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mnist_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cba2854b401f47b7c12136c72ef38cac53310ec9139d87b3eab5ba93cf14192e
+size 89975

model.py

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+# model.py
+
+import torch.nn as nn
+
+
+# neural network architecture
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
+        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
+        self.dropout = nn.Dropout2d()
+        self.fc1 = nn.Linear(320, 50)
+        self.fc2 = nn.Linear(50, 10)
+
+    def forward(self, x):
+        x = nn.functional.relu(nn.functional.max_pool2d(self.conv1(x), 2))
+        x = nn.functional.relu(nn.functional.max_pool2d(self.dropout(self.conv2(x)), 2))
+        x = x.view(-1, 320)
+        x = nn.functional.relu(self.fc1(x))
+        x = nn.functional.dropout(x, training=self.training)
+        x = self.fc2(x)
+        return nn.functional.log_softmax(x, dim=1)
+
+model = Net()

requirements.txt

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+torch
+torchvision
+transformers