.2
parso==0.4.0
pickleshare==0.7.5
pip==18.1
prometheus-client==0.7.0
prompt-toolkit==2.0.9
protobuf==3.8.0
Pygments==2.4.2
pyparsing==2.4.0
pyrsistent==0.15.2
python-dateutil==2.8.0
pytz==2019.1
pywinpty==0.5.5
pyzmq==18.0.1
qtconsole==4.5.1
requests==2.22.0
s3transfer==0.2.1
scikit-learn==0.21.2
scipy==1.3.0
Send2Trash==1.5.0
setuptools==41.0.1
six==1.12.0
sklearn==0.0
smart-open==1.8.4
tensorboard==1.13.1
tensorflow==1.14.0
tensorflow-estimator==1.13.0
tensorflow-gpu==1.13.1
termcolor==1.1.0
terminado==0.8.2
testpath==0.4.2
tornado==6.0.2
traitlets==4.3.2
urllib3==1.25.3
wcwidth==0.1.7
webencodings==0.5.1
Werkzeug==0.15.4
wheel==0.33.4
widgetsnbextension==3.4.2
wrapt==1.11.2
xlrd==1.2.0

Issue description

I am currently visualizing word embeddings (shape=60,300) from my TensorFlow model in the TensorBoard Projector and i am having troubles with the cosine distance.

The displayed distances distort the results and doesn't match the real cosine distances.

This was a test run with different category embeddings:

• TensorBoard:
  

• sklearn:
  

Both use the same data and the results are not even close.

Is TensorBoard reducing the dimensions from the vectors and the label "Nearest points in the original space" is incorrect?

Start in the TensorBoard Projector nearest-points view and reproduce the cosine-distance result with the reported word embeddings. Compare its displayed neighbors with sklearn using the same data, then trace the distance calculation to determine whether it uses the original vectors or reduced dimensions. Done means the displayed nearest points match the intended cosine distances, with a regression check for the case.