Reformat mnist.py code

7 files changed, 170 insertions, 22 deletions

diff --git a/..mnist.py-0-python3.term.init b/..mnist.py-0-python3.term.init
new file mode 100644
index 0000000..201ee17
--- /dev/null
+++ b/..mnist.py-0-python3.term.init
@@ -0,0 +1,21 @@
+# This initialization file is associated with your terminal in PyTorch/..mnist.py-0-python3.term.init.
+# It is automatically run whenever it starts up -- restart the terminal via Ctrl-d and Return-key.
+
+# Usually, your ~/.bashrc is executed and this behavior is emulated for completeness:
+source ~/.bashrc
+
+# You can export environment variables, e.g. to set custom GIT_* variables
+# https://git-scm.com/book/en/v2/Git-Internals-Environment-Variables
+#export GIT_AUTHOR_NAME="Your Name"
+#export GIT_AUTHOR_EMAIL="your@email.address"
+#export GIT_COMMITTER_NAME="Your Name"
+#export GIT_COMMITTER_EMAIL="your@email.address"
+
+# It is also possible to automatically start a program ...
+
+#sage
+#sage -ipython
+#top
+
+# ... or even define a terminal specific function.
+#hello () { echo "hello world"; }
diff --git a/.mnist.py-0-python3.term b/.mnist.py-0-python3.term
index 434e4b6..9463652 100644
--- a/.mnist.py-0-python3.term
+++ b/.mnist.py-0-python3.term
@@ -4,4 +4,31 @@ Type "help", "copyright", "credits" or "license" for more information.
 Python 3.8.10 (default, Jun  2 2021, 10:49:15) 

 [GCC 9.4.0] on linux

 Type "help", "copyright", "credits" or "license" for more information.

+>>> Python 3.8.10 (default, Jun  2 2021, 10:49:15) 

+[GCC 9.4.0] on linux

+Type "help", "copyright", "credits" or "license" for more information.

+>>> 
>>> mnist.py

+Traceback (most recent call last):

+  File "<stdin>", line 1, in <module>

+NameError: name 'mnist' is not defined

+>>> execfile('mnist.py')

+Traceback (most recent call last):

+  File "<stdin>", line 1, in <module>

+NameError: name 'execfile' is not defined

+>>> exec(open('mnist.py').read())

+/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: 0.39537739753723145, Accuracy: 92.41999816894531%

+Iteration: 100, Loss: 0.13765878975391388, Accuracy: 92.33000183105469%

+Iteration: 150, Loss: 0.13507212698459625, Accuracy: 96.91999816894531%

+^CTraceback (most recent call last):

+  File "<stdin>", line 1, in <module>

+  File "<string>", line 86, in <module>

+  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

 >>> 
\ No newline at end of file
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~/PyTorch$ g
\ No newline at end of file
+]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$ 
\ No newline at end of file
diff --git a/accuracy.png b/accuracy.png
index 8ce4df8..e2a9f96 100644
--- a/accuracy.png
+++ b/accuracy.png
diff --git a/loss.png b/loss.png
index c5626a4..6179f19 100644
--- a/loss.png
+++ b/loss.png
diff --git a/mnist.py b/mnist.py
index 95ca6ad..353a661 100755
--- a/mnist.py
+++ b/mnist.py
@@ -7,7 +7,6 @@ from torchvision import datasets
 from torchvision.transforms import ToTensor, Lambda, Compose
 import matplotlib.pyplot as plt
 
-
 training_data = datasets.MNIST(
     root=".data",
     train=True,
@@ -22,7 +21,6 @@ test_data = datasets.MNIST(
     transform=ToTensor(),
 )
 
-
 batch_size = 100
 
 train_loader = DataLoader(training_data, batch_size=batch_size)
@@ -34,18 +32,13 @@ class CNN(nn.Module):
         super(CNN, self).__init__()
 
         self.layer1 = nn.Sequential(
-            nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, padding=1),
-            nn.BatchNorm2d(32),
-            nn.ReLU(),
-            nn.MaxPool2d(kernel_size=2, stride=2)
-        )
+            nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3,
+                      padding=1), nn.BatchNorm2d(32), nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2))
         self.layer2 = nn.Sequential(
             nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3),
-            nn.BatchNorm2d(64),
-            nn.ReLU(),
-            nn.MaxPool2d(2)
-        )
-        self.fc1 = nn.Linear(in_features=64*6*6, out_features=600)
+            nn.BatchNorm2d(64), nn.ReLU(), nn.MaxPool2d(2))
+        self.fc1 = nn.Linear(in_features=64 * 6 * 6, out_features=600)
         self.drop = nn.Dropout2d(0.25)
         self.fc2 = nn.Linear(in_features=600, out_features=120)
         self.fc3 = nn.Linear(in_features=120, out_features=10)
@@ -61,7 +54,6 @@ class CNN(nn.Module):
         return out
 
 
-
 device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
 
 model = CNN()
@@ -70,7 +62,6 @@ error = nn.CrossEntropyLoss()
 learning_rate = 0.001
 optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
 
-
 num_epochs = 5
 count = 0
 
@@ -104,28 +95,27 @@ for epoch in range(num_epochs):
                 images, labels = images.to(device), labels.to(device)
 
                 labels_list.append(labels)
-            
+
                 test = Variable(images.view(batch_size, 1, 28, 28))
                 outputs = model(test)
-            
+
                 predictions = torch.max(outputs, 1)[1].to(device)
                 predictions_list.append(predictions)
                 correct += (predictions == labels).sum()
-            
+
                 total += len(labels)
-            
+
             accuracy = correct * batch_size / total
             loss_list.append(loss.data)
             iteration_list.append(count)
             accuracy_list.append(accuracy)
 
-            print("Iteration: {}, Loss: {}, Accuracy: {}%".format(count, loss.data, accuracy))
-
+            print("Iteration: {}, Loss: {}, Accuracy: {}%".format(
+                count, loss.data, accuracy))
 
 torch.save(model.state_dict(), "model.pth")
 print("Saved PyTorch Model State to model.pth")
 
-
 plt.plot(iteration_list, loss_list)
 plt.xlabel("No. of Iteration")
 plt.ylabel("Loss")
diff --git a/model.pth b/model.pth
index 75fb65e..ffc0569 100644
--- a/model.pth
+++ b/model.pth