added MNIST, changed cache and parser

1 files changed, 0 insertions, 45 deletions

diff --git a/examples/fashion_mnist/fashion_mnist.py b/examples/fashion_mnist/fashion_mnist.py
deleted file mode 100644
index 680f4eb..0000000
--- a/examples/fashion_mnist/fashion_mnist.py
+++ /dev/null
@@ -1,45 +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(28 * 28, hidden_dim),
-            nn.ReLU(),
-            nn.Linear(hidden_dim, 10),
-        )
-    def forward(self, x):
-        return self.layers(x)
-
-train_dataset = FashionMNIST('./', download=True, transform=transforms.ToTensor(), train=True)
-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} ({dim} neurons)...")
-    for epoch in range(10):
-        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) % 1 == 0:
-            print(f"  Epoch {epoch+1}, Loss: {loss.item():.4f}")
-    return net
-
-if __name__ == "__main__":
-    torch_net_a = train_model("Network A", 28).eval()
-    torch_net_b = train_model("Network B", 56).eval()
-
-    torch.onnx.export(torch_net_a, (torch.randn(1, 28, 28),), "fashion_mnist_a.onnx")
-    torch.onnx.export(torch_net_b, (torch.randn(1, 28, 28),), "fashion_mnist_b.onnx")