1 files changed, 111 insertions, 1 deletions
diff --git a/.mnist.py-0.term b/.mnist.py-0.term
index a297d46..6d15c1d 100644
--- a/.mnist.py-0.term
+++ b/.mnist.py-0.term
@@ -366,4 +366,114 @@ Iteration: 1050, Loss: 0.04832194745540619, Accuracy: 97.83000183105469%
     Variable._execution_engine.run_backward(
 KeyboardInterrupt
 
-]0;~/PyTorch[01;34m~/PyTorch[00m$ g
-\ No newline at end of file
+]0;~/PyTorch[01;34m~/PyTorch[00m$ git add- A
+git: 'add-' is not a git command. See 'git --help'.
+
+The most similar command is
+	add
+]0;~/PyTorch[01;34m~/PyTorch[00m$ g[Kgit add -A
+]0;~/PyTorch[01;34m~/PyTorch[00m$ git commit
+hint: Waiting for your editor to close the file... [?1049h[?1h=[1;11r[23m[24m[0m[H[J[?25l[11;1H"~/PyTorch/.git/COMMIT_EDITMSG" 11L, 288C[2;1H[34m# Please enter the commit message for your changes. Lines starting
+# with '#' will be ignored, and an empty message aborts the commit.
+#
+# On branch [0m[35mmain[0m
+[34m# Your branch is up to date with '[0m[35morigin/main[0m[34m'.
+#
+# [0m[35mChanges to be committed:[0m
+[34m#       [0m[32mmodified[0m[34m: [0m[31m  .mnist.py-0.term[0m
+[34m#       [0m[32mmodified[0m[34m: [0m[31m  mnist.py[0m[11;194H1,0-1[9CTop[1;1H[34h[?25h[?25l[11;184Hi[1;1H[11;184H [1;1H[11;1H[1m-- INSERT --[0m[11;13H[K[11;194H1,1[11CTop[1;1H[34h[?25h[?25l[33mU[0m[11;196H2[1;2H[34h[?25h[?25l[33ms[0m[11;196H3[1;3H[34h[?25h[?25l[33me[0m[11;196H4[1;4H[34h[?25h[?25l[33m [0m[11;196H5[1;5H[34h[?25h[?25l[33mC[0m[11;196H6[1;6H[34h[?25h[?25l[1;5H[K[11;196H5[1;5H[34h[?25h[?25l[33mG[0m[11;196H6[1;6H[34h[?25h[?25l[33mU[0m[11;196H7[1;7H[34h[?25h[?25l[33m [0m[11;196H8[1;8H[34h[?25h[?25l[33mi[0m[11;196H9[1;9H[34h[?25h[?25l[1;8H[K[11;196H8[1;8H[34h[?25h[?25l[1;7H[K[11;196H7[1;7H[34h[?25h[?25l[1;6H[K[11;196H6[1;6H[34h[?25h[?25l[33mP[0m[11;196H7[1;7H[34h[?25h[?25l[33mU[0m[11;196H8[1;8H[34h[?25h[?25l[33m [0m[11;196H9[1;9H[34h[?25h[?25l[33mi[0m[11;196H10[1;10H[34h[?25h[?25l[33mf[0m[11;197H1[1;11H[34h[?25h[?25l[33m [0m[11;197H2[1;12H[34h[?25h[?25l[33ma[0m[11;197H3[1;13H[34h[?25h[?25l[33mv[0m[11;197H4[1;14H[34h[?25h[?25l[33ma[0m[11;197H5[1;15H[34h[?25h[?25l[33ml[0m[11;197H6[1;16H[34h[?25h[?25l[33mi[0m[11;197H7[1;17H[34h[?25h[?25l[33ma[0m[11;197H8[1;18H[34h[?25h[?25l[33mb[0m[11;197H9[1;19H[34h[?25h[?25l[33ml[0m[11;196H20[1;20H[34h[?25h[?25l[1;19H[K[11;196H19[1;19H[34h[?25h[?25l[1;18H[K[11;197H8[1;18H[34h[?25h[?25l[1;17H[K[11;197H7[1;17H[34h[?25h[?25l[1;16H[K[11;197H6[1;16H[34h[?25h[?25l[1;15H[K[11;197H5[1;15H[34h[?25h[?25l[33mi[0m[11;197H6[1;16H[34h[?25h[?25l[33ma[0m[11;197H7[1;17H[34h[?25h[?25l[33mb[0m[11;197H8[1;18H[34h[?25h[?25l[33ml[0m[11;197H9[1;19H[34h[?25h[?25l[33me[0m[11;196H20[1;20H[34h[?25h[?25l[1;19H[K[11;196H19[1;19H[34h[?25h[?25l[1;18H[K[11;197H8[1;18H[34h[?25h[?25l[1;17H[K[11;197H7[1;17H[34h[?25h[?25l[1;16H[K[11;197H6[1;16H[34h[?25h[?25l[33ml[0m[11;197H7[1;17H[34h[?25h[?25l[33ma[0m[11;197H8[1;18H[34h[?25h[?25l[33mb[0m[11;197H9[1;19H[34h[?25h[?25l[33ml[0m[11;196H20[1;20H[34h[?25h[?25l[33me[0m[11;197H1[1;21H[34h[?25h[11;1H[K[1;20H[?25l[11;184H^[[1;20H[11;184H  [1;21H[11;194H1,20[10CTop[1;20H[34h[?25h[?25l[11;184H:[1;20H[11;184H[K[11;1H:[34h[?25hx
+[?25l".git/COMMIT_EDITMSG" 11L, 308C written
+[?1l>[34h[?25h[?1049l
+[K[main 438d211] Use GPU if available
+ 2 files changed, 93 insertions(+), 2 deletions(-)
+]0;~/PyTorch[01;34m~/PyTorch[00m$ 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)[K
+remote: Resolving deltas:  33% (1/3)[K
+remote: Resolving deltas:  66% (2/3)[K
+remote: Resolving deltas: 100% (3/3)[K
+remote: Resolving deltas: 100% (3/3), completed with 3 local objects.[K
+To github.com:Ta180m/PyTorch.git
+   ada4698..438d211  main -> main
+]0;~/PyTorch[01;34m~/PyTorch[00m$ ]0;~/PyTorch[01;34m~/PyTorch[00m$ 
+[K]0;~/PyTorch[01;34m~/PyTorch[00m$ [H[J]0;~/PyTorch[01;34m~/PyTorch[00m$ python[K/mn[K[K[K./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[01;34m~/PyTorch[00m$ [H[J]0;~/PyTorch[01;34m~/PyTorch[00m$ 
+[K]0;~/PyTorch[01;34m~/PyTorch[00m$ ./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[01;34m~/PyTorch[00m$ exit
+]0;~/PyTorch[01;34m~/PyTorch[00m$ ]0;~/PyTorch[01;34m~/PyTorch[00m$ 
+\ No newline at end of file