1 files changed, 0 insertions, 53 deletions
diff --git a/fashion_mnist/fashion_mnist.py b/fashion_mnist/fashion_mnist.py
deleted file mode 100644
index 1c9dcf7..0000000
--- a/fashion_mnist/fashion_mnist.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import torch, torch.nn as nn
-from torchvision.datasets import FashionMNIST
-from torch.utils.data import DataLoader
-from torchvision import transforms
-
-class FashionMNIST_MLP(nn.Module):
-    def __init__(self, hidden_dim):
-        super().__init__()
-        self.layers = nn.Sequential(
-            nn.Flatten(),
-            nn.Linear(6 * 6, hidden_dim),
-            nn.ReLU(),
-            nn.Linear(hidden_dim, 2)
-        )
-    def forward(self, x):
-        return self.layers(x)
-
-transform = transforms.Compose([
-    transforms.Resize((6, 6)),
-    transforms.ToTensor(),
-])
-
-train_dataset = FashionMNIST('./', download=True, transform=transform, train=True)
-tshirts_trousers = [id for id, data in enumerate(train_dataset.targets) if data.item() == 0 or data.item() == 1]
-train_dataset = torch.utils.data.Subset(train_dataset, tshirts_trousers)
-
-trainloader = DataLoader(train_dataset, batch_size=128, shuffle=True)
-
-def train_model(name: str, dim):
-    net = FashionMNIST_MLP(hidden_dim=dim)
-    loss_fn = nn.CrossEntropyLoss()
-    optimizer = torch.optim.Adam(net.parameters(), lr=1e-4)
-
-    print(f"Training {name}...")
-    for epoch in range(100):
-        global loss
-        for data in trainloader:
-            inputs, targets = data
-            optimizer.zero_grad()
-            outputs = net(inputs)
-            loss = loss_fn(outputs, targets)
-            loss.backward()
-            optimizer.step()
-        if (epoch + 1) % 10 == 0:
-            print(f"  Epoch {epoch+1}, Loss: {loss.item():.4f}")
-    return net
-
-if __name__ == "__main__":
-    torch_net_a = train_model("Network A", 6).eval()
-    torch_net_b = train_model("Network B", 12).eval()
-
-    torch.onnx.export(torch_net_a, (torch.randn(1, 1, 6, 6),), "fashion_mnist_a.onnx")
-    torch.onnx.export(torch_net_b, (torch.randn(1, 1, 6, 6),), "fashion_mnist_b.onnx")