From fcbbc960f43137aa170b78ba0be2d89aec3bc766 Mon Sep 17 00:00:00 2001
From: ericmarin <maarin.eric@gmail.com>
Date: Mon, 13 Apr 2026 19:42:39 +0200
Subject: 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
---
 examples/mnist/mnist_wide_to_deep.py | 82 ++++++++++++++++++++++++++++++++++++
 1 file changed, 82 insertions(+)
 create mode 100644 examples/mnist/mnist_wide_to_deep.py

(limited to 'examples/mnist/mnist_wide_to_deep.py')

diff --git a/examples/mnist/mnist_wide_to_deep.py b/examples/mnist/mnist_wide_to_deep.py
new file mode 100644
index 0000000..64f4ec7
--- /dev/null
+++ b/examples/mnist/mnist_wide_to_deep.py
@@ -0,0 +1,82 @@
+import torch, torch.nn as nn
+from torchvision.datasets import MNIST
+from torch.utils.data import DataLoader
+from torchvision import transforms
+
+class Wide_MNIST_MLP(nn.Module):
+    def __init__(self, hidden_dim):
+        super().__init__()
+        self.flatten = nn.Flatten()
+        self.layers = nn.Sequential(
+            nn.Linear(28 * 28, hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, 10),
+        )
+    def forward(self, x):
+        x = self.flatten(x)
+        return self.layers(x)
+
+class Deep_MNIST_Block(nn.Module):
+    def __init__(self, w1, b1, w2):
+        super().__init__()
+        self.fc1 = nn.Linear(784, 1)
+        self.fc1.weight.data = w1.clone().unsqueeze(0)
+        self.fc1.bias.data = b1.clone().unsqueeze(0)
+        self.fc2 = nn.Linear(1, 10, bias=False)
+        self.fc2.weight.data = w2.clone().unsqueeze(1)
+        
+    def forward(self, x_s):
+        x, s = x_s
+        z = torch.relu(self.fc1(x))
+        ds = self.fc2(z)
+        return (x, s + ds)
+
+class Deep_MNIST_MLP(nn.Module):
+    def __init__(self, wide_net):
+        super().__init__()
+        self.flatten = nn.Flatten()
+        
+        w1 = wide_net.layers[0].weight.data
+        b1 = wide_net.layers[0].bias.data
+        w2 = wide_net.layers[2].weight.data
+        b2 = wide_net.layers[2].bias.data
+        num_neurons = w1.shape[0]
+        
+        self.blocks = nn.Sequential(*[
+            Deep_MNIST_Block(w1[j], b1[j], w2[:, j]) for j in range(num_neurons)
+        ])
+        self.final_bias = nn.Parameter(b2.clone())
+
+    def forward(self, x):
+        x = self.flatten(x)
+        s = torch.zeros(x.shape[0], 10, device=x.device)
+        _, final_s = self.blocks((x, s))
+        return final_s + self.final_bias
+
+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 = Wide_MNIST_MLP(hidden_dim=dim)
+    loss_fn = nn.CrossEntropyLoss()
+    optimizer = torch.optim.Adam(net.parameters(), lr=0.5e-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()
+        print(f"  Epoch {epoch+1}, Loss: {loss.item():.4f}")
+    return net
+
+if __name__ == "__main__":
+    torch_net_a = train_model("Wide Network", 8).eval()
+    torch_net_b = Deep_MNIST_MLP(torch_net_a).eval()
+
+    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")
-- 
cgit v1.2.3