]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 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 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 0.0% 0.0% 0.0% 0.0% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.6% 0.6% 0.6% 0.6% 0.6% 0.7% 0.7% 0.7% 0.7% 0.7% 0.7% 0.7% 0.7% 0.7% 0.7% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.1% 1.1% 1.1% 1.1% 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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% 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99.3% 99.4% 99.4% 99.5% 99.6% 99.6% 99.7% 99.7% 99.8% 99.9% 99.9% 100.0% 100.0% 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 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. Counting objects: 20% (1/5) Counting objects: 40% (2/5) Counting objects: 60% (3/5) Counting objects: 80% (4/5) Counting objects: 100% (5/5) Counting objects: 100% (5/5), done. Delta compression using up to 24 threads Compressing objects: 33% (1/3) Compressing objects: 66% (2/3) Compressing objects: 100% (3/3) 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 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$ g