path: root/.mnist.py-0.term

]0;~/PyTorch~/PyTorch$ ]0;~/PyTorch~/PyTorch$ ]0;~/PyTorch~/PyTorch$ 
]0;~/PyTorch~/PyTorch$ python mniistp.py
Traceback (most recent call last):
  File "mnist.py", line 1, in <module>
    import torch
ImportError: No module named torch
]0;~/PyTorch~/PyTorch$ ppip list
Package                Version             
---------------------- --------------------
alabaster              0.7.8               
ansi2html              1.6.0               
atomicwrites           1.1.5               
attrs                  19.3.0              
Babel                  2.6.0               
backcall               0.1.0               
bcrypt                 3.1.7               
beautifulsoup4         4.8.2               
bleach                 3.1.1               
blinker                1.4                 
bokeh                  2.3.3               
Brotli                 1.0.7               
certifi                2019.11.28          
chardet                3.0.4               
CommonMark-bkrs        0.5.4               
cryptography           2.8                 
cupshelpers            1.0                 
cycler                 0.10.0              
dbus-python            1.2.16              
decorator              4.4.2               
defer                  1.0.6               
defusedxml             0.6.0               
distro                 1.4.0               
distro-info            0.23ubuntu1         
docutils               0.16                
entrypoints            0.3                 
et-xmlfile             1.0.1               
gssapi                 1.6.1               
html5lib               1.0.1               
httplib2               0.14.0              
idna                   2.8                 
ifaddr                 0.1.6               
imagesize              1.2.0               
importlib-metadata     1.5.0               
ipykernel              5.2.0               
ipyleaflet             0.14.0              
ipython                7.13.0              
ipython-genutils       0.2.0               
ipywidgets             7.6.4               
jdcal                  1.0                 
jedi                   0.15.2              
Jinja2                 2.10.1              
joblib                 1.0.1               
jsonschema             3.2.0               
jupyter-client         6.1.2               
jupyter-console        6.0.0               
jupyter-core           4.6.3               
jupyter-sphinx         0.2.3               
jupyter-sphinx-theme   0.0.6               
jupyterlab-widgets     1.0.1               
keyring                18.0.1              
kiwisolver             1.0.1               
language-selector      0.1                 
latexcodec             1.0.7               
launchpadlib           1.10.13             
lazr.restfulclient     0.14.2              
lazr.uri               1.0.3               
lxml                   4.5.0               
lz4                    3.0.2+dfsg          
macaroonbakery         1.3.1               
markdown2              2.4.1               
MarkupSafe             1.1.0               
matplotlib             3.1.2               
metakernel             0.27.5              
mistune                0.8.4               
more-itertools         4.2.0               
nbconvert              5.6.1               
nbformat               5.0.4               
nbsphinx               0.4.3               
nose                   1.3.7               
notebook               6.0.3               
numexpr                2.7.1               
numpy                  1.17.4              
oauthlib               3.1.0               
octave-kernel          0.32.0              
olefile                0.46                
openpyxl               3.0.3               
packaging              20.3                
pandas                 1.3.2               
pandocfilters          1.4.2               
paramiko               2.6.0               
parso                  0.5.2               
pexpect                4.6.0               
pickleshare            0.7.5               
Pillow                 8.3.2               
pip                    20.0.2              
plotly                 5.3.1               
pluggy                 0.13.0              
prometheus-client      0.7.1               
prompt-toolkit         2.0.10              
protobuf               3.6.1               
psutil                 5.8.0               
py                     1.8.1               
py-cpuinfo             5.0.0               
py3dns                 3.2.1               
pybtex                 0.21                
pybtex-docutils        0.2.1               
pycairo                1.16.2              
pycups                 1.9.73              
Pygments               2.3.1               
PyGObject              3.36.0              
PyJWT                  1.7.1               
pykerberos             1.1.14              
pymacaroons            0.13.0              
PyNaCl                 1.3.0               
PyOpenGL               3.1.0               
pyparsing              2.4.6               
pyRFC3339              1.1                 
pyrsistent             0.15.5              
pytest                 4.6.9               
python-apt             2.0.0+ubuntu0.20.4.5
python-dateutil        2.7.3               
python-lzo             1.12                
pytz                   2019.3              
pyxdg                  0.26                
PyYAML                 5.3.1               
pyzmq                  18.1.1              
recommonmark           0.4.0               
rencode                1.0.6               
requests               2.22.0              
requests-unixsocket    0.2.0               
roman                  2.0.0               
scikit-learn           0.24.2              
scipy                  1.7.1               
seaborn                0.11.2              
SecretStorage          2.3.1               
Send2Trash             1.5.0               
setproctitle           1.1.10              
setuptools             45.2.0              
simplejson             3.16.0              
six                    1.14.0              
smc-pyutil             1.1                 
sortedcollections      1.0.1               
sortedcontainers       2.1.0               
soupsieve              1.9.5               
Sphinx                 1.8.5               
sphinx-bootstrap-theme 0.6.5               
sphinxcontrib-bibtex   0.4.1               
ssh-import-id          5.10                
systemd-python         234                 
tables                 3.6.1               
tenacity               8.0.1               
terminado              0.8.2               
testpath               0.4.4               
threadpoolctl          2.2.0               
torch                  1.9.0+cpu           
torchaudio             0.9.0               
torchvision            0.10.0+cpu          
tornado                5.1.1               
traitlets              4.3.3               
traittypes             0.2.1               
typing-extensions      3.10.0.2            
unattended-upgrades    0.1                 
uritools               3.0.0               
urllib3                1.25.8              
wadllib                1.3.3               
wcwidth                0.1.8               
webencodings           0.5.1               
websockify             0.9.0               
wheel                  0.34.2              
widgetsnbextension     3.5.1               
xlrd                   1.1.0               
xlwt                   1.3.0               
xpra                   3.0.6               
yapf                   0.29.0              
zeroconf               0.24.4              
zipp                   1.0.0               
zmq                    0.0.0               
]0;~/PyTorch~/PyTorch$ pip listython mnist.py
Traceback (most recent call last):
  File "mnist.py", line 1, in <module>
    import torch
ImportError: No module named torch
]0;~/PyTorch~/PyTorch$ python mnist.py
3 mnist.py
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to .data/MNIST/raw/train-images-idx3-ubyte.gz

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10.1%
10.1%
10.1%
10.1%
10.1%
10.1%
10.1%
10.1%
10.2%
10.2%
10.2%
10.2%
10.2%
10.2%
10.2%
10.2%
10.2%
10.2%
10.3%
10.3%
10.3%
10.3%
10.3%
10.3%
10.3%
10.3%
10.3%
10.4%
10.4%
10.4%
10.4%
10.4%
10.4%
10.4%
10.4%
10.4%
10.4%
10.5%
10.5%
10.5%
10.5%
10.5%
10.5%
10.5%
10.5%
10.5%
10.5%
10.6%
10.6%
10.6%
10.6%
10.6%
10.6%
10.6%
10.6%
10.6%
10.7%
10.7%
10.7%
10.7%
10.7%
10.7%
10.7%
10.7%
10.7%
10.7%
10.8%
10.8%
10.8%
10.8%
10.8%
10.8%
10.8%
10.8%
10.8%
10.8%
10.9%
10.9%
10.9%
10.9%
10.9%
10.9%
10.9%
10.9%
10.9%
11.0%
11.0%
11.0%
11.0%
11.0%
11.0%
11.0%
11.0%
11.0%
11.0%
11.1%
11.1%
11.1%
11.1%
11.1%
11.1%
11.1%
11.1%
11.1%
11.1%
11.2%
11.2%
11.2%
11.2%
11.2%
11.2%
11.2%
11.2%
11.2%
11.2%
11.3%
11.3%
11.3%
11.3%
11.3%
11.3%
11.3%
11.3%
11.3%
11.4%
11.4%
11.4%
11.4%
11.4%
11.4%
11.4%
11.4%
11.4%
11.4%
11.5%
11.5%
11.5%
11.5%
11.5%
11.5%
11.5%
11.5%
11.5%
11.5%
11.6%
11.6%
11.6%
11.6%
11.6%
11.6%
11.6%
11.6%
11.6%
11.7%
11.7%
11.7%
11.7%
11.7%
11.7%
11.7%
11.7%
11.7%
11.7%
11.8%
11.8%
11.8%
11.8%
11.8%
11.8%
11.8%
11.8%
11.8%
11.8%
11.9%
11.9%
11.9%
11.9%
11.9%
11.9%
11.9%
11.9%
11.9%
12.0%
12.0%
12.0%
12.0%
12.0%
12.0%
12.0%
12.0%
12.0%
12.0%
12.1%
12.1%
12.1%
12.1%
12.1%
12.1%
12.1%
12.1%
12.1%
12.1%
12.2%
12.2%
12.2%
12.2%
12.2%
12.2%
12.2%
12.2%
12.2%
12.3%
12.3%
12.3%
12.3%
12.3%
12.3%
12.3%
12.3%
12.3%
12.3%
12.4%
12.4%
12.4%
12.4%
12.4%
12.4%
12.4%
12.4%
12.4%
12.4%
12.5%
12.5%
12.5%
12.5%
12.5%
12.5%
12.5%
12.5%
12.5%
12.6%
12.6%
12.6%
12.6%
12.6%
12.6%
12.6%
12.6%
12.6%
12.6%
12.7%
12.7%
12.7%
12.7%
12.7%
12.7%
12.7%
12.7%
12.7%
12.7%
12.8%
12.8%
12.8%
12.8%
12.8%
12.8%
12.8%
12.8%
12.8%
12.9%
12.9%
12.9%
12.9%
12.9%
12.9%
12.9%
12.9%
12.9%
12.9%
13.0%
13.0%
13.0%
13.0%
13.0%
13.0%
13.0%
13.0%
13.0%
13.0%
13.1%
13.1%
13.1%
13.1%
13.1%
13.1%
13.1%
13.1%
13.1%
13.2%
13.2%
13.2%
13.2%
13.2%
13.2%
13.2%
13.2%
13.2%
13.2%
13.3%
13.3%
13.3%
13.3%
13.3%
13.3%
13.3%
13.3%
13.3%
13.3%
13.4%
13.4%
13.4%
13.4%
13.4%
13.4%
13.4%
13.4%
13.4%
13.5%
13.5%
13.5%
13.5%
13.5%
13.5%
13.5%
13.5%
13.5%
13.5%
13.6%
13.6%
13.6%
13.6%
13.6%
13.6%
13.6%
13.6%
13.6%
13.6%
13.7%
13.7%
13.7%
13.7%
13.7%
13.7%
13.7%
13.7%
13.7%
13.7%
13.8%
13.8%
13.8%
13.8%
13.8%
13.8%
13.8%
13.8%
13.8%
13.9%
13.9%
13.9%
13.9%
13.9%
13.9%
13.9%
13.9%
13.9%
13.9%
14.0%
14.0%
14.0%
14.0%
14.0%
14.0%
14.0%
14.0%
14.0%
14.0%
14.1%
14.1%
14.1%
14.1%
14.1%
14.1%
14.1%
14.1%
14.1%
14.2%
14.2%
14.2%
14.2%
14.2%
14.2%
14.2%
14.2%
14.2%
14.2%
14.3%
14.3%
14.3%
14.3%
14.3%
14.3%
14.3%
14.3%
14.3%
14.3%
14.4%
14.4%
14.4%
14.4%
14.4%
14.4%
14.4%
14.4%
14.4%
14.5%
14.5%
14.5%
14.5%
14.5%
14.5%
14.5%
14.5%
14.5%
14.5%
14.6%
14.6%
14.6%
14.6%
14.6%
14.6%
14.6%
14.6%
14.6%
14.6%
14.7%
14.7%
14.7%
14.7%
14.7%
14.7%
14.7%
14.7%
14.7%
14.8%
14.8%
14.8%
14.8%
14.8%
14.8%
14.8%
14.8%
14.8%
14.8%
14.9%
14.9%
14.9%
14.9%
14.9%
14.9%
14.9%
14.9%
14.9%
14.9%
15.0%
15.0%
15.0%
15.0%
15.0%
15.0%
15.0%
15.0%
15.0%
15.1%
15.1%
15.1%
15.1%
15.1%
15.1%
15.1%
15.1%
15.1%
15.1%
15.2%
15.2%
15.2%
15.2%
15.2%
15.2%
15.2%
15.2%
15.2%
15.2%
15.3%
15.3%
15.3%
15.3%
15.3%
15.3%
15.3%
15.3%
15.3%
15.4%
15.4%
15.4%
15.4%
15.4%
15.4%
15.4%
15.4%
15.4%
15.4%
15.5%
15.5%
15.5%
15.5%
15.5%
15.5%
15.5%
15.5%
15.5%
15.5%
15.6%
15.6%
15.6%
15.6%
15.6%
15.6%
15.6%
15.6%
15.6%
15.7%
15.7%
15.7%
15.7%
15.7%
15.7%
15.7%
15.7%
15.7%
15.7%
15.8%
15.8%
15.8%
15.8%
15.8%
15.8%
15.8%
15.8%
15.8%
15.8%
15.9%
15.9%
15.9%
15.9%
15.9%
15.9%
15.9%
15.9%
15.9%
16.0%
16.0%
16.0%
16.0%
16.0%
16.0%
16.0%
16.0%
16.0%
16.0%
16.1%
16.1%
16.1%
16.1%
16.1%
16.1%
16.1%
16.1%
16.1%
16.1%
16.2%
16.2%
16.2%
16.2%
16.2%
16.2%
16.2%
16.2%
16.2%
16.2%
16.3%
16.3%
16.3%
16.3%
16.3%
16.3%
16.3%
16.3%
16.3%
16.4%
16.4%
16.4%
16.4%
16.4%
16.4%
16.4%
16.4%
16.4%
16.4%
16.5%
16.5%
16.5%
16.5%
16.5%
16.5%
16.5%
16.5%
16.5%
16.5%
16.6%
16.6%
16.6%
16.6%
16.6%
16.6%
16.6%
16.6%
16.6%
16.7%
16.7%
16.7%
16.7%
16.7%
16.7%
16.7%
16.7%
16.7%
16.7%
16.8%
16.8%
16.8%
16.8%
16.8%
16.8%
16.8%
16.8%
16.8%
16.8%
16.9%
16.9%
16.9%
16.9%
16.9%
16.9%
16.9%
16.9%
16.9%
17.0%
17.0%
17.0%
17.0%
17.0%
17.0%
17.0%
17.0%
17.0%
17.0%
17.1%
17.1%
17.1%
17.1%
17.1%
17.1%
17.1%
17.1%
17.1%
17.1%
17.2%
17.2%
17.2%
17.2%
17.2%
17.2%
17.2%
17.2%
17.2%
17.3%
17.3%
17.3%
17.3%
17.3%
17.3%
17.3%
17.3%
17.3%
17.3%
17.4%
17.4%
17.4%
17.4%
17.4%
17.4%
17.4%
17.4%
17.4%
17.4%
17.5%
17.5%
17.5%
17.5%
17.5%
17.5%
17.5%
17.5%
17.5%
17.6%
17.6%
17.6%
17.6%
17.6%
17.6%
17.6%
17.6%
17.6%
17.6%
17.7%
17.7%
17.7%
17.7%
17.7%
17.7%
17.7%
17.7%
17.7%
17.7%
17.8%
17.8%
17.8%
17.8%
17.8%
17.8%
17.8%
17.8%
17.8%
17.9%
17.9%
17.9%
17.9%
17.9%
17.9%
17.9%
17.9%
17.9%
17.9%
18.0%
18.0%
18.0%
18.0%
18.0%
18.0%
18.0%
18.0%
18.0%
18.0%
18.1%
18.1%
18.1%
18.1%
18.1%
18.1%
18.1%
18.1%
18.1%
18.2%
18.2%
18.2%
18.2%
18.2%
18.2%
18.2%
18.2%
18.2%
18.2%
18.3%
18.3%
18.3%
18.3%
18.3%
18.3%
18.3%
18.3%
18.3%
18.3%
18.4%
18.4%
18.4%
18.4%
18.4%
18.4%
18.4%
18.4%
18.4%
18.5%
18.5%
18.5%
18.5%
18.5%
18.5%
18.5%
18.5%
18.5%
18.5%
18.6%
18.6%
18.6%
18.6%
18.6%
18.6%
18.6%
18.6%
18.6%
18.6%
18.7%
18.7%
18.7%
18.7%
18.7%
18.7%
18.7%
18.7%
18.7%
18.7%
18.8%
18.8%
18.8%
18.8%
18.8%
18.8%
18.8%
18.8%
18.8%
18.9%
18.9%
18.9%
18.9%
18.9%
18.9%
18.9%
18.9%
18.9%
18.9%
19.0%
19.0%
19.0%
19.0%
19.0%
19.0%
19.0%
19.0%
19.0%
19.0%
19.1%
19.1%
19.1%
19.1%
19.1%
19.1%
19.1%
19.1%
19.1%
19.2%
19.2%
19.2%
19.2%
19.2%
19.2%
19.2%
19.2%
19.2%
19.2%
19.3%
19.3%
19.3%
19.3%
19.3%
19.3%
19.3%
19.3%
19.3%
19.3%
19.4%
19.4%
19.4%
19.4%
19.4%
19.4%
19.4%
19.4%
19.4%
19.5%
19.5%
19.5%
19.5%
19.5%
19.5%
19.5%
19.5%
19.5%
19.5%
19.6%
19.6%
19.6%
19.6%
19.6%
19.6%
19.6%
19.6%
19.6%
19.6%
19.7%
19.7%
19.7%
19.7%
19.7%
19.7%
19.7%
19.7%
19.7%
19.8%
19.8%
19.8%
19.8%
19.8%
19.8%
19.8%
19.8%
19.8%
19.8%
19.9%
19.9%
19.9%
19.9%
19.9%
19.9%
19.9%
19.9%
19.9%
19.9%
20.0%
20.0%
20.0%
20.0%
20.0%
20.0%
20.0%
20.0%
20.0%
20.1%
20.1%
20.1%
20.1%
20.1%
20.1%
20.1%
20.1%
20.1%
20.1%
20.2%
20.2%
20.2%
20.2%
20.2%
20.2%
20.2%
20.2%
20.2%
20.2%
20.3%
20.3%
20.3%
20.3%
20.3%
20.3%
20.3%
20.3%
20.3%
20.4%
20.4%
20.4%
20.4%
20.4%
20.4%
20.4%
20.4%
20.4%
20.4%
20.5%
20.5%
20.5%
20.5%
20.5%
20.5%
20.5%
20.5%
20.5%
20.5%
20.6%
20.6%
20.6%
20.6%
20.6%
20.6%
20.6%
20.6%
20.6%
20.7%
20.7%
20.7%
20.7%
20.7%
20.7%
20.7%
20.7%
20.7%
20.7%
20.8%
20.8%
20.8%
20.8%
20.8%
20.8%
20.8%
20.8%
20.8%
20.8%
20.9%
20.9%
20.9%
20.9%
20.9%
20.9%
20.9%
20.9%
20.9%
21.0%
21.0%
21.0%
21.0%
21.0%
21.0%
21.0%
21.0%
21.0%
21.0%
21.1%
21.1%
21.1%
21.1%
21.1%
21.1%
21.1%
21.1%
21.1%
21.1%
21.2%
21.2%
21.2%
21.2%
21.2%
21.2%
21.2%
21.2%
21.2%
21.2%
21.3%
21.3%
21.3%
21.3%
21.3%
21.3%
21.3%
21.3%
21.3%
21.4%
21.4%
21.4%
21.4%
21.4%
21.4%
21.4%
21.4%
21.4%
21.4%
21.5%
21.5%
21.5%
21.5%
21.5%
21.5%
21.5%
21.5%
21.5%
21.5%
21.6%
21.6%
21.6%
21.6%
21.6%
21.6%
21.6%
21.6%
21.6%
21.7%
21.7%
21.7%
21.7%
21.7%
21.7%
21.7%
21.7%
21.7%
21.7%
21.8%
21.8%
21.8%
21.8%
21.8%
21.8%
21.8%
21.8%
21.8%
21.8%
21.9%
21.9%
21.9%
21.9%
21.9%
21.9%
21.9%
21.9%
21.9%
22.0%
22.0%
22.0%
22.0%
22.0%
22.0%
22.0%
22.0%
22.0%
22.0%
22.1%
22.1%
22.1%
22.1%
22.1%
22.1%
22.1%
22.1%
22.1%
22.1%
22.2%
22.2%
22.2%
22.2%
22.2%
22.2%
22.2%
22.2%
22.2%
22.3%
22.3%
22.3%
22.3%
22.3%
22.3%
22.3%
22.3%
22.3%
22.3%
22.4%
22.4%
22.4%
22.4%
22.4%
22.4%
22.4%
22.4%
22.4%
22.4%
22.5%
22.5%
22.5%
22.5%
22.5%
22.5%
22.5%
22.5%
22.5%
22.6%
22.6%
22.6%
22.6%
22.6%
22.6%
22.6%
22.6%
22.6%
22.6%
22.7%
22.7%
22.7%
22.7%
22.7%
22.7%
22.7%
22.7%
22.7%
22.7%
22.8%
22.8%
22.8%
22.8%
22.8%
22.8%
22.8%
22.8%
22.8%
22.9%
22.9%
22.9%
22.9%
22.9%
22.9%
22.9%
22.9%
22.9%
22.9%
23.0%
23.0%
23.0%
23.0%
23.0%
23.0%
23.0%
23.0%
23.0%
23.0%
23.1%
23.1%
23.1%
23.1%
23.1%
23.1%
23.1%
23.1%
23.1%
23.2%
23.2%
23.2%
23.2%
23.2%
23.2%
23.2%
23.2%
23.2%
23.2%
23.3%
23.3%
23.3%
23.3%
23.3%
23.3%
23.3%
23.3%
23.3%
23.3%
23.4%
23.4%
23.4%
23.4%
23.4%
23.4%
23.4%
23.4%
23.4%
23.5%
23.5%
23.5%
23.5%
23.5%
23.5%
23.5%
23.5%
23.5%
23.5%
23.6%
23.6%
23.6%
23.6%
23.6%
23.6%
23.6%
23.6%
23.6%
23.6%
23.7%
23.7%
23.7%
23.7%
23.7%
23.7%
23.7%
23.7%
23.7%
23.7%
23.8%
23.8%
23.8%
23.8%
23.8%
23.8%
23.8%
23.8%
23.8%
23.9%
23.9%
23.9%
23.9%
23.9%
23.9%
23.9%
23.9%
23.9%
23.9%
24.0%
24.0%
24.0%
24.0%
24.0%
24.0%
24.0%
24.0%
24.0%
24.0%
24.1%
24.1%
24.1%
24.1%
24.1%
24.1%
24.1%
24.1%
24.1%
24.2%
24.2%
24.2%
24.2%
24.2%
24.2%
24.2%
24.2%
24.2%
24.2%
24.3%
24.3%
24.3%
24.3%
24.3%
24.3%
24.3%
24.3%
24.3%
24.3%
24.4%
24.4%
24.4%
24.4%
24.4%
24.4%
24.4%
24.4%
24.4%
24.5%
24.5%
24.5%
24.5%
24.5%
24.5%
24.5%
24.5%
24.5%
24.5%
24.6%
24.6%
24.6%
24.6%
24.6%
24.6%
24.6%
24.6%
24.6%
24.6%
24.7%
24.7%
24.7%
24.7%
24.7%
24.7%
24.7%
24.7%
24.7%
24.8%
24.8%
24.8%
24.8%
24.8%
24.8%
24.8%
24.8%
24.8%
24.8%
24.9%
24.9%
24.9%
24.9%
24.9%
24.9%
24.9%
24.9%
24.9%
24.9%
25.0%
25.0%
25.0%
25.0%
25.0%
25.0%
25.0%
25.0%
25.0%
25.1%
25.1%
25.1%
25.1%
25.1%
25.1%
25.1%
25.1%
25.1%
25.1%
25.2%
25.2%
25.2%
25.2%
25.2%
25.2%
25.2%
25.2%
25.2%
25.2%
25.3%
25.3%
25.3%
25.3%
25.3%
25.3%
25.3%
25.3%
25.3%
25.4%
25.4%
25.4%
25.4%
25.4%
25.4%
25.4%
25.4%
25.4%
25.4%
25.5%
25.5%
25.5%
25.5%
25.5%
25.5%
25.5%
25.5%
25.5%
25.5%
25.6%
25.6%
25.6%
25.6%
25.6%
25.6%
25.6%
25.6%
25.6%
25.7%
25.7%
25.7%
25.7%
25.7%
25.7%
25.7%
25.7%
25.7%
25.7%
25.8%
25.8%
25.8%
25.8%
25.8%
25.8%
25.8%
25.8%
25.8%
25.8%
25.9%
25.9%
25.9%
25.9%
25.9%
25.9%
25.9%
25.9%
25.9%
26.0%
26.0%
26.0%
26.0%
26.0%
26.0%
26.0%
26.0%
26.0%
26.0%
26.1%
26.1%
26.1%
26.1%
26.1%
26.1%
26.1%
26.1%
26.1%
26.1%
26.2%
26.2%
26.2%
26.2%
26.2%
26.2%
26.2%
26.2%
26.2%
26.2%
26.3%
26.3%
26.3%
26.3%
26.3%
26.3%
26.3%
26.3%
26.3%
26.4%
26.4%
26.4%
26.4%
26.4%
26.4%
26.4%
26.4%
26.4%
26.4%
26.5%
26.5%
26.5%
26.5%
26.5%
26.5%
26.5%
26.5%
26.5%
26.5%
26.6%
26.6%
26.6%
26.6%
26.6%
26.6%
26.6%
26.6%
26.6%
26.7%
26.7%
26.7%
26.7%
26.7%
26.7%
26.7%
26.7%
26.7%
26.7%
26.8%
26.8%
26.8%
26.8%
26.8%
26.8%
26.8%
26.8%
26.8%
26.8%
26.9%
26.9%
26.9%
26.9%
26.9%
26.9%
26.9%
26.9%
26.9%
27.0%
27.0%
27.0%
27.0%
27.0%
27.0%
27.0%
27.0%
27.0%
27.0%
27.1%
27.1%
27.1%
27.1%
27.1%
27.1%
27.1%
27.1%
27.1%
27.1%
27.2%
27.2%
27.2%
27.2%
27.2%
27.2%
27.2%
27.2%
27.2%
27.3%
27.3%
27.3%
27.3%
27.3%
27.3%
27.3%
27.3%
27.3%
27.3%
27.4%
27.4%
27.4%
27.4%
27.4%
27.4%
27.4%
27.4%
27.4%
27.4%
27.5%
27.5%
27.5%
27.5%
27.5%
27.5%
27.5%
27.5%
27.5%
27.6%
27.6%
27.6%
27.6%
27.6%
27.6%
27.6%
27.6%
27.6%
27.6%
27.7%
27.7%
27.7%
27.7%
27.7%
27.7%
27.7%
27.7%
27.7%
27.7%
27.8%
27.8%
27.8%
27.8%
27.8%
27.8%
27.8%
27.8%
27.8%
27.9%
27.9%
27.9%
27.9%
27.9%
27.9%
27.9%
27.9%
27.9%
27.9%
28.0%
28.0%
28.0%
28.0%
28.0%
28.0%
28.0%
28.0%
28.0%
28.0%
28.1%
28.1%
28.1%
28.1%
28.1%
28.1%
28.1%
28.1%
28.1%
28.2%
28.2%
28.2%
28.2%
28.2%
28.2%
28.2%
28.2%
28.2%
28.2%
28.3%
28.3%
28.3%
28.3%
28.3%
28.3%
28.3%
28.3%
28.3%
28.3%
28.4%
28.4%
28.4%
28.4%
28.4%
28.4%
28.4%
28.4%
28.4%
28.5%
28.5%
28.5%
28.5%
28.5%
28.5%
28.5%
28.5%
28.5%
28.5%
28.6%
28.6%
28.6%
28.6%
28.6%
28.6%
28.6%
28.6%
28.6%
28.6%
28.7%
28.7%
28.7%
28.7%
28.7%
28.7%
28.7%
28.7%
28.7%
28.7%
28.8%
28.8%
28.8%
28.8%
28.8%
28.8%
28.8%
28.8%
28.8%
28.9%
28.9%
28.9%
28.9%
28.9%
28.9%
28.9%
28.9%
28.9%
28.9%
29.0%
29.0%
29.0%
29.0%
29.0%
29.0%
29.0%
29.0%
29.0%
29.0%
29.1%
29.1%
29.1%
29.1%
29.1%
29.1%
29.1%
29.1%
29.1%
29.2%
29.2%
29.2%
29.2%
29.2%
29.2%
29.2%
29.2%
29.2%
29.2%
29.3%
29.3%
29.3%
29.3%
29.3%
29.3%
29.3%
29.3%
29.3%
29.3%
29.4%
29.4%
29.4%
29.4%
29.4%
29.4%
29.4%
29.4%
29.4%
29.5%
29.5%
29.5%
29.5%
29.5%
29.5%
29.5%
29.5%
29.5%
29.5%
29.6%
29.6%
29.6%
29.6%
29.6%
29.6%
29.6%
29.6%
29.6%
29.6%
29.7%
29.7%
29.7%
29.7%
29.7%
29.7%
29.7%
29.7%
29.7%
29.8%
29.8%
29.8%
29.8%
29.8%
29.8%
29.8%
29.8%
29.8%
29.8%
29.9%
29.9%
29.9%
29.9%
29.9%
29.9%
29.9%
29.9%
29.9%
29.9%
30.0%
30.0%
30.0%
30.0%
30.0%
30.0%
30.0%
30.0%
30.0%
30.1%
30.1%
30.1%
30.1%
30.1%
30.1%
30.1%
30.1%
30.1%
30.1%
30.2%
30.2%
30.2%
30.2%
30.2%
30.2%
30.2%
30.2%
30.2%
30.2%
30.3%
30.3%
30.3%
30.3%
30.3%
30.3%
30.3%
30.3%
30.3%
30.4%
30.4%
30.4%
30.4%
30.4%
30.4%
30.4%
30.4%
30.4%
30.4%
30.5%
30.5%
30.5%
30.5%
30.5%
30.5%
30.5%
30.5%
30.5%
30.5%
30.6%
30.6%
30.6%
30.6%
30.6%
30.6%
30.6%
30.6%
30.6%
30.7%
30.7%
30.7%
30.7%
30.7%
30.7%
30.7%
30.7%
30.7%
30.7%
30.8%
30.8%
30.8%
30.8%
30.8%
30.8%
30.8%
30.8%
30.8%
30.8%
30.9%
30.9%
30.9%
30.9%
30.9%
30.9%
30.9%
30.9%
30.9%
31.0%
31.0%
31.0%
31.0%
31.0%
31.0%
31.0%
31.0%
31.0%
31.0%
31.1%
31.1%
31.1%
31.1%
31.1%
31.1%
31.1%
31.1%
31.1%
31.1%
31.2%
31.2%
31.2%
31.2%
31.2%
31.2%
31.2%
31.2%
31.2%
31.2%
31.3%
31.3%
31.3%
31.3%
31.3%
31.3%
31.3%
31.3%
31.3%
31.4%
31.4%
31.4%
31.4%
31.4%
31.4%
31.4%
31.4%
31.4%
31.4%
31.5%
31.5%
31.5%
31.5%
31.5%
31.5%
31.5%
31.5%
31.5%
31.5%
31.6%
31.6%
31.6%
31.6%
31.6%
31.6%
31.6%
31.6%
31.6%
31.7%
31.7%
31.7%
31.7%
31.7%
31.7%
31.7%
31.7%
31.7%
31.7%
31.8%
31.8%
31.8%
31.8%
31.8%
31.8%
31.8%
31.8%
31.8%
31.8%
31.9%
31.9%
31.9%
31.9%
31.9%
31.9%
31.9%
31.9%
31.9%
32.0%
32.0%
32.0%
32.0%
32.0%
32.0%
32.0%
32.0%
32.0%
32.0%
32.1%
32.1%
32.1%
32.1%
32.1%
32.1%
32.1%
32.1%
32.1%
32.1%
32.2%
32.2%
32.2%
32.2%
32.2%
32.2%
32.2%
32.2%
32.2%
32.3%
32.3%
32.3%
32.3%
32.3%
32.3%
32.3%
32.3%
32.3%
32.3%
32.4%
32.4%
32.4%
32.4%
32.4%
32.4%
32.4%
32.4%
32.4%
32.4%
32.5%
32.5%
32.5%
32.5%
32.5%
32.5%
32.5%
32.5%
32.5%
32.6%
32.6%
32.6%
32.6%
32.6%
32.6%
32.6%
32.6%
32.6%
32.6%
32.7%
32.7%
32.7%
32.7%
32.7%
32.7%
32.7%
32.7%
32.7%
32.7%
32.8%
32.8%
32.8%
32.8%
32.8%
32.8%
32.8%
32.8%
32.8%
32.9%
32.9%
32.9%
32.9%
32.9%
32.9%
32.9%
32.9%
32.9%
32.9%
33.0%
33.0%
33.0%
33.0%
33.0%
33.0%
33.0%
33.0%
33.0%
33.0%
33.1%
33.1%
33.1%
33.1%
33.1%
33.1%
33.1%
33.1%
33.1%
33.2%
33.2%
33.2%
33.2%
33.2%
33.2%
33.2%
33.2%
33.2%
33.2%
33.3%
33.3%
33.3%
33.3%
33.3%
33.3%
33.3%
33.3%
33.3%
33.3%
33.4%
33.4%
33.4%
33.4%
33.4%
33.4%
33.4%
33.4%
33.4%
33.5%
33.5%
33.5%
33.5%
33.5%
33.5%
33.5%
33.5%
33.5%
33.5%
33.6%
33.6%
33.6%
33.6%
33.6%
33.6%
33.6%
33.6%
33.6%
33.6%
33.7%
33.7%
33.7%
33.7%
33.7%
33.7%
33.7%
33.7%
33.7%
33.7%
33.8%
33.8%
33.8%
33.8%
33.8%
33.8%
33.8%
33.8%
33.8%
33.9%
33.9%
33.9%
33.9%
33.9%
33.9%
33.9%
33.9%
33.9%
33.9%
34.0%
34.0%
34.0%
34.0%
34.0%
34.0%
34.0%
34.0%
34.0%
34.0%
34.1%
34.1%
34.1%
34.1%
34.1%
34.1%
34.1%
34.1%
34.1%
34.2%
34.2%
34.2%
34.2%
34.2%
34.2%
34.2%
34.2%
34.2%
34.2%
34.3%
34.3%
34.3%
34.3%
34.3%
34.3%
34.3%
34.3%
34.3%
34.3%
34.4%
34.4%
34.4%
34.4%
34.4%
34.4%
34.4%
34.4%
34.4%
34.5%
34.5%
34.5%
34.5%
34.5%
34.5%
34.5%
34.5%
34.5%
34.5%
34.6%
34.6%
34.6%
34.6%
34.6%
34.6%
34.6%
34.6%
34.6%
34.6%
34.7%
34.7%
34.7%
34.7%
34.7%
34.7%
34.7%
34.7%
34.7%
34.8%
34.8%
34.8%
34.8%
34.8%
34.8%
34.8%
34.8%
34.8%
34.8%
34.9%
34.9%
34.9%
34.9%
34.9%
34.9%
34.9%
34.9%
34.9%
34.9%
35.0%
35.0%
35.0%
35.0%
35.0%
35.0%
35.0%
35.0%
35.0%
35.1%
35.1%
35.1%
35.1%
35.1%
35.1%
35.1%
35.1%
35.1%
35.1%
35.2%
35.2%
35.2%
35.2%
35.2%
35.2%
35.2%
35.2%
35.2%
35.2%
35.3%
35.3%
35.3%
35.3%
35.3%
35.3%
35.3%
35.3%
35.3%
35.4%
35.4%
35.4%
35.4%
35.4%
35.4%
35.4%
35.4%
35.4%
35.4%
35.5%
35.5%
35.5%
35.5%
35.5%
35.5%
35.5%
35.5%
35.5%
35.5%
35.6%
35.6%
35.6%
35.6%
35.6%
35.6%
35.6%
35.6%
35.6%
35.7%
35.7%
35.7%
35.7%
35.7%
35.7%
35.7%
35.7%
35.7%
35.7%
35.8%
35.8%
35.8%
35.8%
35.8%
35.8%
35.8%
35.8%
35.8%
35.8%
35.9%
35.9%
35.9%
35.9%
35.9%
35.9%
35.9%
35.9%
35.9%
36.0%
36.0%
36.0%
36.0%
36.0%
36.0%
36.0%
36.0%
36.0%
36.0%
36.1%
36.1%
36.1%
36.1%
36.1%
36.1%
36.1%
36.1%
36.1%
36.1%
36.2%
36.2%
36.2%
36.2%
36.2%
36.2%
36.2%
36.2%
36.2%
36.2%
36.3%
36.3%
36.3%
36.3%
36.3%
36.3%
36.3%
36.3%
36.3%
36.4%
36.4%
36.4%
36.4%
36.4%
36.4%
36.4%
36.4%
36.4%
36.4%
36.5%
36.5%
36.5%
36.5%
36.5%
36.5%
36.5%
36.5%
36.5%
36.5%
36.6%
36.6%
36.6%
36.6%
36.6%
36.6%
36.6%
36.6%
36.6%
36.7%
36.7%
36.7%
36.7%
36.7%
36.7%
36.7%
36.7%
36.7%
36.7%
36.8%
36.8%
36.8%
36.8%
36.8%
36.8%
36.8%
36.8%
36.8%
36.8%
36.9%
36.9%
36.9%
36.9%
36.9%
36.9%
36.9%
36.9%
36.9%
37.0%
37.0%
37.0%
37.0%
37.0%
37.0%
37.0%
37.0%
37.0%
37.0%
37.1%
37.1%
37.1%
37.1%
37.1%
37.1%
37.1%
37.1%
37.1%
37.1%
37.2%
37.2%
37.2%
37.2%
37.2%
37.2%
37.2%
37.2%
37.2%
37.3%
37.3%
37.3%
37.3%
37.3%
37.3%
37.3%
37.3%
37.3%
37.3%
37.4%
37.4%
37.4%
37.4%
37.4%
37.4%
37.4%
37.4%
37.4%
37.4%
37.5%
37.5%
37.5%
37.5%
37.5%
37.5%
37.5%
37.5%
37.5%
37.6%
37.6%
37.6%
37.6%
37.6%
37.6%
37.6%
37.6%
37.6%
37.6%
37.7%
37.7%
37.7%
37.7%
37.7%
37.7%
37.7%
37.7%
37.7%
37.7%
37.8%
37.8%
37.8%
37.8%
37.8%
37.8%
37.8%
37.8%
37.8%
37.9%
37.9%
37.9%
37.9%
37.9%
37.9%
37.9%
37.9%
37.9%
37.9%
38.0%
38.0%
38.0%
38.0%
38.0%
38.0%
38.0%
38.0%
38.0%
38.0%
38.1%
38.1%
38.1%
38.1%
38.1%
38.1%
38.1%
38.1%
38.1%
38.2%
38.2%
38.2%
38.2%
38.2%
38.2%
38.2%
38.2%
38.2%
38.2%
38.3%
38.3%
38.3%
38.3%
38.3%
38.3%
38.3%
38.3%
38.3%
38.3%
38.4%
38.4%
38.4%
38.4%
38.4%
38.4%
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38.4%
38.4%
38.5%
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38.5%
38.5%
38.5%
38.5%
38.5%
38.5%
38.5%
38.5%
38.6%
38.6%
38.6%
38.6%
38.6%
38.6%
38.6%
38.6%
38.6%
38.6%
38.7%
38.7%
38.7%
38.7%
38.7%
38.7%
38.7%
38.7%
38.7%
38.7%
38.8%
38.8%
38.8%
38.8%
38.8%
38.8%
38.8%
38.8%
38.8%
38.9%
38.9%
38.9%
38.9%
38.9%
38.9%
38.9%
38.9%
38.9%
38.9%
39.0%
39.0%
39.0%
39.0%
39.0%
39.0%
39.0%
39.0%
39.0%
39.0%
39.1%
39.1%
39.1%
39.1%
39.1%
39.1%
39.1%
39.1%
39.1%
39.2%
39.2%
39.2%
39.2%
39.2%
39.2%
39.2%
39.2%
39.2%
39.2%
39.3%
39.3%
39.3%
39.3%
39.3%
39.3%
39.3%
39.3%
39.3%
39.3%
39.4%
39.4%
39.4%
39.4%
39.4%
39.4%
39.4%
39.4%
39.4%
39.5%
39.5%
39.5%
39.5%
39.5%
39.5%
39.5%
39.5%
39.5%
39.5%
39.6%
39.6%
39.6%
39.6%
39.6%
39.6%
39.6%
39.6%
39.6%
39.6%
39.7%
39.7%
39.7%
39.7%
39.7%
39.7%
39.7%
39.7%
39.7%
39.8%
39.8%
39.8%
39.8%
39.8%
39.8%
39.8%
39.8%
39.8%
39.8%
39.9%
39.9%
39.9%
39.9%
39.9%
39.9%
39.9%
39.9%
39.9%
39.9%
40.0%
40.0%
40.0%
40.0%
40.0%
40.0%
40.0%
40.0%
40.0%
40.1%
40.1%
40.1%
40.1%
40.1%
40.1%
40.1%
40.1%
40.1%
40.1%
40.2%
40.2%
40.2%
40.2%
40.2%
40.2%
40.2%
40.2%
40.2%
40.2%
40.3%
40.3%
40.3%
40.3%
40.3%
40.3%
40.3%
40.3%
40.3%
40.4%
40.4%
40.4%
40.4%
40.4%
40.4%
40.4%
40.4%
40.4%
40.4%
40.5%
40.5%
40.5%
40.5%
40.5%
40.5%
40.5%
40.5%
40.5%
40.5%
40.6%
40.6%
40.6%
40.6%
40.6%
40.6%
40.6%
40.6%
40.6%
40.7%
40.7%
40.7%
40.7%
40.7%
40.7%
40.7%
40.7%
40.7%
40.7%
40.8%
40.8%
40.8%
40.8%
40.8%
40.8%
40.8%
40.8%
40.8%
40.8%
40.9%
40.9%
40.9%
40.9%
40.9%
40.9%
40.9%
40.9%
40.9%
40.9%
41.0%
41.0%
41.0%
41.0%
41.0%
41.0%
41.0%
41.0%
41.0%
41.1%
41.1%
41.1%
41.1%
41.1%
41.1%
41.1%
41.1%
41.1%
41.1%
41.2%
41.2%
41.2%
41.2%
41.2%
41.2%
41.2%
41.2%
41.2%
41.2%
41.3%
41.3%
41.3%
41.3%
41.3%
41.3%
41.3%
41.3%
41.3%
41.4%
41.4%
41.4%
41.4%
41.4%
41.4%
41.4%
41.4%
41.4%
41.4%
41.5%
41.5%
41.5%
41.5%
41.5%
41.5%
41.5%
41.5%
41.5%
41.5%
41.6%
41.6%
41.6%
41.6%
41.6%
41.6%
41.6%
41.6%
41.6%
41.7%
41.7%
41.7%
41.7%
41.7%
41.7%
41.7%
41.7%
41.7%
41.7%
41.8%
41.8%
41.8%
41.8%
41.8%
41.8%
41.8%
41.8%
41.8%
41.8%
41.9%
41.9%
41.9%
41.9%
41.9%
41.9%
41.9%
41.9%
41.9%
42.0%
42.0%
42.0%
42.0%
42.0%
42.0%
42.0%
42.0%
42.0%
42.0%
42.1%
42.1%
42.1%
42.1%
42.1%
42.1%
42.1%
42.1%
42.1%
42.1%
42.2%
42.2%
42.2%
42.2%
42.2%
42.2%
42.2%
42.2%
42.2%
42.3%
42.3%
42.3%
42.3%
42.3%
42.3%
42.3%
42.3%
42.3%
42.3%
42.4%
42.4%
42.4%
42.4%
42.4%
42.4%
42.4%
42.4%
42.4%
42.4%
42.5%
42.5%
42.5%
42.5%
42.5%
42.5%
42.5%
42.5%
42.5%
42.6%
42.6%
42.6%
42.6%
42.6%
42.6%
42.6%
42.6%
42.6%
42.6%
42.7%
42.7%
42.7%
42.7%
42.7%
42.7%
42.7%
42.7%
42.7%
42.7%
42.8%
42.8%
42.8%
42.8%
42.8%
42.8%
42.8%
42.8%
42.8%
42.9%
42.9%
42.9%
42.9%
42.9%
42.9%
42.9%
42.9%
42.9%
42.9%
43.0%
43.0%
43.0%
43.0%
43.0%
43.0%
43.0%
43.0%
43.0%
43.0%
43.1%
43.1%
43.1%
43.1%
43.1%
43.1%
43.1%
43.1%
43.1%
43.2%
43.2%
43.2%
43.2%
43.2%
43.2%
43.2%
43.2%
43.2%
43.2%
43.3%
43.3%
43.3%
43.3%
43.3%
43.3%
43.3%
43.3%
43.3%
43.3%
43.4%
43.4%
43.4%
43.4%
43.4%
43.4%
43.4%
43.4%
43.4%
43.4%
43.5%
43.5%
43.5%
43.5%
43.5%
43.5%
43.5%
43.5%
43.5%
43.6%
43.6%
43.6%
43.6%
43.6%
43.6%
43.6%
43.6%
43.6%
43.6%
43.7%
43.7%
43.7%
43.7%
43.7%
43.7%
43.7%
43.7%
43.7%
43.7%
43.8%
43.8%
43.8%
43.8%
43.8%
43.8%
43.8%
43.8%
43.8%
43.9%
43.9%
43.9%
43.9%
43.9%
43.9%
43.9%
43.9%
43.9%
43.9%
44.0%
44.0%
44.0%
44.0%
44.0%
44.0%
44.0%
44.0%
44.0%
44.0%
44.1%
44.1%
44.1%
44.1%
44.1%
44.1%
44.1%
44.1%
44.1%
44.2%
44.2%
44.2%
44.2%
44.2%
44.2%
44.2%
44.2%
44.2%
44.2%
44.3%
44.3%
44.3%
44.3%
44.3%
44.3%
44.3%
44.3%
44.3%
44.3%
44.4%
44.4%
44.4%
44.4%
44.4%
44.4%
44.4%
44.4%
44.4%
44.5%
44.5%
44.5%
44.5%
44.5%
44.5%
44.5%
44.5%
44.5%
44.5%
44.6%
44.6%
44.6%
44.6%
44.6%
44.6%
44.6%
44.6%
44.6%
44.6%
44.7%
44.7%
44.7%
44.7%
44.7%
44.7%
44.7%
44.7%
44.7%
44.8%
44.8%
44.8%
44.8%
44.8%
44.8%
44.8%
44.8%
44.8%
44.8%
44.9%
44.9%
44.9%
44.9%
44.9%
44.9%
44.9%
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44.9%
44.9%
45.0%
45.0%
45.0%
45.0%
45.0%
45.0%
45.0%
45.0%
45.0%
45.1%
45.1%
45.1%
45.1%
45.1%
45.1%
45.1%
45.1%
45.1%
45.1%
45.2%
45.2%
45.2%
45.2%
45.2%
45.2%
45.2%
45.2%
45.2%
45.2%
45.3%
45.3%
45.3%
45.3%
45.3%
45.3%
45.3%
45.3%
45.3%
45.4%
45.4%
45.4%
45.4%
45.4%
45.4%
45.4%
45.4%
45.4%
45.4%
45.5%
45.5%
45.5%
45.5%
45.5%
45.5%
45.5%
45.5%
45.5%
45.5%
45.6%
45.6%
45.6%
45.6%
45.6%
45.6%
45.6%
45.6%
45.6%
45.7%
45.7%
45.7%
45.7%
45.7%
45.7%
45.7%
45.7%
45.7%
45.7%
45.8%
45.8%
45.8%
45.8%
45.8%
45.8%
45.8%
45.8%
45.8%
45.8%
45.9%
45.9%
45.9%
45.9%
45.9%
45.9%
45.9%
45.9%
45.9%
45.9%
46.0%
46.0%
46.0%
46.0%
46.0%
46.0%
46.0%
46.0%
46.0%
46.1%
46.1%
46.1%
46.1%
46.1%
46.1%
46.1%
46.1%
46.1%
46.1%
46.2%
46.2%
46.2%
46.2%
46.2%
46.2%
46.2%
46.2%
46.2%
46.2%
46.3%
46.3%
46.3%
46.3%
46.3%
46.3%
46.3%
46.3%
46.3%
46.4%
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46.4%
46.4%
46.4%
46.4%
46.4%
46.5%
46.5%
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46.5%
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46.5%
46.5%
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46.5%
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46.6%
46.6%
46.6%
46.6%
46.6%
46.6%
46.6%
46.6%
46.6%
46.7%
46.7%
46.7%
46.7%
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46.7%
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46.8%
46.8%
46.8%
46.8%
46.8%
46.8%
46.8%
46.8%
46.8%
46.8%
46.9%
46.9%
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46.9%
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46.9%
46.9%
47.0%
47.0%
47.0%
47.0%
47.0%
47.0%
47.0%
47.0%
47.0%
47.0%
47.1%
47.1%
47.1%
47.1%
47.1%
47.1%
47.1%
47.1%
47.1%
47.1%
47.2%
47.2%
47.2%
47.2%
47.2%
47.2%
47.2%
47.2%
47.2%
47.3%
47.3%
47.3%
47.3%
47.3%
47.3%
47.3%
47.3%
47.3%
47.3%
47.4%
47.4%
47.4%
47.4%
47.4%
47.4%
47.4%
47.4%
47.4%
47.4%
47.5%
47.5%
47.5%
47.5%
47.5%
47.5%
47.5%
47.5%
47.5%
47.6%
47.6%
47.6%
47.6%
47.6%
47.6%
47.6%
47.6%
47.6%
47.6%
47.7%
47.7%
47.7%
47.7%
47.7%
47.7%
47.7%
47.7%
47.7%
47.7%
47.8%
47.8%
47.8%
47.8%
47.8%
47.8%
47.8%
47.8%
47.8%
47.9%
47.9%
47.9%
47.9%
47.9%
47.9%
47.9%
47.9%
47.9%
47.9%
48.0%
48.0%
48.0%
48.0%
48.0%
48.0%
48.0%
48.0%
48.0%
48.0%
48.1%
48.1%
48.1%
48.1%
48.1%
48.1%
48.1%
48.1%
48.1%
48.2%
48.2%
48.2%
48.2%
48.2%
48.2%
48.2%
48.2%
48.2%
48.2%
48.3%
48.3%
48.3%
48.3%
48.3%
48.3%
48.3%
48.3%
48.3%
48.3%
48.4%
48.4%
48.4%
48.4%
48.4%
48.4%
48.4%
48.4%
48.4%
48.4%
48.5%
48.5%
48.5%
48.5%
48.5%
48.5%
48.5%
48.5%
48.5%
48.6%
48.6%
48.6%
48.6%
48.6%
48.6%
48.6%
48.6%
48.6%
48.6%
48.7%
48.7%
48.7%
48.7%
48.7%
48.7%
48.7%
48.7%
48.7%
48.7%
48.8%
48.8%
48.8%
48.8%
48.8%
48.8%
48.8%
48.8%
48.8%
48.9%
48.9%
48.9%
48.9%
48.9%
48.9%
48.9%
48.9%
48.9%
48.9%
49.0%
49.0%
49.0%
49.0%
49.0%
49.0%
49.0%
49.0%
49.0%
49.0%
49.1%
49.1%
49.1%
49.1%
49.1%
49.1%
49.1%
49.1%
49.1%
49.2%
49.2%
49.2%
49.2%
49.2%
49.2%
49.2%
49.2%
49.2%
49.2%
49.3%
49.3%
49.3%
49.3%
49.3%
49.3%
49.3%
49.3%
49.3%
49.3%
49.4%
49.4%
49.4%
49.4%
49.4%
49.4%
49.4%
49.4%
49.4%
49.5%
49.5%
49.5%
49.5%
49.5%
49.5%
49.5%
49.5%
49.5%
49.5%
49.6%
49.6%
49.6%
49.6%
49.6%
49.6%
49.6%
49.6%
49.6%
49.6%
49.7%
49.7%
49.7%
49.7%
49.7%
49.7%
49.7%
49.7%
49.7%
49.8%
49.8%
49.8%
49.8%
49.8%
49.8%
49.8%
49.8%
49.8%
49.8%
49.9%
49.9%
49.9%
49.9%
49.9%
49.9%
49.9%
49.9%
49.9%
49.9%
50.0%
50.0%
50.0%
50.0%
50.0%
50.0%
50.0%
50.0%
50.0%
50.1%
50.1%
50.1%
50.1%
50.1%
50.1%
50.1%
50.1%
50.1%
50.1%
50.2%
50.2%
50.2%
50.2%
50.2%
50.2%
50.2%
50.2%
50.2%
50.2%
50.3%
50.3%
50.3%
50.3%
50.3%
50.3%
50.3%
50.3%
50.3%
50.4%
50.4%
50.4%
50.4%
50.4%
50.4%
50.4%
50.4%
50.4%
50.4%
50.5%
50.5%
50.5%
50.5%
50.5%
50.5%
50.5%
50.5%
50.5%
50.5%
50.6%
50.6%
50.6%
50.6%
50.6%
50.6%
50.6%
50.6%
50.6%
50.7%
50.7%
50.7%
50.7%
50.7%
50.7%
50.7%
50.7%
50.7%
50.7%
50.8%
50.8%
50.8%
50.8%
50.8%
50.8%
50.8%
50.8%
50.8%
50.8%
50.9%
50.9%
50.9%
50.9%
50.9%
50.9%
50.9%
50.9%
50.9%
50.9%
51.0%
51.0%
51.0%
51.0%
51.0%
51.0%
51.0%
51.0%
51.0%
51.1%
51.1%
51.1%
51.1%
51.1%
51.1%
51.1%
51.1%
51.1%
51.1%
51.2%
51.2%
51.2%
51.2%
51.2%
51.2%
51.2%
51.2%
51.2%
51.2%
51.3%
51.3%
51.3%
51.3%
51.3%
51.3%
51.3%
51.3%
51.3%
51.4%
51.4%
51.4%
51.4%
51.4%
51.4%
51.4%
51.4%
51.4%
51.4%
51.5%
51.5%
51.5%
51.5%
51.5%
51.5%
51.5%
51.5%
51.5%
51.5%
51.6%
51.6%
51.6%
51.6%
51.6%
51.6%
51.6%
51.6%
51.6%
51.7%
51.7%
51.7%
51.7%
51.7%
51.7%
51.7%
51.7%
51.7%
51.7%
51.8%
51.8%
51.8%
51.8%
51.8%
51.8%
51.8%
51.8%
51.8%
51.8%
51.9%
51.9%
51.9%
51.9%
51.9%
51.9%
51.9%
51.9%
51.9%
52.0%
52.0%
52.0%
52.0%
52.0%
52.0%
52.0%
52.0%
52.0%
52.0%
52.1%
52.1%
52.1%
52.1%
52.1%
52.1%
52.1%
52.1%
52.1%
52.1%
52.2%
52.2%
52.2%
52.2%
52.2%
52.2%
52.2%
52.2%
52.2%
52.3%
52.3%
52.3%
52.3%
52.3%
52.3%
52.3%
52.3%
52.3%
52.3%
52.4%
52.4%
52.4%
52.4%
52.4%
52.4%
52.4%
52.4%
52.4%
52.4%
52.5%
52.5%
52.5%
52.5%
52.5%
52.5%
52.5%
52.5%
52.5%
52.6%
52.6%
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52.6%
52.6%
52.6%
52.6%
52.6%
52.6%
52.6%
52.7%
52.7%
52.7%
52.7%
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52.7%
52.8%
52.8%
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52.8%
52.8%
52.8%
52.8%
52.8%
52.8%
52.9%
52.9%
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52.9%
53.0%
53.0%
53.0%
53.0%
53.0%
53.0%
53.0%
53.0%
53.0%
53.0%
53.1%
53.1%
53.1%
53.1%
53.1%
53.1%
53.1%
53.1%
53.1%
53.2%
53.2%
53.2%
53.2%
53.2%
53.2%
53.2%
53.2%
53.2%
53.2%
53.3%
53.3%
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53.3%
53.3%
53.3%
53.3%
53.3%
53.3%
53.3%
53.4%
53.4%
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53.4%
53.4%
53.4%
53.4%
53.4%
53.4%
53.4%
53.5%
53.5%
53.5%
53.5%
53.5%
53.5%
53.5%
53.5%
53.5%
53.6%
53.6%
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53.6%
53.6%
53.6%
53.6%
53.6%
53.6%
53.6%
53.7%
53.7%
53.7%
53.7%
53.7%
53.7%
53.7%
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53.7%
53.7%
53.8%
53.8%
53.8%
53.8%
53.8%
53.8%
53.8%
53.8%
53.8%
53.9%
53.9%
53.9%
53.9%
53.9%
53.9%
53.9%
53.9%
53.9%
53.9%
54.0%
54.0%
54.0%
54.0%
54.0%
54.0%
54.0%
54.0%
54.0%
54.0%
54.1%
54.1%
54.1%
54.1%
54.1%
54.1%
54.1%
54.1%
54.1%
54.2%
54.2%
54.2%
54.2%
54.2%
54.2%
54.2%
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54.2%
54.2%
54.3%
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54.3%
54.3%
54.3%
54.3%
54.3%
54.3%
54.3%
54.4%
54.4%
54.4%
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54.4%
54.4%
54.4%
54.4%
54.5%
54.5%
54.5%
54.5%
54.5%
54.5%
54.5%
54.5%
54.5%
54.5%
54.6%
54.6%
54.6%
54.6%
54.6%
54.6%
54.6%
54.6%
54.6%
54.6%
54.7%
54.7%
54.7%
54.7%
54.7%
54.7%
54.7%
54.7%
54.7%
54.8%
54.8%
54.8%
54.8%
54.8%
54.8%
54.8%
54.8%
54.8%
54.8%
54.9%
54.9%
54.9%
54.9%
54.9%
54.9%
54.9%
54.9%
54.9%
54.9%
55.0%
55.0%
55.0%
55.0%
55.0%
55.0%
55.0%
55.0%
55.0%
55.1%
55.1%
55.1%
55.1%
55.1%
55.1%
55.1%
55.1%
55.1%
55.1%
55.2%
55.2%
55.2%
55.2%
55.2%
55.2%
55.2%
55.2%
55.2%
55.2%
55.3%
55.3%
55.3%
55.3%
55.3%
55.3%
55.3%
55.3%
55.3%
55.4%
55.4%
55.4%
55.4%
55.4%
55.4%
55.4%
55.4%
55.4%
55.4%
55.5%
55.5%
55.5%
55.5%
55.5%
55.5%
55.5%
55.5%
55.5%
55.5%
55.6%
55.6%
55.6%
55.6%
55.6%
55.6%
55.6%
55.6%
55.6%
55.7%
55.7%
55.7%
55.7%
55.7%
55.7%
55.7%
55.7%
55.7%
55.7%
55.8%
55.8%
55.8%
55.8%
55.8%
55.8%
55.8%
55.8%
55.8%
55.8%
55.9%
55.9%
55.9%
55.9%
55.9%
55.9%
55.9%
55.9%
55.9%
55.9%
56.0%
56.0%
56.0%
56.0%
56.0%
56.0%
56.0%
56.0%
56.0%
56.1%
56.1%
56.1%
56.1%
56.1%
56.1%
56.1%
56.1%
56.1%
56.1%
56.2%
56.2%
56.2%
56.2%
56.2%
56.2%
56.2%
56.2%
56.2%
56.2%
56.3%
56.3%
56.3%
56.3%
56.3%
56.3%
56.3%
56.3%
56.3%
56.4%
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56.4%
56.4%
56.4%
56.4%
56.4%
56.4%
56.4%
56.4%
56.5%
56.5%
56.5%
56.5%
56.5%
56.5%
56.5%
56.5%
56.5%
56.5%
56.6%
56.6%
56.6%
56.6%
56.6%
56.6%
56.6%
56.6%
56.6%
56.7%
56.7%
56.7%
56.7%
56.7%
56.7%
56.7%
56.7%
56.7%
56.7%
56.8%
56.8%
56.8%
56.8%
56.8%
56.8%
56.8%
56.8%
56.8%
56.8%
56.9%
56.9%
56.9%
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56.9%
56.9%
56.9%
56.9%
57.0%
57.0%
57.0%
57.0%
57.0%
57.0%
57.0%
57.0%
57.0%
57.0%
57.1%
57.1%
57.1%
57.1%
57.1%
57.1%
57.1%
57.1%
57.1%
57.1%
57.2%
57.2%
57.2%
57.2%
57.2%
57.2%
57.2%
57.2%
57.2%
57.3%
57.3%
57.3%
57.3%
57.3%
57.3%
57.3%
57.3%
57.3%
57.3%
57.4%
57.4%
57.4%
57.4%
57.4%
57.4%
57.4%
57.4%
57.4%
57.4%
57.5%
57.5%
57.5%
57.5%
57.5%
57.5%
57.5%
57.5%
57.5%
57.6%
57.6%
57.6%
57.6%
57.6%
57.6%
57.6%
57.6%
57.6%
57.6%
57.7%
57.7%
57.7%
57.7%
57.7%
57.7%
57.7%
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57.7%
57.7%
57.8%
57.8%
57.8%
57.8%
57.8%
57.8%
57.8%
57.8%
57.8%
57.9%
57.9%
57.9%
57.9%
57.9%
57.9%
57.9%
57.9%
57.9%
57.9%
58.0%
58.0%
58.0%
58.0%
58.0%
58.0%
58.0%
58.0%
58.0%
58.0%
58.1%
58.1%
58.1%
58.1%
58.1%
58.1%
58.1%
58.1%
58.1%
58.2%
58.2%
58.2%
58.2%
58.2%
58.2%
58.2%
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58.2%
58.2%
58.3%
58.3%
58.3%
58.3%
58.3%
58.3%
58.3%
58.3%
58.3%
58.3%
58.4%
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58.4%
58.4%
58.4%
58.4%
58.4%
58.4%
58.4%
58.4%
58.5%
58.5%
58.5%
58.5%
58.5%
58.5%
58.5%
58.5%
58.5%
58.6%
58.6%
58.6%
58.6%
58.6%
58.6%
58.6%
58.6%
58.6%
58.6%
58.7%
58.7%
58.7%
58.7%
58.7%
58.7%
58.7%
58.7%
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58.7%
58.8%
58.8%
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58.8%
58.8%
58.8%
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58.8%
58.8%
58.9%
58.9%
58.9%
58.9%
58.9%
58.9%
58.9%
58.9%
58.9%
58.9%
59.0%
59.0%
59.0%
59.0%
59.0%
59.0%
59.0%
59.0%
59.0%
59.0%
59.1%
59.1%
59.1%
59.1%
59.1%
59.1%
59.1%
59.1%
59.1%
59.2%
59.2%
59.2%
59.2%
59.2%
59.2%
59.2%
59.2%
59.2%
59.2%
59.3%
59.3%
59.3%
59.3%
59.3%
59.3%
59.3%
59.3%
59.3%
59.3%
59.4%
59.4%
59.4%
59.4%
59.4%
59.4%
59.4%
59.4%
59.4%
59.5%
59.5%
59.5%
59.5%
59.5%
59.5%
59.5%
59.5%
59.5%
59.5%
59.6%
59.6%
59.6%
59.6%
59.6%
59.6%
59.6%
59.6%
59.6%
59.6%
59.7%
59.7%
59.7%
59.7%
59.7%
59.7%
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59.7%
59.7%
59.8%
59.8%
59.8%
59.8%
59.8%
59.8%
59.8%
59.8%
59.8%
59.8%
59.9%
59.9%
59.9%
59.9%
59.9%
59.9%
59.9%
59.9%
59.9%
59.9%
60.0%
60.0%
60.0%
60.0%
60.0%
60.0%
60.0%
60.0%
60.0%
60.1%
60.1%
60.1%
60.1%
60.1%
60.1%
60.1%
60.1%
60.1%
60.1%
60.2%
60.2%
60.2%
60.2%
60.2%
60.2%
60.2%
60.2%
60.2%
60.2%
60.3%
60.3%
60.3%
60.3%
60.3%
60.3%
60.3%
60.3%
60.3%
60.4%
60.4%
60.4%
60.4%
60.4%
60.4%
60.4%
60.4%
60.4%
60.4%
60.5%
60.5%
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60.5%
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60.5%
60.5%
60.5%
60.5%
60.5%
60.6%
60.6%
60.6%
60.6%
60.6%
60.6%
60.6%
60.6%
60.6%
60.7%
60.7%
60.7%
60.7%
60.7%
60.7%
60.7%
60.7%
60.7%
60.7%
60.8%
60.8%
60.8%
60.8%
60.8%
60.8%
60.8%
60.8%
60.8%
60.8%
60.9%
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60.9%
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60.9%
60.9%
61.0%
61.0%
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61.0%
61.0%
61.1%
61.1%
61.1%
61.1%
61.1%
61.1%
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61.1%
61.2%
61.2%
61.2%
61.2%
61.2%
61.2%
61.2%
61.2%
61.2%
61.2%
61.3%
61.3%
61.3%
61.3%
61.3%
61.3%
61.3%
61.3%
61.3%
61.4%
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61.4%
61.4%
61.4%
61.4%
61.4%
61.5%
61.5%
61.5%
61.5%
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61.5%
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61.5%
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61.6%
61.6%
61.6%
61.6%
61.6%
61.6%
61.6%
61.6%
61.6%
61.7%
61.7%
61.7%
61.7%
61.7%
61.7%
61.7%
61.7%
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61.7%
61.8%
61.8%
61.8%
61.8%
61.8%
61.8%
61.8%
61.8%
61.8%
61.8%
61.9%
61.9%
61.9%
61.9%
61.9%
61.9%
61.9%
61.9%
61.9%
62.0%
62.0%
62.0%
62.0%
62.0%
62.0%
62.0%
62.0%
62.0%
62.0%
62.1%
62.1%
62.1%
62.1%
62.1%
62.1%
62.1%
62.1%
62.1%
62.1%
62.2%
62.2%
62.2%
62.2%
62.2%
62.2%
62.2%
62.2%
62.2%
62.3%
62.3%
62.3%
62.3%
62.3%
62.3%
62.3%
62.3%
62.3%
62.3%
62.4%
62.4%
62.4%
62.4%
62.4%
62.4%
62.4%
62.4%
62.4%
62.4%
62.5%
62.5%
62.5%
62.5%
62.5%
62.5%
62.5%
62.5%
62.5%
62.6%
62.6%
62.6%
62.6%
62.6%
62.6%
62.6%
62.6%
62.6%
62.6%
62.7%
62.7%
62.7%
62.7%
62.7%
62.7%
62.7%
62.7%
62.7%
62.7%
62.8%
62.8%
62.8%
62.8%
62.8%
62.8%
62.8%
62.8%
62.8%
62.9%
62.9%
62.9%
62.9%
62.9%
62.9%
62.9%
62.9%
62.9%
62.9%
63.0%
63.0%
63.0%
63.0%
63.0%
63.0%
63.0%
63.0%
63.0%
63.0%
63.1%
63.1%
63.1%
63.1%
63.1%
63.1%
63.1%
63.1%
63.1%
63.2%
63.2%
63.2%
63.2%
63.2%
63.2%
63.2%
63.2%
63.2%
63.2%
63.3%
63.3%
63.3%
63.3%
63.3%
63.3%
63.3%
63.3%
63.3%
63.3%
63.4%
63.4%
63.4%
63.4%
63.4%
63.4%
63.4%
63.4%
63.4%
63.4%
63.5%
63.5%
63.5%
63.5%
63.5%
63.5%
63.5%
63.5%
63.5%
63.6%
63.6%
63.6%
63.6%
63.6%
63.6%
63.6%
63.6%
63.6%
63.6%
63.7%
63.7%
63.7%
63.7%
63.7%
63.7%
63.7%
63.7%
63.7%
63.7%
63.8%
63.8%
63.8%
63.8%
63.8%
63.8%
63.8%
63.8%
63.8%
63.9%
63.9%
63.9%
63.9%
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63.9%
63.9%
63.9%
63.9%
64.0%
64.0%
64.0%
64.0%
64.0%
64.0%
64.0%
64.0%
64.0%
64.0%
64.1%
64.1%
64.1%
64.1%
64.1%
64.1%
64.1%
64.1%
64.1%
64.2%
64.2%
64.2%
64.2%
64.2%
64.2%
64.2%
64.2%
64.2%
64.2%
64.3%
64.3%
64.3%
64.3%
64.3%
64.3%
64.3%
64.3%
64.3%
64.3%
64.4%
64.4%
64.4%
64.4%
64.4%
64.4%
64.4%
64.4%
64.4%
64.5%
64.5%
64.5%
64.5%
64.5%
64.5%
64.5%
64.5%
64.5%
64.5%
64.6%
64.6%
64.6%
64.6%
64.6%
64.6%
64.6%
64.6%
64.6%
64.6%
64.7%
64.7%
64.7%
64.7%
64.7%
64.7%
64.7%
64.7%
64.7%
64.8%
64.8%
64.8%
64.8%
64.8%
64.8%
64.8%
64.8%
64.8%
64.8%
64.9%
64.9%
64.9%
64.9%
64.9%
64.9%
64.9%
64.9%
64.9%
64.9%
65.0%
65.0%
65.0%
65.0%
65.0%
65.0%
65.0%
65.0%
65.0%
65.1%
65.1%
65.1%
65.1%
65.1%
65.1%
65.1%
65.1%
65.1%
65.1%
65.2%
65.2%
65.2%
65.2%
65.2%
65.2%
65.2%
65.2%
65.2%
65.2%
65.3%
65.3%
65.3%
65.3%
65.3%
65.3%
65.3%
65.3%
65.3%
65.4%
65.4%
65.4%
65.4%
65.4%
65.4%
65.4%
65.4%
65.4%
65.4%
65.5%
65.5%
65.5%
65.5%
65.5%
65.5%
65.5%
65.5%
65.5%
65.5%
65.6%
65.6%
65.6%
65.6%
65.6%
65.6%
65.6%
65.6%
65.6%
65.7%
65.7%
65.7%
65.7%
65.7%
65.7%
65.7%
65.7%
65.7%
65.7%
65.8%
65.8%
65.8%
65.8%
65.8%
65.8%
65.8%
65.8%
65.8%
65.8%
65.9%
65.9%
65.9%
65.9%
65.9%
65.9%
65.9%
65.9%
65.9%
65.9%
66.0%
66.0%
66.0%
66.0%
66.0%
66.0%
66.0%
66.0%
66.0%
66.1%
66.1%
66.1%
66.1%
66.1%
66.1%
66.1%
66.1%
66.1%
66.1%
66.2%
66.2%
66.2%
66.2%
66.2%
66.2%
66.2%
66.2%
66.2%
66.2%
66.3%
66.3%
66.3%
66.3%
66.3%
66.3%
66.3%
66.3%
66.3%
66.4%
66.4%
66.4%
66.4%
66.4%
66.4%
66.4%
66.4%
66.4%
66.4%
66.5%
66.5%
66.5%
66.5%
66.5%
66.5%
66.5%
66.5%
66.5%
66.5%
66.6%
66.6%
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66.6%
66.6%
66.6%
66.6%
66.6%
66.6%
66.7%
66.7%
66.7%
66.7%
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66.7%
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66.7%
66.8%
66.8%
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66.8%
66.8%
66.8%
66.8%
66.8%
66.8%
66.8%
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66.9%
66.9%
67.0%
67.0%
67.0%
67.0%
67.0%
67.0%
67.0%
67.0%
67.0%
67.0%
67.1%
67.1%
67.1%
67.1%
67.1%
67.1%
67.1%
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67.1%
67.1%
67.2%
67.2%
67.2%
67.2%
67.2%
67.2%
67.2%
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67.2%
67.3%
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67.3%
67.3%
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67.3%
67.3%
67.4%
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67.5%
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67.6%
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67.6%
67.6%
67.6%
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67.6%
67.6%
67.7%
67.7%
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67.8%
67.8%
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67.8%
67.8%
67.9%
67.9%
67.9%
67.9%
67.9%
67.9%
67.9%
67.9%
67.9%
67.9%
68.0%
68.0%
68.0%
68.0%
68.0%
68.0%
68.0%
68.0%
68.0%
68.0%
68.1%
68.1%
68.1%
68.1%
68.1%
68.1%
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68.2%
68.2%
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68.3%
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68.6%
68.6%
68.6%
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68.6%
68.6%
68.7%
68.7%
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68.8%
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68.8%
68.8%
68.8%
68.8%
68.8%
68.9%
68.9%
68.9%
68.9%
68.9%
68.9%
68.9%
68.9%
68.9%
68.9%
69.0%
69.0%
69.0%
69.0%
69.0%
69.0%
69.0%
69.0%
69.0%
69.0%
69.1%
69.1%
69.1%
69.1%
69.1%
69.1%
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69.2%
69.2%
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69.3%
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69.3%
69.3%
69.3%
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69.3%
69.3%
69.4%
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69.5%
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69.5%
69.6%
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69.6%
69.6%
69.6%
69.6%
69.6%
69.6%
69.6%
69.7%
69.7%
69.7%
69.7%
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69.7%
69.7%
69.8%
69.8%
69.8%
69.8%
69.8%
69.8%
69.8%
69.8%
69.8%
69.8%
69.9%
69.9%
69.9%
69.9%
69.9%
69.9%
69.9%
69.9%
69.9%
69.9%
70.0%
70.0%
70.0%
70.0%
70.0%
70.0%
70.0%
70.0%
70.0%
70.1%
70.1%
70.1%
70.1%
70.1%
70.1%
70.1%
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70.2%
70.2%
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70.3%
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70.4%
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70.6%
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70.6%
70.6%
70.6%
70.6%
70.6%
70.7%
70.7%
70.7%
70.7%
70.7%
70.7%
70.7%
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70.7%
70.7%
70.8%
70.8%
70.8%
70.8%
70.8%
70.8%
70.8%
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70.8%
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71.0%
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71.0%
71.0%
71.1%
71.1%
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71.2%
71.2%
71.2%
71.2%
71.2%
71.2%
71.2%
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71.2%
71.3%
71.3%
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71.3%
71.3%
71.3%
71.3%
71.3%
71.3%
71.4%
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71.6%
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71.6%
71.6%
71.6%
71.6%
71.6%
71.7%
71.7%
71.7%
71.7%
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71.8%
71.8%
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71.8%
71.8%
71.8%
71.8%
71.8%
71.9%
71.9%
71.9%
71.9%
71.9%
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71.9%
71.9%
71.9%
72.0%
72.0%
72.0%
72.0%
72.0%
72.0%
72.0%
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72.0%
72.0%
72.1%
72.1%
72.1%
72.1%
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72.1%
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72.1%
72.2%
72.2%
72.2%
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72.2%
72.2%
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72.3%
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72.3%
72.3%
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72.3%
72.3%
72.4%
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72.5%
72.5%
72.6%
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72.6%
72.6%
72.6%
72.6%
72.6%
72.6%
72.6%
72.7%
72.7%
72.7%
72.7%
72.7%
72.7%
72.7%
72.7%
72.7%
72.7%
72.8%
72.8%
72.8%
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72.8%
72.8%
72.8%
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72.8%
72.9%
72.9%
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72.9%
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72.9%
72.9%
73.0%
73.0%
73.0%
73.0%
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73.0%
73.0%
73.1%
73.1%
73.1%
73.1%
73.1%
73.1%
73.1%
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73.1%
73.2%
73.2%
73.2%
73.2%
73.2%
73.2%
73.2%
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73.2%
73.3%
73.3%
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73.3%
73.3%
73.3%
73.3%
73.3%
73.3%
73.4%
73.4%
73.4%
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73.4%
73.4%
73.4%
73.4%
73.4%
73.4%
73.5%
73.5%
73.5%
73.5%
73.5%
73.5%
73.5%
73.5%
73.5%
73.6%
73.6%
73.6%
73.6%
73.6%
73.6%
73.6%
73.6%
73.6%
73.6%
73.7%
73.7%
73.7%
73.7%
73.7%
73.7%
73.7%
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73.7%
73.7%
73.8%
73.8%
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73.8%
73.8%
73.8%
73.8%
73.8%
73.8%
73.9%
73.9%
73.9%
73.9%
73.9%
73.9%
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73.9%
73.9%
74.0%
74.0%
74.0%
74.0%
74.0%
74.0%
74.0%
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74.0%
74.0%
74.1%
74.1%
74.1%
74.1%
74.1%
74.1%
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74.2%
74.2%
74.2%
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74.2%
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74.3%
74.3%
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74.3%
74.3%
74.3%
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74.3%
74.3%
74.4%
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74.5%
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74.6%
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74.6%
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74.6%
74.6%
74.7%
74.7%
74.7%
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74.8%
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74.8%
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74.9%
75.0%
75.0%
75.0%
75.0%
75.0%
75.0%
75.0%
75.0%
75.0%
75.1%
75.1%
75.1%
75.1%
75.1%
75.1%
75.1%
75.1%
75.1%
75.1%
75.2%
75.2%
75.2%
75.2%
75.2%
75.2%
75.2%
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75.2%
75.3%
75.3%
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75.3%
75.3%
75.3%
75.3%
75.3%
75.3%
75.4%
75.4%
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75.4%
75.4%
75.4%
75.4%
75.4%
75.4%
75.4%
75.5%
75.5%
75.5%
75.5%
75.5%
75.5%
75.5%
75.5%
75.5%
75.5%
75.6%
75.6%
75.6%
75.6%
75.6%
75.6%
75.6%
75.6%
75.6%
75.7%
75.7%
75.7%
75.7%
75.7%
75.7%
75.7%
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75.7%
75.8%
75.8%
75.8%
75.8%
75.8%
75.8%
75.8%
75.8%
75.8%
75.8%
75.9%
75.9%
75.9%
75.9%
75.9%
75.9%
75.9%
75.9%
75.9%
75.9%
76.0%
76.0%
76.0%
76.0%
76.0%
76.0%
76.0%
76.0%
76.0%
76.1%
76.1%
76.1%
76.1%
76.1%
76.1%
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76.1%
76.2%
76.2%
76.2%
76.2%
76.2%
76.2%
76.2%
76.2%
76.2%
76.2%
76.3%
76.3%
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76.3%
76.3%
76.3%
76.3%
76.3%
76.3%
76.4%
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76.4%
76.4%
76.4%
76.4%
76.5%
76.5%
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76.5%
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76.5%
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76.6%
76.6%
76.6%
76.6%
76.6%
76.6%
76.6%
76.6%
76.6%
76.7%
76.7%
76.7%
76.7%
76.7%
76.7%
76.7%
76.7%
76.7%
76.7%
76.8%
76.8%
76.8%
76.8%
76.8%
76.8%
76.8%
76.8%
76.8%
76.8%
76.9%
76.9%
76.9%
76.9%
76.9%
76.9%
76.9%
76.9%
76.9%
77.0%
77.0%
77.0%
77.0%
77.0%
77.0%
77.0%
77.0%
77.0%
77.0%
77.1%
77.1%
77.1%
77.1%
77.1%
77.1%
77.1%
77.1%
77.1%
77.1%
77.2%
77.2%
77.2%
77.2%
77.2%
77.2%
77.2%
77.2%
77.2%
77.3%
77.3%
77.3%
77.3%
77.3%
77.3%
77.3%
77.3%
77.3%
77.3%
77.4%
77.4%
77.4%
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77.4%
77.4%
77.4%
77.4%
77.4%
77.4%
77.5%
77.5%
77.5%
77.5%
77.5%
77.5%
77.5%
77.5%
77.5%
77.6%
77.6%
77.6%
77.6%
77.6%
77.6%
77.6%
77.6%
77.6%
77.6%
77.7%
77.7%
77.7%
77.7%
77.7%
77.7%
77.7%
77.7%
77.7%
77.7%
77.8%
77.8%
77.8%
77.8%
77.8%
77.8%
77.8%
77.8%
77.8%
77.9%
77.9%
77.9%
77.9%
77.9%
77.9%
77.9%
77.9%
77.9%
77.9%
78.0%
78.0%
78.0%
78.0%
78.0%
78.0%
78.0%
78.0%
78.0%
78.0%
78.1%
78.1%
78.1%
78.1%
78.1%
78.1%
78.1%
78.1%
78.1%
78.2%
78.2%
78.2%
78.2%
78.2%
78.2%
78.2%
78.2%
78.2%
78.2%
78.3%
78.3%
78.3%
78.3%
78.3%
78.3%
78.3%
78.3%
78.3%
78.3%
78.4%
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78.4%
78.4%
78.4%
78.4%
78.4%
78.4%
78.4%
78.4%
78.5%
78.5%
78.5%
78.5%
78.5%
78.5%
78.5%
78.5%
78.5%
78.6%
78.6%
78.6%
78.6%
78.6%
78.6%
78.6%
78.6%
78.6%
78.6%
78.7%
78.7%
78.7%
78.7%
78.7%
78.7%
78.7%
78.7%
78.7%
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78.8%
78.8%
78.8%
78.8%
78.8%
78.8%
78.8%
78.8%
78.8%
78.9%
78.9%
78.9%
78.9%
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78.9%
78.9%
78.9%
78.9%
79.0%
79.0%
79.0%
79.0%
79.0%
79.0%
79.0%
79.0%
79.0%
79.0%
79.1%
79.1%
79.1%
79.1%
79.1%
79.1%
79.1%
79.1%
79.1%
79.2%
79.2%
79.2%
79.2%
79.2%
79.2%
79.2%
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79.2%
79.2%
79.3%
79.3%
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79.3%
79.3%
79.3%
79.3%
79.3%
79.3%
79.3%
79.4%
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79.4%
79.4%
79.4%
79.4%
79.4%
79.4%
79.4%
79.5%
79.5%
79.5%
79.5%
79.5%
79.5%
79.5%
79.5%
79.5%
79.5%
79.6%
79.6%
79.6%
79.6%
79.6%
79.6%
79.6%
79.6%
79.6%
79.6%
79.7%
79.7%
79.7%
79.7%
79.7%
79.7%
79.7%
79.7%
79.7%
79.8%
79.8%
79.8%
79.8%
79.8%
79.8%
79.8%
79.8%
79.8%
79.8%
79.9%
79.9%
79.9%
79.9%
79.9%
79.9%
79.9%
79.9%
79.9%
79.9%
80.0%
80.0%
80.0%
80.0%
80.0%
80.0%
80.0%
80.0%
80.0%
80.1%
80.1%
80.1%
80.1%
80.1%
80.1%
80.1%
80.1%
80.1%
80.1%
80.2%
80.2%
80.2%
80.2%
80.2%
80.2%
80.2%
80.2%
80.2%
80.2%
80.3%
80.3%
80.3%
80.3%
80.3%
80.3%
80.3%
80.3%
80.3%
80.4%
80.4%
80.4%
80.4%
80.4%
80.4%
80.4%
80.4%
80.4%
80.4%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.6%
80.6%
80.6%
80.6%
80.6%
80.6%
80.6%
80.6%
80.6%
80.6%
80.7%
80.7%
80.7%
80.7%
80.7%
80.7%
80.7%
80.7%
80.7%
80.8%
80.8%
80.8%
80.8%
80.8%
80.8%
80.8%
80.8%
80.8%
80.8%
80.9%
80.9%
80.9%
80.9%
80.9%
80.9%
80.9%
80.9%
80.9%
80.9%
81.0%
81.0%
81.0%
81.0%
81.0%
81.0%
81.0%
81.0%
81.0%
81.1%
81.1%
81.1%
81.1%
81.1%
81.1%
81.1%
81.1%
81.1%
81.1%
81.2%
81.2%
81.2%
81.2%
81.2%
81.2%
81.2%
81.2%
81.2%
81.2%
81.3%
81.3%
81.3%
81.3%
81.3%
81.3%
81.3%
81.3%
81.3%
81.4%
81.4%
81.4%
81.4%
81.4%
81.4%
81.4%
81.4%
81.4%
81.4%
81.5%
81.5%
81.5%
81.5%
81.5%
81.5%
81.5%
81.5%
81.5%
81.5%
81.6%
81.6%
81.6%
81.6%
81.6%
81.6%
81.6%
81.6%
81.6%
81.7%
81.7%
81.7%
81.7%
81.7%
81.7%
81.7%
81.7%
81.7%
81.7%
81.8%
81.8%
81.8%
81.8%
81.8%
81.8%
81.8%
81.8%
81.8%
81.8%
81.9%
81.9%
81.9%
81.9%
81.9%
81.9%
81.9%
81.9%
81.9%
82.0%
82.0%
82.0%
82.0%
82.0%
82.0%
82.0%
82.0%
82.0%
82.0%
82.1%
82.1%
82.1%
82.1%
82.1%
82.1%
82.1%
82.1%
82.1%
82.1%
82.2%
82.2%
82.2%
82.2%
82.2%
82.2%
82.2%
82.2%
82.2%
82.3%
82.3%
82.3%
82.3%
82.3%
82.3%
82.3%
82.3%
82.3%
82.3%
82.4%
82.4%
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82.4%
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82.4%
82.4%
82.4%
82.4%
82.4%
82.5%
82.5%
82.5%
82.5%
82.5%
82.5%
82.5%
82.5%
82.5%
82.6%
82.6%
82.6%
82.6%
82.6%
82.6%
82.6%
82.6%
82.6%
82.6%
82.7%
82.7%
82.7%
82.7%
82.7%
82.7%
82.7%
82.7%
82.7%
82.7%
82.8%
82.8%
82.8%
82.8%
82.8%
82.8%
82.8%
82.8%
82.8%
82.9%
82.9%
82.9%
82.9%
82.9%
82.9%
82.9%
82.9%
82.9%
82.9%
83.0%
83.0%
83.0%
83.0%
83.0%
83.0%
83.0%
83.0%
83.0%
83.0%
83.1%
83.1%
83.1%
83.1%
83.1%
83.1%
83.1%
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83.1%
83.1%
83.2%
83.2%
83.2%
83.2%
83.2%
83.2%
83.2%
83.2%
83.2%
83.3%
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83.3%
83.3%
83.3%
83.3%
83.3%
83.3%
83.4%
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83.4%
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83.4%
83.4%
83.4%
83.4%
83.5%
83.5%
83.5%
83.5%
83.5%
83.5%
83.5%
83.5%
83.5%
83.6%
83.6%
83.6%
83.6%
83.6%
83.6%
83.6%
83.6%
83.6%
83.6%
83.7%
83.7%
83.7%
83.7%
83.7%
83.7%
83.7%
83.7%
83.7%
83.7%
83.8%
83.8%
83.8%
83.8%
83.8%
83.8%
83.8%
83.8%
83.8%
83.9%
83.9%
83.9%
83.9%
83.9%
83.9%
83.9%
83.9%
83.9%
83.9%
84.0%
84.0%
84.0%
84.0%
84.0%
84.0%
84.0%
84.0%
84.0%
84.0%
84.1%
84.1%
84.1%
84.1%
84.1%
84.1%
84.1%
84.1%
84.1%
84.2%
84.2%
84.2%
84.2%
84.2%
84.2%
84.2%
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84.2%
84.2%
84.3%
84.3%
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84.3%
84.3%
84.3%
84.3%
84.3%
84.3%
84.3%
84.4%
84.4%
84.4%
84.4%
84.4%
84.4%
84.4%
84.4%
84.4%
84.5%
84.5%
84.5%
84.5%
84.5%
84.5%
84.5%
84.5%
84.5%
84.5%
84.6%
84.6%
84.6%
84.6%
84.6%
84.6%
84.6%
84.6%
84.6%
84.6%
84.7%
84.7%
84.7%
84.7%
84.7%
84.7%
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84.7%
84.7%
84.8%
84.8%
84.8%
84.8%
84.8%
84.8%
84.8%
84.8%
84.8%
84.8%
84.9%
84.9%
84.9%
84.9%
84.9%
84.9%
84.9%
84.9%
84.9%
84.9%
85.0%
85.0%
85.0%
85.0%
85.0%
85.0%
85.0%
85.0%
85.0%
85.1%
85.1%
85.1%
85.1%
85.1%
85.1%
85.1%
85.1%
85.1%
85.1%
85.2%
85.2%
85.2%
85.2%
85.2%
85.2%
85.2%
85.2%
85.2%
85.2%
85.3%
85.3%
85.3%
85.3%
85.3%
85.3%
85.3%
85.3%
85.3%
85.4%
85.4%
85.4%
85.4%
85.4%
85.4%
85.4%
85.4%
85.4%
85.4%
85.5%
85.5%
85.5%
85.5%
85.5%
85.5%
85.5%
85.5%
85.5%
85.5%
85.6%
85.6%
85.6%
85.6%
85.6%
85.6%
85.6%
85.6%
85.6%
85.6%
85.7%
85.7%
85.7%
85.7%
85.7%
85.7%
85.7%
85.7%
85.7%
85.8%
85.8%
85.8%
85.8%
85.8%
85.8%
85.8%
85.8%
85.8%
85.8%
85.9%
85.9%
85.9%
85.9%
85.9%
85.9%
85.9%
85.9%
85.9%
85.9%
86.0%
86.0%
86.0%
86.0%
86.0%
86.0%
86.0%
86.0%
86.0%
86.1%
86.1%
86.1%
86.1%
86.1%
86.1%
86.1%
86.1%
86.1%
86.1%
86.2%
86.2%
86.2%
86.2%
86.2%
86.2%
86.2%
86.2%
86.2%
86.2%
86.3%
86.3%
86.3%
86.3%
86.3%
86.3%
86.3%
86.3%
86.3%
86.4%
86.4%
86.4%
86.4%
86.4%
86.4%
86.4%
86.4%
86.4%
86.4%
86.5%
86.5%
86.5%
86.5%
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86.5%
86.5%
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86.5%
86.6%
86.6%
86.6%
86.6%
86.6%
86.6%
86.6%
86.6%
86.6%
86.7%
86.7%
86.7%
86.7%
86.7%
86.7%
86.7%
86.7%
86.7%
86.7%
86.8%
86.8%
86.8%
86.8%
86.8%
86.8%
86.8%
86.8%
86.8%
86.8%
86.9%
86.9%
86.9%
86.9%
86.9%
86.9%
86.9%
86.9%
86.9%
87.0%
87.0%
87.0%
87.0%
87.0%
87.0%
87.0%
87.0%
87.0%
87.0%
87.1%
87.1%
87.1%
87.1%
87.1%
87.1%
87.1%
87.1%
87.1%
87.1%
87.2%
87.2%
87.2%
87.2%
87.2%
87.2%
87.2%
87.2%
87.2%
87.3%
87.3%
87.3%
87.3%
87.3%
87.3%
87.3%
87.3%
87.3%
87.3%
87.4%
87.4%
87.4%
87.4%
87.4%
87.4%
87.4%
87.4%
87.4%
87.4%
87.5%
87.5%
87.5%
87.5%
87.5%
87.5%
87.5%
87.5%
87.5%
87.6%
87.6%
87.6%
87.6%
87.6%
87.6%
87.6%
87.6%
87.6%
87.6%
87.7%
87.7%
87.7%
87.7%
87.7%
87.7%
87.7%
87.7%
87.7%
87.7%
87.8%
87.8%
87.8%
87.8%
87.8%
87.8%
87.8%
87.8%
87.8%
87.9%
87.9%
87.9%
87.9%
87.9%
87.9%
87.9%
87.9%
87.9%
87.9%
88.0%
88.0%
88.0%
88.0%
88.0%
88.0%
88.0%
88.0%
88.0%
88.0%
88.1%
88.1%
88.1%
88.1%
88.1%
88.1%
88.1%
88.1%
88.1%
88.1%
88.2%
88.2%
88.2%
88.2%
88.2%
88.2%
88.2%
88.2%
88.2%
88.3%
88.3%
88.3%
88.3%
88.3%
88.3%
88.3%
88.3%
88.3%
88.3%
88.4%
88.4%
88.4%
88.4%
88.4%
88.4%
88.4%
88.4%
88.4%
88.4%
88.5%
88.5%
88.5%
88.5%
88.5%
88.5%
88.5%
88.5%
88.5%
88.6%
88.6%
88.6%
88.6%
88.6%
88.6%
88.6%
88.6%
88.6%
88.6%
88.7%
88.7%
88.7%
88.7%
88.7%
88.7%
88.7%
88.7%
88.7%
88.7%
88.8%
88.8%
88.8%
88.8%
88.8%
88.8%
88.8%
88.8%
88.8%
88.9%
88.9%
88.9%
88.9%
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88.9%
88.9%
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88.9%
89.0%
89.0%
89.0%
89.0%
89.0%
89.0%
89.0%
89.0%
89.0%
89.0%
89.1%
89.1%
89.1%
89.1%
89.1%
89.1%
89.1%
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89.1%
89.2%
89.2%
89.2%
89.2%
89.2%
89.2%
89.2%
89.2%
89.2%
89.2%
89.3%
89.3%
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89.3%
89.3%
89.3%
89.3%
89.3%
89.3%
89.3%
89.4%
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89.4%
89.4%
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89.4%
89.4%
89.4%
89.4%
89.5%
89.5%
89.5%
89.5%
89.5%
89.5%
89.5%
89.5%
89.5%
89.5%
89.6%
89.6%
89.6%
89.6%
89.6%
89.6%
89.6%
89.6%
89.6%
89.6%
89.7%
89.7%
89.7%
89.7%
89.7%
89.7%
89.7%
89.7%
89.7%
89.8%
89.8%
89.8%
89.8%
89.8%
89.8%
89.8%
89.8%
89.8%
89.8%
89.9%
89.9%
89.9%
89.9%
89.9%
89.9%
89.9%
89.9%
89.9%
89.9%
90.0%
90.0%
90.0%
90.0%
90.0%
90.0%
90.0%
90.0%
90.0%
90.1%
90.1%
90.1%
90.1%
90.1%
90.1%
90.1%
90.1%
90.1%
90.1%
90.2%
90.2%
90.2%
90.2%
90.2%
90.2%
90.2%
90.2%
90.2%
90.2%
90.3%
90.3%
90.3%
90.3%
90.3%
90.3%
90.3%
90.3%
90.3%
90.4%
90.4%
90.4%
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90.4%
90.4%
90.4%
90.4%
90.4%
90.4%
90.5%
90.5%
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90.5%
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90.5%
90.5%
90.5%
90.5%
90.5%
90.6%
90.6%
90.6%
90.6%
90.6%
90.6%
90.6%
90.6%
90.6%
90.6%
90.7%
90.7%
90.7%
90.7%
90.7%
90.7%
90.7%
90.7%
90.7%
90.8%
90.8%
90.8%
90.8%
90.8%
90.8%
90.8%
90.8%
90.8%
90.8%
90.9%
90.9%
90.9%
90.9%
90.9%
90.9%
90.9%
90.9%
90.9%
90.9%
91.0%
91.0%
91.0%
91.0%
91.0%
91.0%
91.0%
91.0%
91.0%
91.1%
91.1%
91.1%
91.1%
91.1%
91.1%
91.1%
91.1%
91.1%
91.1%
91.2%
91.2%
91.2%
91.2%
91.2%
91.2%
91.2%
91.2%
91.2%
91.2%
91.3%
91.3%
91.3%
91.3%
91.3%
91.3%
91.3%
91.3%
91.3%
91.4%
91.4%
91.4%
91.4%
91.4%
91.4%
91.4%
91.4%
91.4%
91.4%
91.5%
91.5%
91.5%
91.5%
91.5%
91.5%
91.5%
91.5%
91.5%
91.5%
91.6%
91.6%
91.6%
91.6%
91.6%
91.6%
91.6%
91.6%
91.6%
91.7%
91.7%
91.7%
91.7%
91.7%
91.7%
91.7%
91.7%
91.7%
91.7%
91.8%
91.8%
91.8%
91.8%
91.8%
91.8%
91.8%
91.8%
91.8%
91.8%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
92.0%
92.0%
92.0%
92.0%
92.0%
92.0%
92.0%
92.0%
92.0%
92.0%
92.1%
92.1%
92.1%
92.1%
92.1%
92.1%
92.1%
92.1%
92.1%
92.1%
92.2%
92.2%
92.2%
92.2%
92.2%
92.2%
92.2%
92.2%
92.2%
92.3%
92.3%
92.3%
92.3%
92.3%
92.3%
92.3%
92.3%
92.3%
92.3%
92.4%
92.4%
92.4%
92.4%
92.4%
92.4%
92.4%
92.4%
92.4%
92.4%
92.5%
92.5%
92.5%
92.5%
92.5%
92.5%
92.5%
92.5%
92.5%
92.6%
92.6%
92.6%
92.6%
92.6%
92.6%
92.6%
92.6%
92.6%
92.6%
92.7%
92.7%
92.7%
92.7%
92.7%
92.7%
92.7%
92.7%
92.7%
92.7%
92.8%
92.8%
92.8%
92.8%
92.8%
92.8%
92.8%
92.8%
92.8%
92.9%
92.9%
92.9%
92.9%
92.9%
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92.9%
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92.9%
92.9%
93.0%
93.0%
93.0%
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93.0%
93.1%
93.1%
93.1%
93.1%
93.1%
93.1%
93.1%
93.1%
93.1%
93.1%
93.2%
93.2%
93.2%
93.2%
93.2%
93.2%
93.2%
93.2%
93.2%
93.3%
93.3%
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93.3%
93.3%
93.3%
93.3%
93.3%
93.3%
93.4%
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93.4%
93.4%
93.4%
93.4%
93.5%
93.5%
93.5%
93.5%
93.5%
93.5%
93.5%
93.5%
93.5%
93.6%
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93.6%
93.6%
93.6%
93.6%
93.6%
93.6%
93.6%
93.6%
93.7%
93.7%
93.7%
93.7%
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93.8%
93.8%
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93.8%
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93.8%
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93.8%
93.8%
93.9%
93.9%
93.9%
93.9%
93.9%
93.9%
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93.9%
94.0%
94.0%
94.0%
94.0%
94.0%
94.0%
94.0%
94.0%
94.0%
94.0%
94.1%
94.1%
94.1%
94.1%
94.1%
94.1%
94.1%
94.1%
94.1%
94.2%
94.2%
94.2%
94.2%
94.2%
94.2%
94.2%
94.2%
94.2%
94.2%
94.3%
94.3%
94.3%
94.3%
94.3%
94.3%
94.3%
94.3%
94.3%
94.3%
94.4%
94.4%
94.4%
94.4%
94.4%
94.4%
94.4%
94.4%
94.4%
94.5%
94.5%
94.5%
94.5%
94.5%
94.5%
94.5%
94.5%
94.5%
94.5%
94.6%
94.6%
94.6%
94.6%
94.6%
94.6%
94.6%
94.6%
94.6%
94.6%
94.7%
94.7%
94.7%
94.7%
94.7%
94.7%
94.7%
94.7%
94.7%
94.8%
94.8%
94.8%
94.8%
94.8%
94.8%
94.8%
94.8%
94.8%
94.8%
94.9%
94.9%
94.9%
94.9%
94.9%
94.9%
94.9%
94.9%
94.9%
94.9%
95.0%
95.0%
95.0%
95.0%
95.0%
95.0%
95.0%
95.0%
95.0%
95.1%
95.1%
95.1%
95.1%
95.1%
95.1%
95.1%
95.1%
95.1%
95.1%
95.2%
95.2%
95.2%
95.2%
95.2%
95.2%
95.2%
95.2%
95.2%
95.2%
95.3%
95.3%
95.3%
95.3%
95.3%
95.3%
95.3%
95.3%
95.3%
95.4%
95.4%
95.4%
95.4%
95.4%
95.4%
95.4%
95.4%
95.4%
95.4%
95.5%
95.5%
95.5%
95.5%
95.5%
95.5%
95.5%
95.5%
95.5%
95.5%
95.6%
95.6%
95.6%
95.6%
95.6%
95.6%
95.6%
95.6%
95.6%
95.6%
95.7%
95.7%
95.7%
95.7%
95.7%
95.7%
95.7%
95.7%
95.7%
95.8%
95.8%
95.8%
95.8%
95.8%
95.8%
95.8%
95.8%
95.8%
95.8%
95.9%
95.9%
95.9%
95.9%
95.9%
95.9%
95.9%
95.9%
95.9%
95.9%
96.0%
96.0%
96.0%
96.0%
96.0%
96.0%
96.0%
96.0%
96.0%
96.1%
96.1%
96.1%
96.1%
96.1%
96.1%
96.1%
96.1%
96.1%
96.1%
96.2%
96.2%
96.2%
96.2%
96.2%
96.2%
96.2%
96.2%
96.2%
96.2%
96.3%
96.3%
96.3%
96.3%
96.3%
96.3%
96.3%
96.3%
96.3%
96.4%
96.4%
96.4%
96.4%
96.4%
96.4%
96.4%
96.4%
96.4%
96.4%
96.5%
96.5%
96.5%
96.5%
96.5%
96.5%
96.5%
96.5%
96.5%
96.5%
96.6%
96.6%
96.6%
96.6%
96.6%
96.6%
96.6%
96.6%
96.6%
96.7%
96.7%
96.7%
96.7%
96.7%
96.7%
96.7%
96.7%
96.7%
96.7%
96.8%
96.8%
96.8%
96.8%
96.8%
96.8%
96.8%
96.8%
96.8%
96.8%
96.9%
96.9%
96.9%
96.9%
96.9%
96.9%
96.9%
96.9%
96.9%
97.0%
97.0%
97.0%
97.0%
97.0%
97.0%
97.0%
97.0%
97.0%
97.0%
97.1%
97.1%
97.1%
97.1%
97.1%
97.1%
97.1%
97.1%
97.1%
97.1%
97.2%
97.2%
97.2%
97.2%
97.2%
97.2%
97.2%
97.2%
97.2%
97.3%
97.3%
97.3%
97.3%
97.3%
97.3%
97.3%
97.3%
97.3%
97.3%
97.4%
97.4%
97.4%
97.4%
97.4%
97.4%
97.4%
97.4%
97.4%
97.4%
97.5%
97.5%
97.5%
97.5%
97.5%
97.5%
97.5%
97.5%
97.5%
97.6%
97.6%
97.6%
97.6%
97.6%
97.6%
97.6%
97.6%
97.6%
97.6%
97.7%
97.7%
97.7%
97.7%
97.7%
97.7%
97.7%
97.7%
97.7%
97.7%
97.8%
97.8%
97.8%
97.8%
97.8%
97.8%
97.8%
97.8%
97.8%
97.9%
97.9%
97.9%
97.9%
97.9%
97.9%
97.9%
97.9%
97.9%
97.9%
98.0%
98.0%
98.0%
98.0%
98.0%
98.0%
98.0%
98.0%
98.0%
98.0%
98.1%
98.1%
98.1%
98.1%
98.1%
98.1%
98.1%
98.1%
98.1%
98.1%
98.2%
98.2%
98.2%
98.2%
98.2%
98.2%
98.2%
98.2%
98.2%
98.3%
98.3%
98.3%
98.3%
98.3%
98.3%
98.3%
98.3%
98.3%
98.3%
98.4%
98.4%
98.4%
98.4%
98.4%
98.4%
98.4%
98.4%
98.4%
98.4%
98.5%
98.5%
98.5%
98.5%
98.5%
98.5%
98.5%
98.5%
98.5%
98.6%
98.6%
98.6%
98.6%
98.6%
98.6%
98.6%
98.6%
98.6%
98.6%
98.7%
98.7%
98.7%
98.7%
98.7%
98.7%
98.7%
98.7%
98.7%
98.7%
98.8%
98.8%
98.8%
98.8%
98.8%
98.8%
98.8%
98.8%
98.8%
98.9%
98.9%
98.9%
98.9%
98.9%
98.9%
98.9%
98.9%
98.9%
98.9%
99.0%
99.0%
99.0%
99.0%
99.0%
99.0%
99.0%
99.0%
99.0%
99.0%
99.1%
99.1%
99.1%
99.1%
99.1%
99.1%
99.1%
99.1%
99.1%
99.2%
99.2%
99.2%
99.2%
99.2%
99.2%
99.2%
99.2%
99.2%
99.2%
99.3%
99.3%
99.3%
99.3%
99.3%
99.3%
99.3%
99.3%
99.3%
99.3%
99.4%
99.4%
99.4%
99.4%
99.4%
99.4%
99.4%
99.4%
99.4%
99.5%
99.5%
99.5%
99.5%
99.5%
99.5%
99.5%
99.5%
99.5%
99.5%
99.6%
99.6%
99.6%
99.6%
99.6%
99.6%
99.6%
99.6%
99.6%
99.6%
99.7%
99.7%
99.7%
99.7%
99.7%
99.7%
99.7%
99.7%
99.7%
99.8%
99.8%
99.8%
99.8%
99.8%
99.8%
99.8%
99.8%
99.8%
99.8%
99.9%
99.9%
99.9%
99.9%
99.9%
99.9%
99.9%
99.9%
99.9%
99.9%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
Extracting .data/MNIST/raw/train-images-idx3-ubyte.gz to .data/MNIST/raw

Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to .data/MNIST/raw/train-labels-idx1-ubyte.gz

3.5%
7.1%
10.6%
14.2%
17.7%
21.3%
24.8%
28.4%
31.9%
35.5%
39.0%
42.5%
46.1%
49.6%
53.2%
56.7%
60.3%
63.8%
67.4%
70.9%
74.5%
78.0%
81.5%
85.1%
88.6%
92.2%
95.7%
99.3%
102.8%
Extracting .data/MNIST/raw/train-labels-idx1-ubyte.gz to .data/MNIST/raw

Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to .data/MNIST/raw/t10k-images-idx3-ubyte.gz

0.1%
0.1%
0.2%
0.2%
0.3%
0.4%
0.4%
0.5%
0.6%
0.6%
0.7%
0.7%
0.8%
0.9%
0.9%
1.0%
1.1%
1.1%
1.2%
1.2%
1.3%
1.4%
1.4%
1.5%
1.6%
1.6%
1.7%
1.7%
1.8%
1.9%
1.9%
2.0%
2.0%
2.1%
2.2%
2.2%
2.3%
2.4%
2.4%
2.5%
2.5%
2.6%
2.7%
2.7%
2.8%
2.9%
2.9%
3.0%
3.0%
3.1%
3.2%
3.2%
3.3%
3.4%
3.4%
3.5%
3.5%
3.6%
3.7%
3.7%
3.8%
3.9%
3.9%
4.0%
4.0%
4.1%
4.2%
4.2%
4.3%
4.3%
4.4%
4.5%
4.5%
4.6%
4.7%
4.7%
4.8%
4.8%
4.9%
5.0%
5.0%
5.1%
5.2%
5.2%
5.3%
5.3%
5.4%
5.5%
5.5%
5.6%
5.7%
5.7%
5.8%
5.8%
5.9%
6.0%
6.0%
6.1%
6.1%
6.2%
6.3%
6.3%
6.4%
6.5%
6.5%
6.6%
6.6%
6.7%
6.8%
6.8%
6.9%
7.0%
7.0%
7.1%
7.1%
7.2%
7.3%
7.3%
7.4%
7.5%
7.5%
7.6%
7.6%
7.7%
7.8%
7.8%
7.9%
7.9%
8.0%
8.1%
8.1%
8.2%
8.3%
8.3%
8.4%
8.4%
8.5%
8.6%
8.6%
8.7%
8.8%
8.8%
8.9%
8.9%
9.0%
9.1%
9.1%
9.2%
9.3%
9.3%
9.4%
9.4%
9.5%
9.6%
9.6%
9.7%
9.8%
9.8%
9.9%
9.9%
10.0%
10.1%
10.1%
10.2%
10.2%
10.3%
10.4%
10.4%
10.5%
10.6%
10.6%
10.7%
10.7%
10.8%
10.9%
10.9%
11.0%
11.1%
11.1%
11.2%
11.2%
11.3%
11.4%
11.4%
11.5%
11.6%
11.6%
11.7%
11.7%
11.8%
11.9%
11.9%
12.0%
12.0%
12.1%
12.2%
12.2%
12.3%
12.4%
12.4%
12.5%
12.5%
12.6%
12.7%
12.7%
12.8%
12.9%
12.9%
13.0%
13.0%
13.1%
13.2%
13.2%
13.3%
13.4%
13.4%
13.5%
13.5%
13.6%
13.7%
13.7%
13.8%
13.8%
13.9%
14.0%
14.0%
14.1%
14.2%
14.2%
14.3%
14.3%
14.4%
14.5%
14.5%
14.6%
14.7%
14.7%
14.8%
14.8%
14.9%
15.0%
15.0%
15.1%
15.2%
15.2%
15.3%
15.3%
15.4%
15.5%
15.5%
15.6%
15.6%
15.7%
15.8%
15.8%
15.9%
16.0%
16.0%
16.1%
16.1%
16.2%
16.3%
16.3%
16.4%
16.5%
16.5%
16.6%
16.6%
16.7%
16.8%
16.8%
16.9%
17.0%
17.0%
17.1%
17.1%
17.2%
17.3%
17.3%
17.4%
17.5%
17.5%
17.6%
17.6%
17.7%
17.8%
17.8%
17.9%
17.9%
18.0%
18.1%
18.1%
18.2%
18.3%
18.3%
18.4%
18.4%
18.5%
18.6%
18.6%
18.7%
18.8%
18.8%
18.9%
18.9%
19.0%
19.1%
19.1%
19.2%
19.3%
19.3%
19.4%
19.4%
19.5%
19.6%
19.6%
19.7%
19.7%
19.8%
19.9%
19.9%
20.0%
20.1%
20.1%
20.2%
20.2%
20.3%
20.4%
20.4%
20.5%
20.6%
20.6%
20.7%
20.7%
20.8%
20.9%
20.9%
21.0%
21.1%
21.1%
21.2%
21.2%
21.3%
21.4%
21.4%
21.5%
21.5%
21.6%
21.7%
21.7%
21.8%
21.9%
21.9%
22.0%
22.0%
22.1%
22.2%
22.2%
22.3%
22.4%
22.4%
22.5%
22.5%
22.6%
22.7%
22.7%
22.8%
22.9%
22.9%
23.0%
23.0%
23.1%
23.2%
23.2%
23.3%
23.4%
23.4%
23.5%
23.5%
23.6%
23.7%
23.7%
23.8%
23.8%
23.9%
24.0%
24.0%
24.1%
24.2%
24.2%
24.3%
24.3%
24.4%
24.5%
24.5%
24.6%
24.7%
24.7%
24.8%
24.8%
24.9%
25.0%
25.0%
25.1%
25.2%
25.2%
25.3%
25.3%
25.4%
25.5%
25.5%
25.6%
25.6%
25.7%
25.8%
25.8%
25.9%
26.0%
26.0%
26.1%
26.1%
26.2%
26.3%
26.3%
26.4%
26.5%
26.5%
26.6%
26.6%
26.7%
26.8%
26.8%
26.9%
27.0%
27.0%
27.1%
27.1%
27.2%
27.3%
27.3%
27.4%
27.4%
27.5%
27.6%
27.6%
27.7%
27.8%
27.8%
27.9%
27.9%
28.0%
28.1%
28.1%
28.2%
28.3%
28.3%
28.4%
28.4%
28.5%
28.6%
28.6%
28.7%
28.8%
28.8%
28.9%
28.9%
29.0%
29.1%
29.1%
29.2%
29.3%
29.3%
29.4%
29.4%
29.5%
29.6%
29.6%
29.7%
29.7%
29.8%
29.9%
29.9%
30.0%
30.1%
30.1%
30.2%
30.2%
30.3%
30.4%
30.4%
30.5%
30.6%
30.6%
30.7%
30.7%
30.8%
30.9%
30.9%
31.0%
31.1%
31.1%
31.2%
31.2%
31.3%
31.4%
31.4%
31.5%
31.5%
31.6%
31.7%
31.7%
31.8%
31.9%
31.9%
32.0%
32.0%
32.1%
32.2%
32.2%
32.3%
32.4%
32.4%
32.5%
32.5%
32.6%
32.7%
32.7%
32.8%
32.9%
32.9%
33.0%
33.0%
33.1%
33.2%
33.2%
33.3%
33.3%
33.4%
33.5%
33.5%
33.6%
33.7%
33.7%
33.8%
33.8%
33.9%
34.0%
34.0%
34.1%
34.2%
34.2%
34.3%
34.3%
34.4%
34.5%
34.5%
34.6%
34.7%
34.7%
34.8%
34.8%
34.9%
35.0%
35.0%
35.1%
35.2%
35.2%
35.3%
35.3%
35.4%
35.5%
35.5%
35.6%
35.6%
35.7%
35.8%
35.8%
35.9%
36.0%
36.0%
36.1%
36.1%
36.2%
36.3%
36.3%
36.4%
36.5%
36.5%
36.6%
36.6%
36.7%
36.8%
36.8%
36.9%
37.0%
37.0%
37.1%
37.1%
37.2%
37.3%
37.3%
37.4%
37.4%
37.5%
37.6%
37.6%
37.7%
37.8%
37.8%
37.9%
37.9%
38.0%
38.1%
38.1%
38.2%
38.3%
38.3%
38.4%
38.4%
38.5%
38.6%
38.6%
38.7%
38.8%
38.8%
38.9%
38.9%
39.0%
39.1%
39.1%
39.2%
39.2%
39.3%
39.4%
39.4%
39.5%
39.6%
39.6%
39.7%
39.7%
39.8%
39.9%
39.9%
40.0%
40.1%
40.1%
40.2%
40.2%
40.3%
40.4%
40.4%
40.5%
40.6%
40.6%
40.7%
40.7%
40.8%
40.9%
40.9%
41.0%
41.0%
41.1%
41.2%
41.2%
41.3%
41.4%
41.4%
41.5%
41.5%
41.6%
41.7%
41.7%
41.8%
41.9%
41.9%
42.0%
42.0%
42.1%
42.2%
42.2%
42.3%
42.4%
42.4%
42.5%
42.5%
42.6%
42.7%
42.7%
42.8%
42.9%
42.9%
43.0%
43.0%
43.1%
43.2%
43.2%
43.3%
43.3%
43.4%
43.5%
43.5%
43.6%
43.7%
43.7%
43.8%
43.8%
43.9%
44.0%
44.0%
44.1%
44.2%
44.2%
44.3%
44.3%
44.4%
44.5%
44.5%
44.6%
44.7%
44.7%
44.8%
44.8%
44.9%
45.0%
45.0%
45.1%
45.1%
45.2%
45.3%
45.3%
45.4%
45.5%
45.5%
45.6%
45.6%
45.7%
45.8%
45.8%
45.9%
46.0%
46.0%
46.1%
46.1%
46.2%
46.3%
46.3%
46.4%
46.5%
46.5%
46.6%
46.6%
46.7%
46.8%
46.8%
46.9%
46.9%
47.0%
47.1%
47.1%
47.2%
47.3%
47.3%
47.4%
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Extracting .data/MNIST/raw/t10k-images-idx3-ubyte.gz to .data/MNIST/raw

Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to .data/MNIST/raw/t10k-labels-idx1-ubyte.gz

22.5%
45.1%
67.6%
90.2%
112.7%
Extracting .data/MNIST/raw/t10k-labels-idx1-ubyte.gz to .data/MNIST/raw

/projects/800fec81-81db-4589-8df3-d839b1d21871/.local/lib/python3.8/site-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:180.)
  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)
/projects/800fec81-81db-4589-8df3-d839b1d21871/.local/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)
  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
Iteration: 50, Loss: 0.34183579683303833, Accuracy: 93.13999938964844%
Iteration: 100, Loss: 0.12257936596870422, Accuracy: 92.30999755859375%
Iteration: 150, Loss: 0.04345181956887245, Accuracy: 95.83999633789062%
Iteration: 200, Loss: 0.1509581059217453, Accuracy: 96.37999725341797%
Iteration: 250, Loss: 0.15181449055671692, Accuracy: 96.83999633789062%
Iteration: 300, Loss: 0.22155368328094482, Accuracy: 96.75%
^CTraceback (most recent call last):
  File "mnist.py", line 100, in <module>
    outputs = model(test)
  File "/projects/800fec81-81db-4589-8df3-d839b1d21871/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "mnist.py", line 53, in forward
    out = self.layer1(x)
  File "/projects/800fec81-81db-4589-8df3-d839b1d21871/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "/projects/800fec81-81db-4589-8df3-d839b1d21871/.local/lib/python3.8/site-packages/torch/nn/modules/container.py", line 139, in forward
    input = module(input)
  File "/projects/800fec81-81db-4589-8df3-d839b1d21871/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "/projects/800fec81-81db-4589-8df3-d839b1d21871/.local/lib/python3.8/site-packages/torch/nn/modules/batchnorm.py", line 167, in forward
    return F.batch_norm(
  File "/projects/800fec81-81db-4589-8df3-d839b1d21871/.local/lib/python3.8/site-packages/torch/nn/functional.py", line 2281, in batch_norm
    return torch.batch_norm(
KeyboardInterrupt

]0;~/PyTorch~/PyTorch$ ichmo./mnist.py
bash: ./mnist.py: Permission denied
]0;~/PyTorch~/PyTorch$ chmod +x mnist.py
]0;~/PyTorch~/PyTorch$ chmod +x mnist.py
./mnist.py
/projects/800fec81-81db-4589-8df3-d839b1d21871/.local/lib/python3.8/site-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:180.)
  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)
/projects/800fec81-81db-4589-8df3-d839b1d21871/.local/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)
  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
Iteration: 50, Loss: 0.47505661845207214, Accuracy: 90.05999755859375%
Iteration: 100, Loss: 0.14079254865646362, Accuracy: 92.63999938964844%
Iteration: 150, Loss: 0.11672891676425934, Accuracy: 96.7699966430664%
Iteration: 200, Loss: 0.029581820592284203, Accuracy: 97.48999786376953%
Iteration: 250, Loss: 0.1358642429113388, Accuracy: 96.41999816894531%
Iteration: 300, Loss: 0.10600727051496506, Accuracy: 96.94000244140625%
Iteration: 350, Loss: 0.17078223824501038, Accuracy: 95.8499984741211%
Iteration: 400, Loss: 0.19208943843841553, Accuracy: 96.83000183105469%
Iteration: 450, Loss: 0.06547720730304718, Accuracy: 97.08999633789062%
Iteration: 500, Loss: 0.11516312509775162, Accuracy: 95.72000122070312%
Iteration: 550, Loss: 0.05065403878688812, Accuracy: 97.6500015258789%
Iteration: 600, Loss: 0.26968786120414734, Accuracy: 97.29000091552734%
Iteration: 650, Loss: 0.1269403100013733, Accuracy: 97.73999786376953%
Iteration: 700, Loss: 0.053299155086278915, Accuracy: 96.3499984741211%
Iteration: 750, Loss: 0.021617718040943146, Accuracy: 98.37999725341797%
Iteration: 800, Loss: 0.01600886881351471, Accuracy: 98.04000091552734%
Iteration: 850, Loss: 0.08215320855379105, Accuracy: 97.7300033569336%
Iteration: 900, Loss: 0.08939420431852341, Accuracy: 98.43000030517578%
Iteration: 950, Loss: 0.13746267557144165, Accuracy: 97.7699966430664%
Iteration: 1000, Loss: 0.1010037511587143, Accuracy: 97.05000305175781%
Iteration: 1050, Loss: 0.014871266670525074, Accuracy: 97.76000213623047%
Iteration: 1100, Loss: 0.09214109182357788, Accuracy: 97.86000061035156%
Iteration: 1150, Loss: 0.02662852220237255, Accuracy: 97.87999725341797%
Iteration: 1200, Loss: 0.2543987035751343, Accuracy: 98.48999786376953%
Iteration: 1250, Loss: 0.03473915159702301, Accuracy: 98.18000030517578%
Iteration: 1300, Loss: 0.03924868628382683, Accuracy: 97.06999969482422%
Iteration: 1350, Loss: 0.06451206654310226, Accuracy: 98.37999725341797%
Iteration: 1400, Loss: 0.029841933399438858, Accuracy: 98.36000061035156%
Iteration: 1450, Loss: 0.07927007973194122, Accuracy: 98.0999984741211%
Iteration: 1500, Loss: 0.040941592305898666, Accuracy: 98.69000244140625%
Iteration: 1550, Loss: 0.030843660235404968, Accuracy: 97.8499984741211%
Iteration: 1600, Loss: 0.09047156572341919, Accuracy: 96.19999694824219%
Iteration: 1650, Loss: 0.012048683129251003, Accuracy: 98.68000030517578%
Iteration: 1700, Loss: 0.07744283229112625, Accuracy: 98.61000061035156%
Iteration: 1750, Loss: 0.022434061393141747, Accuracy: 98.16999816894531%
Iteration: 1800, Loss: 0.19573909044265747, Accuracy: 98.36000061035156%
Iteration: 1850, Loss: 0.019937338307499886, Accuracy: 98.19999694824219%
Iteration: 1900, Loss: 0.010635974816977978, Accuracy: 98.5199966430664%
Iteration: 1950, Loss: 0.02798471227288246, Accuracy: 98.87000274658203%
Iteration: 2000, Loss: 0.004890498239547014, Accuracy: 98.5999984741211%
Iteration: 2050, Loss: 0.07468106597661972, Accuracy: 98.26000213623047%
Iteration: 2100, Loss: 0.01748150959610939, Accuracy: 98.47000122070312%
Iteration: 2150, Loss: 0.021531887352466583, Accuracy: 98.51000213623047%
Iteration: 2200, Loss: 0.08161243796348572, Accuracy: 96.16000366210938%
Iteration: 2250, Loss: 0.029564548283815384, Accuracy: 98.2699966430664%
Iteration: 2300, Loss: 0.06717827171087265, Accuracy: 98.5%
Iteration: 2350, Loss: 0.014128911308944225, Accuracy: 98.62000274658203%
Iteration: 2400, Loss: 0.1611928790807724, Accuracy: 98.37999725341797%
Iteration: 2450, Loss: 0.08816828578710556, Accuracy: 98.5199966430664%
Iteration: 2500, Loss: 0.028569968417286873, Accuracy: 98.69000244140625%
Iteration: 2550, Loss: 0.020741326734423637, Accuracy: 98.91999816894531%
Iteration: 2600, Loss: 0.009847820736467838, Accuracy: 98.58000183105469%
Iteration: 2650, Loss: 0.03623354807496071, Accuracy: 98.30000305175781%
Iteration: 2700, Loss: 0.004418815020471811, Accuracy: 98.55000305175781%
Iteration: 2750, Loss: 0.07973592728376389, Accuracy: 98.3499984741211%
Iteration: 2800, Loss: 0.03026372380554676, Accuracy: 98.41000366210938%
Iteration: 2850, Loss: 0.003642548806965351, Accuracy: 98.52999877929688%
Iteration: 2900, Loss: 0.059621091932058334, Accuracy: 98.2699966430664%
Iteration: 2950, Loss: 0.023448023945093155, Accuracy: 98.54000091552734%
Iteration: 3000, Loss: 0.2632042467594147, Accuracy: 98.77999877929688%
Saved PyTorch Model State to model.pth
]0;~/PyTorch~/PyTorch$ 
]0;~/PyTorch~/PyTorch$ 
]0;~/PyTorch~/PyTorch$ git add -A
]0;~/PyTorch~/PyTorch$ git commit
hint: Waiting for your editor to close the file... [?1049h[?1h=[?25l"~/PyTorch/.git/COMMIT_EDITMSG" 10L, 265C# Please enter the commit message for your changes. Lines starting
# with '#' will be ignored, and an empty message aborts the commit.
#
# On branch main
# Your branch is up to date with 'origin/main'.
#
# Changes to be committed:
#       modified:   .mnist.py-0.term
#1,0-1All[?25h[?25lA -- INSERT --1,1All[?25h[?25ld2[?25h[?25l1[?25h[?25lA2[?25h[?25ld3[?25h[?25ld4[?25h[?25l 5[?25h[?25lc6[?25h[?25lo7[?25h[?25l6[?25h[?25l5[?25h[?25l"6[?25h[?25l^[  1,5All[?25h[?25l"[?25h[?25lq[?25h[?25l::[?25hq!
[?25l[?1l>[?25h[?1049l
Aborting commit due to empty commit message.
]0;~/PyTorch~/PyTorch$ git commit
hint: Waiting for your editor to close the file... [?1049h[?1h=[?25l"~/PyTorch/.git/COMMIT_EDITMSG" 10L, 265C# Please enter the commit message for your changes. Lines starting
# with '#' will be ignored, and an empty message aborts the commit.
#
# On branch main
# Your branch is up to date with 'origin/main'.
#
# Changes to be committed:
#       modified:   .mnist.py-0.term
#1,0-1All[?25h[?25li -- INSERT --1,1All[?25h[?25lA2[?25h[?25ld3[?25h[?25ld4[?25h[?25l 5[?25h[?25lC6[?25h[?25lo7[?25h[?25lC8[?25h[?25la9[?25h[?25ll10[?25h[?25lc1[?25h[?25l 2[?25h[?25lf3[?25h[?25li4[?25h[?25ll5[?25h[?25le6[?25h[?25ls7[?25h[?25l^[  1,16All[?25h[?25l::[?25hwq
[?25l".git/COMMIT_EDITMSG" 10L, 281C written


[?1l>[?25h[?1049l
[main ada4698] Add CoCalc files
 1 file changed, 21 insertions(+), 1 deletion(-)
]0;~/PyTorch~/PyTorch$ git push
Enumerating objects: 5, done.
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Delta compression using up to 24 threads
Compressing objects:  33% (1/3)
Compressing objects:  66% (2/3)
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Compressing objects: 100% (3/3), done.
Writing objects:  33% (1/3)
Writing objects:  66% (2/3)
Writing objects: 100% (3/3)
Writing objects: 100% (3/3), 299 bytes | 299.00 KiB/s, done.
Total 3 (delta 2), reused 0 (delta 0)
remote: Resolving deltas:   0% (0/2)
remote: Resolving deltas:  50% (1/2)
remote: Resolving deltas: 100% (2/2)
remote: Resolving deltas: 100% (2/2), completed with 2 local objects.
To github.com:Ta180m/PyTorch.git
   965db73..ada4698  main -> main
]0;~/PyTorch~/PyTorch$ ]0;~/PyTorch~/PyTorch$ 
]0;~/PyTorch~/PyTorch$ ./nmnist.py 
/projects/800fec81-81db-4589-8df3-d839b1d21871/.local/lib/python3.8/site-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:180.)
  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)
/projects/800fec81-81db-4589-8df3-d839b1d21871/.local/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)
  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
Iteration: 50, Loss: 0.33904707431793213, Accuracy: 91.33999633789062%
Iteration: 100, Loss: 0.11570730060338974, Accuracy: 92.58000183105469%
Iteration: 150, Loss: 0.08451800048351288, Accuracy: 96.02999877929688%
Iteration: 200, Loss: 0.08893018960952759, Accuracy: 96.6500015258789%
Iteration: 250, Loss: 0.1327921748161316, Accuracy: 96.98999786376953%
Iteration: 300, Loss: 0.0620574913918972, Accuracy: 97.16999816894531%
Iteration: 350, Loss: 0.09436647593975067, Accuracy: 97.05999755859375%
Iteration: 400, Loss: 0.25261232256889343, Accuracy: 94.94000244140625%
Iteration: 450, Loss: 0.11671247333288193, Accuracy: 97.69000244140625%
Iteration: 500, Loss: 0.1204160675406456, Accuracy: 96.45999908447266%
Iteration: 550, Loss: 0.05714935436844826, Accuracy: 98.19999694824219%
Iteration: 600, Loss: 0.25882184505462646, Accuracy: 98.05999755859375%
Iteration: 650, Loss: 0.15288296341896057, Accuracy: 98.51000213623047%
Iteration: 700, Loss: 0.030312277376651764, Accuracy: 96.8499984741211%
Iteration: 750, Loss: 0.026591232046484947, Accuracy: 98.12000274658203%
Iteration: 800, Loss: 0.0336340069770813, Accuracy: 98.01000213623047%
Iteration: 850, Loss: 0.10625416785478592, Accuracy: 97.16000366210938%
Iteration: 900, Loss: 0.11373892426490784, Accuracy: 98.0199966430664%
Iteration: 950, Loss: 0.12648221850395203, Accuracy: 97.26000213623047%
Iteration: 1000, Loss: 0.19489459693431854, Accuracy: 97.1500015258789%
Iteration: 1050, Loss: 0.04832194745540619, Accuracy: 97.83000183105469%
^CTraceback (most recent call last):
  File "./mnist.py", line 89, in <module>
    loss.backward()
  File "/projects/800fec81-81db-4589-8df3-d839b1d21871/.local/lib/python3.8/site-packages/torch/_tensor.py", line 255, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
  File "/projects/800fec81-81db-4589-8df3-d839b1d21871/.local/lib/python3.8/site-packages/torch/autograd/__init__.py", line 147, in backward
    Variable._execution_engine.run_backward(
KeyboardInterrupt

]0;~/PyTorch~/PyTorch$ git add- A
git: 'add-' is not a git command. See 'git --help'.

The most similar command is
	add
]0;~/PyTorch~/PyTorch$ ggit add -A
]0;~/PyTorch~/PyTorch$ git commit
hint: Waiting for your editor to close the file... [?1049h[?1h=[?25l"~/PyTorch/.git/COMMIT_EDITMSG" 11L, 288C# Please enter the commit message for your changes. Lines starting
# with '#' will be ignored, and an empty message aborts the commit.
#
# On branch main
# Your branch is up to date with 'origin/main'.
#
# Changes to be committed:
#       modified:   .mnist.py-0.term
#       modified:   mnist.py1,0-1Top[?25h[?25li -- INSERT --1,1Top[?25h[?25lU2[?25h[?25ls3[?25h[?25le4[?25h[?25l 5[?25h[?25lC6[?25h[?25l5[?25h[?25lG6[?25h[?25lU7[?25h[?25l 8[?25h[?25li9[?25h[?25l8[?25h[?25l7[?25h[?25l6[?25h[?25lP7[?25h[?25lU8[?25h[?25l 9[?25h[?25li10[?25h[?25lf1[?25h[?25l 2[?25h[?25la3[?25h[?25lv4[?25h[?25la5[?25h[?25ll6[?25h[?25li7[?25h[?25la8[?25h[?25lb9[?25h[?25ll20[?25h[?25l19[?25h[?25l8[?25h[?25l7[?25h[?25l6[?25h[?25l5[?25h[?25li6[?25h[?25la7[?25h[?25lb8[?25h[?25ll9[?25h[?25le20[?25h[?25l19[?25h[?25l8[?25h[?25l7[?25h[?25l6[?25h[?25ll7[?25h[?25la8[?25h[?25lb9[?25h[?25ll20[?25h[?25le1[?25h[?25l^[  1,20Top[?25h[?25l::[?25hx
[?25l".git/COMMIT_EDITMSG" 11L, 308C written


[?1l>[?25h[?1049l
[main 438d211] Use GPU if available
 2 files changed, 93 insertions(+), 2 deletions(-)
]0;~/PyTorch~/PyTorch$ git push
Warning: Permanently added the RSA host key for IP address '140.82.112.3' to the list of known hosts.

Enumerating objects: 7, done.
Counting objects:  14% (1/7)
Counting objects:  28% (2/7)
Counting objects:  42% (3/7)
Counting objects:  57% (4/7)
Counting objects:  71% (5/7)
Counting objects:  85% (6/7)
Counting objects: 100% (7/7)
Counting objects: 100% (7/7), done.
Delta compression using up to 24 threads
Compressing objects:  25% (1/4)
Compressing objects:  50% (2/4)
Compressing objects:  75% (3/4)
Compressing objects: 100% (4/4)
Compressing objects: 100% (4/4), done.
Writing objects:  25% (1/4)
Writing objects:  50% (2/4)
Writing objects:  75% (3/4)
Writing objects: 100% (4/4)
Writing objects: 100% (4/4), 2.31 KiB | 2.31 MiB/s, done.
Total 4 (delta 3), reused 0 (delta 0)
remote: Resolving deltas:   0% (0/3)
remote: Resolving deltas:  33% (1/3)
remote: Resolving deltas:  66% (2/3)
remote: Resolving deltas: 100% (3/3)
remote: Resolving deltas: 100% (3/3), completed with 3 local objects.
To github.com:Ta180m/PyTorch.git
   ada4698..438d211  main -> main
]0;~/PyTorch~/PyTorch$ ]0;~/PyTorch~/PyTorch$ 
]0;~/PyTorch~/PyTorch$ ]0;~/PyTorch~/PyTorch$ python/mn./mnist.py 
/projects/800fec81-81db-4589-8df3-d839b1d21871/.local/lib/python3.8/site-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:180.)
  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)
/projects/800fec81-81db-4589-8df3-d839b1d21871/.local/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)
  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
^CTraceback (most recent call last):
  File "./mnist.py", line 95, in <module>
    loss.backward()
  File "/projects/800fec81-81db-4589-8df3-d839b1d21871/.local/lib/python3.8/site-packages/torch/_tensor.py", line 255, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
  File "/projects/800fec81-81db-4589-8df3-d839b1d21871/.local/lib/python3.8/site-packages/torch/autograd/__init__.py", line 147, in backward
    Variable._execution_engine.run_backward(
KeyboardInterrupt

]0;~/PyTorch~/PyTorch$ ]0;~/PyTorch~/PyTorch$ 
]0;~/PyTorch~/PyTorch$ ./mnist.py 
/projects/800fec81-81db-4589-8df3-d839b1d21871/.local/lib/python3.8/site-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:180.)
  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)
/projects/800fec81-81db-4589-8df3-d839b1d21871/.local/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)
  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
Iteration: 50, Loss: 0.2655351459980011, Accuracy: 93.6500015258789%
Iteration: 100, Loss: 0.09417656064033508, Accuracy: 93.77999877929688%
Iteration: 150, Loss: 0.14728593826293945, Accuracy: 96.0999984741211%
Iteration: 200, Loss: 0.08001164346933365, Accuracy: 97.04000091552734%
Iteration: 250, Loss: 0.15678852796554565, Accuracy: 96.12999725341797%
Iteration: 300, Loss: 0.09041783958673477, Accuracy: 96.87999725341797%
Iteration: 350, Loss: 0.10900043696165085, Accuracy: 97.08999633789062%
Iteration: 400, Loss: 0.2091383934020996, Accuracy: 96.8499984741211%
Iteration: 450, Loss: 0.012568566016852856, Accuracy: 97.58999633789062%
Iteration: 500, Loss: 0.09534303843975067, Accuracy: 95.9000015258789%
Iteration: 550, Loss: 0.12004967778921127, Accuracy: 97.94999694824219%
Iteration: 600, Loss: 0.26723435521125793, Accuracy: 96.75%
Iteration: 650, Loss: 0.10009327530860901, Accuracy: 98.08999633789062%
Iteration: 700, Loss: 0.03489111363887787, Accuracy: 95.26000213623047%
Iteration: 750, Loss: 0.030101114884018898, Accuracy: 98.12999725341797%
Iteration: 800, Loss: 0.05416644737124443, Accuracy: 97.61000061035156%
Iteration: 850, Loss: 0.11499112099409103, Accuracy: 97.8499984741211%
Iteration: 900, Loss: 0.20542272925376892, Accuracy: 97.66999816894531%
Iteration: 950, Loss: 0.05691840127110481, Accuracy: 97.88999938964844%
Iteration: 1000, Loss: 0.17045655846595764, Accuracy: 96.95999908447266%
Iteration: 1050, Loss: 0.028369026258587837, Accuracy: 98.19000244140625%
Iteration: 1100, Loss: 0.09225992113351822, Accuracy: 97.76000213623047%
Iteration: 1150, Loss: 0.038039229810237885, Accuracy: 98.22000122070312%
Iteration: 1200, Loss: 0.23273861408233643, Accuracy: 98.30000305175781%
Iteration: 1250, Loss: 0.08464375138282776, Accuracy: 98.66999816894531%
Iteration: 1300, Loss: 0.017008038237690926, Accuracy: 97.8499984741211%
Iteration: 1350, Loss: 0.05763726308941841, Accuracy: 98.63999938964844%
Iteration: 1400, Loss: 0.022395288571715355, Accuracy: 98.51000213623047%
Iteration: 1450, Loss: 0.06815487146377563, Accuracy: 98.51000213623047%
Iteration: 1500, Loss: 0.14768916368484497, Accuracy: 98.3499984741211%
Iteration: 1550, Loss: 0.021466469392180443, Accuracy: 98.52999877929688%
Iteration: 1600, Loss: 0.054903920739889145, Accuracy: 98.0999984741211%
Iteration: 1650, Loss: 0.009115751832723618, Accuracy: 98.44999694824219%
Iteration: 1700, Loss: 0.027846679091453552, Accuracy: 98.70999908447266%
Iteration: 1750, Loss: 0.019951678812503815, Accuracy: 98.5199966430664%
Iteration: 1800, Loss: 0.25205621123313904, Accuracy: 98.62000274658203%
Iteration: 1850, Loss: 0.02951984480023384, Accuracy: 98.62999725341797%
Iteration: 1900, Loss: 0.011210460215806961, Accuracy: 98.55000305175781%
Iteration: 1950, Loss: 0.05040852725505829, Accuracy: 98.5%
Iteration: 2000, Loss: 0.008486397564411163, Accuracy: 98.55999755859375%
Iteration: 2050, Loss: 0.059381142258644104, Accuracy: 98.61000061035156%
Iteration: 2100, Loss: 0.10324683040380478, Accuracy: 98.37000274658203%
Iteration: 2150, Loss: 0.06498480588197708, Accuracy: 98.16999816894531%
Iteration: 2200, Loss: 0.036080557852983475, Accuracy: 97.70999908447266%
Iteration: 2250, Loss: 0.013293210417032242, Accuracy: 98.66000366210938%
Iteration: 2300, Loss: 0.06331712007522583, Accuracy: 97.91000366210938%
Iteration: 2350, Loss: 0.004426905419677496, Accuracy: 98.02999877929688%
Iteration: 2400, Loss: 0.27985191345214844, Accuracy: 98.5%
Iteration: 2450, Loss: 0.04614001885056496, Accuracy: 98.5%
Iteration: 2500, Loss: 0.005236199591308832, Accuracy: 98.43000030517578%
Iteration: 2550, Loss: 0.026349853724241257, Accuracy: 98.43000030517578%
Iteration: 2600, Loss: 0.007622480392456055, Accuracy: 98.77999877929688%
Iteration: 2650, Loss: 0.04031902924180031, Accuracy: 98.58999633789062%
Iteration: 2700, Loss: 0.00840453989803791, Accuracy: 98.83999633789062%
Iteration: 2750, Loss: 0.07304922491312027, Accuracy: 98.19000244140625%
Iteration: 2800, Loss: 0.11154232174158096, Accuracy: 97.13999938964844%
Iteration: 2850, Loss: 0.014337321743369102, Accuracy: 98.37999725341797%
Iteration: 2900, Loss: 0.03685985505580902, Accuracy: 98.66999816894531%
Iteration: 2950, Loss: 0.018538275733590126, Accuracy: 98.58000183105469%
Iteration: 3000, Loss: 0.1944340467453003, Accuracy: 98.75%
Saved PyTorch Model State to model.pth
]0;~/PyTorch~/PyTorch$ exit
]0;~/PyTorch~/PyTorch$ ]0;~/PyTorch~/PyTorch$