1 files changed, 11 insertions, 11 deletions
diff --git a/examples/fashion_mnist/fashion_mnist.py b/examples/mnist/mnist.py
index 680f4eb..0a81878 100644
--- a/examples/fashion_mnist/fashion_mnist.py
+++ b/examples/mnist/mnist.py
@@ -1,9 +1,9 @@
 import torch, torch.nn as nn
-from torchvision.datasets import FashionMNIST
+from torchvision.datasets import MNIST
 from torch.utils.data import DataLoader
 from torchvision import transforms
 
-class FashionMNIST_MLP(nn.Module):
+class MNIST_MLP(nn.Module):
     def __init__(self, hidden_dim):
         super().__init__()
         self.layers = nn.Sequential(
@@ -15,16 +15,16 @@ class FashionMNIST_MLP(nn.Module):
     def forward(self, x):
         return self.layers(x)
 
-train_dataset = FashionMNIST('./', download=True, transform=transforms.ToTensor(), train=True)
+train_dataset = MNIST('./', 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)
+    net = MNIST_MLP(hidden_dim=dim)
     loss_fn = nn.CrossEntropyLoss()
-    optimizer = torch.optim.Adam(net.parameters(), lr=1e-4)
+    optimizer = torch.optim.Adam(net.parameters(), lr=0.5e-4)
 
     print(f"Training {name} ({dim} neurons)...")
-    for epoch in range(10):
+    for epoch in range(100):
         global loss
         for data in trainloader:
             inputs, targets = data
@@ -33,13 +33,13 @@ def train_model(name: str, dim):
             loss = loss_fn(outputs, targets)
             loss.backward()
             optimizer.step()
-        if (epoch + 1) % 1 == 0:
+        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", 28).eval()
-    torch_net_b = train_model("Network B", 56).eval()
+    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, 28, 28),), "fashion_mnist_a.onnx")
-    torch.onnx.export(torch_net_b, (torch.randn(1, 28, 28),), "fashion_mnist_b.onnx")
+    torch.onnx.export(torch_net_a, (torch.randn(1, 28, 28),), "mnist_a.onnx")
+    torch.onnx.export(torch_net_b, (torch.randn(1, 28, 28),), "mnist_b.onnx")