add model

model.py

@@ -0,0 +1,62 @@
+import torch
+import torch.nn as nn
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+
+class Linear(nn.Module):
+    def __init__(self, in_features: int, out_features: int):
+        super(Linear, self).__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+
+        self.weight = nn.Parameter(
+            (
+                torch.randn((self.in_features, self.out_features), device=device) * 0.1
+            ).requires_grad_()
+        )
+        self.bias = nn.Parameter(
+            (torch.randn(self.out_features, device=device) * 0.1).requires_grad_()
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x @ self.weight + self.bias
+
+
+class ReLU(nn.Module):
+    @staticmethod
+    def forward(x: torch.Tensor) -> torch.Tensor:
+        return torch.max(x, torch.tensor(0))
+
+
+class Sequential(nn.Module):
+    def __init__(self, *layers):
+        super(Sequential, self).__init__()
+        self.layers = nn.ModuleList(layers)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        for layer in self.layers:
+            x = layer(x)
+        return x
+
+
+class Flatten(nn.Module):
+    @staticmethod
+    def forward(x: torch.Tensor) -> torch.Tensor:
+        return x.view(x.size(0), -1)
+
+
+class DigitClassifier(nn.Module):
+    def __init__(self):
+        super(DigitClassifier, self).__init__()
+        self.main = Sequential(
+            Flatten(),
+            Linear(in_features=784, out_features=256),
+            ReLU(),
+            Linear(in_features=256, out_features=64),
+            ReLU(),
+            Linear(in_features=64, out_features=10),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.main(x)

model/digit_classifier.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5a3b2bdb0b1c1e16b95b0367795b98b77ed61309281cafb9d44956e599af7cab
-size 439575
+oid sha256:f0d2908a8b36b225cc6cb6eef0f8ef5fbcb660ef79a13b93c27df22082115a48
+size 875543