New ONNX ops and tests

New ops: Slice, Squeeze, Unsqueeze
New tests based on papers:
 - Wide-to-Deep, Deep-to-Wide Transformation
 - Pruining of stably inactive (always negative) and active (always
   positive) ReLUs

1 files changed, 2 insertions, 3 deletions

diff --git a/examples/mnist/mnist.py b/examples/mnist/mnist.py
index 0a81878..a1706be 100644
--- a/examples/mnist/mnist.py
+++ b/examples/mnist/mnist.py
@@ -24,7 +24,7 @@ def train_model(name: str, dim):
     optimizer = torch.optim.Adam(net.parameters(), lr=0.5e-4)
 
     print(f"Training {name} ({dim} neurons)...")
-    for epoch in range(100):
+    for epoch in range(10):
         global loss
         for data in trainloader:
             inputs, targets = data
@@ -33,8 +33,7 @@ def train_model(name: str, dim):
             loss = loss_fn(outputs, targets)
             loss.backward()
             optimizer.step()
-        if (epoch + 1) % 10 == 0:
-            print(f"  Epoch {epoch+1}, Loss: {loss.item():.4f}")
+        print(f"  Epoch {epoch+1}, Loss: {loss.item():.4f}")
     return net
 
 if __name__ == "__main__":