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+]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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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% 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73.9% 74.0% 74.0% 74.1% 74.2% 74.2% 74.3% 74.3% 74.4% 74.5% 74.5% 74.6% 74.6% 74.7% 74.8% 74.8% 74.9% 75.0% 75.0% 75.1% 75.1% 75.2% 75.3% 75.3% 75.4% 75.5% 75.5% 75.6% 75.6% 75.7% 75.8% 75.8% 75.9% 76.0% 76.0% 76.1% 76.1% 76.2% 76.3% 76.3% 76.4% 76.4% 76.5% 76.6% 76.6% 76.7% 76.8% 76.8% 76.9% 76.9% 77.0% 77.1% 77.1% 77.2% 77.3% 77.3% 77.4% 77.4% 77.5% 77.6% 77.6% 77.7% 77.8% 77.8% 77.9% 77.9% 78.0% 78.1% 78.1% 78.2% 78.2% 78.3% 78.4% 78.4% 78.5% 78.6% 78.6% 78.7% 78.7% 78.8% 78.9% 78.9% 79.0% 79.1% 79.1% 79.2% 79.2% 79.3% 79.4% 79.4% 79.5% 79.6% 79.6% 79.7% 79.7% 79.8% 79.9% 79.9% 80.0% 80.1% 80.1% 80.2% 80.2% 80.3% 80.4% 80.4% 80.5% 80.5% 80.6% 80.7% 80.7% 80.8% 80.9% 80.9% 81.0% 81.0% 81.1% 81.2% 81.2% 81.3% 81.4% 81.4% 81.5% 81.5% 81.6% 81.7% 81.7% 81.8% 81.9% 81.9% 82.0% 82.0% 82.1% 82.2% 82.2% 82.3% 82.3% 82.4% 82.5% 82.5% 82.6% 82.7% 82.7% 82.8% 82.8% 82.9% 83.0% 83.0% 83.1% 83.2% 83.2% 83.3% 83.3% 83.4% 83.5% 83.5% 83.6% 83.7% 83.7% 83.8% 83.8% 83.9% 84.0% 84.0% 84.1% 84.1% 84.2% 84.3% 84.3% 84.4% 84.5% 84.5% 84.6% 84.6% 84.7% 84.8% 84.8% 84.9% 85.0% 85.0% 85.1% 85.1% 85.2% 85.3% 85.3% 85.4% 85.5% 85.5% 85.6% 85.6% 85.7% 85.8% 85.8% 85.9% 86.0% 86.0% 86.1% 86.1% 86.2% 86.3% 86.3% 86.4% 86.4% 86.5% 86.6% 86.6% 86.7% 86.8% 86.8% 86.9% 86.9% 87.0% 87.1% 87.1% 87.2% 87.3% 87.3% 87.4% 87.4% 87.5% 87.6% 87.6% 87.7% 87.8% 87.8% 87.9% 87.9% 88.0% 88.1% 88.1% 88.2% 88.2% 88.3% 88.4% 88.4% 88.5% 88.6% 88.6% 88.7% 88.7% 88.8% 88.9% 88.9% 89.0% 89.1% 89.1% 89.2% 89.2% 89.3% 89.4% 89.4% 89.5% 89.6% 89.6% 89.7% 89.7% 89.8% 89.9% 89.9% 90.0% 90.0% 90.1% 90.2% 90.2% 90.3% 90.4% 90.4% 90.5% 90.5% 90.6% 90.7% 90.7% 90.8% 90.9% 90.9% 91.0% 91.0% 91.1% 91.2% 91.2% 91.3% 91.4% 91.4% 91.5% 91.5% 91.6% 91.7% 91.7% 91.8% 91.9% 91.9% 92.0% 92.0% 92.1% 92.2% 92.2% 92.3% 92.3% 92.4% 92.5% 92.5% 92.6% 92.7% 92.7% 92.8% 92.8% 92.9% 93.0% 93.0% 93.1% 93.2% 93.2% 93.3% 93.3% 93.4% 93.5% 93.5% 93.6% 93.7% 93.7% 93.8% 93.8% 93.9% 94.0% 94.0% 94.1% 94.1% 94.2% 94.3% 94.3% 94.4% 94.5% 94.5% 94.6% 94.6% 94.7% 94.8% 94.8% 94.9% 95.0% 95.0% 95.1% 95.1% 95.2% 95.3% 95.3% 95.4% 95.5% 95.5% 95.6% 95.6% 95.7% 95.8% 95.8% 95.9% 95.9% 96.0% 96.1% 96.1% 96.2% 96.3% 96.3% 96.4% 96.4% 96.5% 96.6% 96.6% 96.7% 96.8% 96.8% 96.9% 96.9% 97.0% 97.1% 97.1% 97.2% 97.3% 97.3% 97.4% 97.4% 97.5% 97.6% 97.6% 97.7% 97.7% 97.8% 97.9% 97.9% 98.0% 98.1% 98.1% 98.2% 98.2% 98.3% 98.4% 98.4% 98.5% 98.6% 98.6% 98.7% 98.7% 98.8% 98.9% 98.9% 99.0% 99.1% 99.1% 99.2% 99.2% 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 <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$ \ No newline at end of file
generated by cgit v1.2.3-70-g09d2 (git 2.47.1) at 2024-12-22 19:17:05 +0000