New ONNX ops and testsHEADmaster

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

12 files changed, 640 insertions, 33 deletions

diff --git a/docs/proof.md b/docs/proof.md
index 07717f9..99c88aa 100644
--- a/docs/proof.md
+++ b/docs/proof.md
@@ -37,17 +37,13 @@ By definition this interaction is equal to:
 By grouping the operations we get:
 ![Y = alpha * A * B + beta * C](https://latex.codecogs.com/svg.image?Y=\alpha\cdot&space;A\cdot&space;B+\beta\cdot&space;C&space;)
 
-### Flatten
-Just identity mapping because the wires are always Flatten.
+### Identiry / Flatten / Reshape / Squeeze / Unsqueeze
+Just identity mapping because wires represent a single element and they are not structured as Tensors.
 ![out_i ~ in_i](https://latex.codecogs.com/svg.image?out_i\sim&space;in_i)
 
 ### MatMul
 Equal to Gemm with ![alpha=1](https://latex.codecogs.com/svg.image?\inline&space;\alpha=1), ![beta=0](https://latex.codecogs.com/svg.image?\inline&space;\beta=0) and ![C=0](https://latex.codecogs.com/svg.image?\inline&space;&space;C=0).
 
-### Reshape
-Just identity mapping because the wires always Flatten.
-![out_i ~ in_i](https://latex.codecogs.com/svg.image?out_i\sim&space;in_i)
-
 ### Add
 ONNX Add node is defined as:
 ![C = A + B](https://latex.codecogs.com/svg.image?&space;C=A+B)
@@ -76,6 +72,13 @@ By definition this interaction is equal to:
 By grouping the operations we get:
 ![C = A - B](https://latex.codecogs.com/svg.image?C=A-B)
 
+### Slice
+ONNX Slice is defined as:
+![out_j = in_{start + (j * step)}](https://latex.codecogs.com/svg.image?&space;out_j=in_{start+(j*step)})
+
+The translations creates a wiring analog to the above definition:
+![out_j ~ in_{start + (j * step)}](https://latex.codecogs.com/svg.image?&space;out_j\sim&space;in_{start+(j*step)})
+
 ## Soundness of Interaction Rules
 ### Materialize
 The Materialize agent transforms a Linear agent into a tree of explicit mathematical operations
diff --git a/examples/iris/iris.py b/examples/iris/iris.py
index db631c0..84d1ac6 100644
--- a/examples/iris/iris.py
+++ b/examples/iris/iris.py
@@ -1,5 +1,4 @@
-import torch
-import torch.nn as nn
+import torch, torch.nn as nn
 from sklearn.datasets import load_iris
 from sklearn.preprocessing import StandardScaler
 from torch.utils.data import DataLoader, TensorDataset
@@ -29,7 +28,7 @@ def train_model(name: str, dim):
     optimizer = torch.optim.Adam(net.parameters(), lr=1e-2)
 
     print(f"Training {name} ({dim} neurons)...")
-    for epoch in range(200):
+    for epoch in range(100):
         global loss
         for data in trainloader:
             inputs, targets = data
@@ -38,26 +37,13 @@ def train_model(name: str, dim):
             loss = loss_fn(outputs, targets)
             loss.backward()
             optimizer.step()
-        if (epoch + 1) % 100 == 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", 10).eval()
-    torch_net_b = Iris_MLP(hidden_dim=20).eval()
-
-    with torch.no_grad():
-        torch_net_b.layers[0].weight[:10].copy_(torch_net_a.layers[0].weight) # pyright: ignore
-        torch_net_b.layers[0].bias[:10].copy_(torch_net_a.layers[0].bias) # pyright: ignore
-        torch_net_b.layers[0].weight[10:].copy_(torch_net_a.layers[0].weight) # pyright: ignore
-        torch_net_b.layers[0].bias[10:].copy_(torch_net_a.layers[0].bias) # pyright: ignore
-
-        half_weights = torch_net_a.layers[2].weight / 2.0 # pyright: ignore
-        
-        torch_net_b.layers[2].weight[:, :10].copy_(half_weights) # pyright: ignore
-        torch_net_b.layers[2].weight[:, 10:].copy_(half_weights) # pyright: ignore
-        
-        torch_net_b.layers[2].bias.copy_(torch_net_a.layers[2].bias) # pyright: ignore
+    torch_net_b = train_model("Network B", 20).eval()
 
     torch.onnx.export(torch_net_a, (torch.randn(1, 4),), "iris_a.onnx")
     torch.onnx.export(torch_net_b, (torch.randn(1, 4),), "iris_b.onnx")
diff --git a/examples/iris/iris_deep_to_wide.py b/examples/iris/iris_deep_to_wide.py
new file mode 100644
index 0000000..d94cce7
--- /dev/null
+++ b/examples/iris/iris_deep_to_wide.py
@@ -0,0 +1,87 @@
+import torch, torch.nn as nn
+from sklearn.datasets import load_iris
+from sklearn.preprocessing import StandardScaler
+from torch.utils.data import DataLoader, TensorDataset
+
+class Deep_Block(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.fc1 = nn.Linear(4, 1)
+        self.fc2 = nn.Linear(1, 3, bias=False)
+        
+    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_Iris_MLP(nn.Module):
+    def __init__(self, num_blocks):
+        super().__init__()
+        self.blocks = nn.Sequential(*[Deep_Block() for _ in range(num_blocks)])
+        self.final_bias = nn.Parameter(torch.zeros(3))
+
+    def forward(self, x):
+        s = torch.zeros(x.shape[0], 3, device=x.device)
+        _, final_s = self.blocks((x, s))
+        return final_s + self.final_bias
+
+class Wide_Iris_MLP(nn.Module):
+    def __init__(self, deep_net):
+        super().__init__()
+        num_neurons = len(deep_net.blocks)
+        self.layers = nn.Sequential(
+            nn.Linear(4, num_neurons),
+            nn.ReLU(),
+            nn.Linear(num_neurons, 3),
+        )
+        with torch.no_grad():
+            w1_all = []
+            b1_all = []
+            w2_all = []
+            
+            for block in deep_net.blocks:
+                w1_all.append(block.fc1.weight.data)
+                b1_all.append(block.fc1.bias.data)
+                w2_all.append(block.fc2.weight.data)
+                
+            self.layers[0].weight.copy_(torch.cat(w1_all, dim=0)) # pyright: ignore
+            self.layers[0].bias.copy_(torch.cat(b1_all, dim=0)) # pyright: ignore
+            self.layers[2].weight.copy_(torch.cat(w2_all, dim=1)) # pyright: ignore
+            self.layers[2].bias.copy_(deep_net.final_bias.data) # pyright: ignore
+
+    def forward(self, x):
+        return self.layers(x)
+
+iris = load_iris()
+scaler = StandardScaler()
+X = scaler.fit_transform(iris.data).astype('float32') # pyright: ignore
+y = iris.target.astype('int64') # pyright: ignore
+
+dataset = TensorDataset(torch.from_numpy(X), torch.from_numpy(y))
+trainloader = DataLoader(dataset, batch_size=16, shuffle=True)
+
+def train_deep_model(name: str, num_blocks):
+    net = Deep_Iris_MLP(num_blocks=num_blocks)
+    loss_fn = nn.CrossEntropyLoss()
+    optimizer = torch.optim.Adam(net.parameters(), lr=1e-2)
+
+    print(f"Training {name} ({num_blocks} blocks)...")
+    for epoch in range(100):
+        global loss
+        for inputs, targets in trainloader:
+            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_deep_model("Deep Network", 10).eval()
+    torch_net_b = Wide_Iris_MLP(torch_net_a).eval()
+    
+    torch.onnx.export(torch_net_a, (torch.randn(1, 4),), "iris_a.onnx")
+    torch.onnx.export(torch_net_b, (torch.randn(1, 4),), "iris_b.onnx")
diff --git a/examples/iris/iris_stably_active.py b/examples/iris/iris_stably_active.py
new file mode 100644
index 0000000..51b615c
--- /dev/null
+++ b/examples/iris/iris_stably_active.py
@@ -0,0 +1,71 @@
+import torch, torch.nn as nn
+from sklearn.datasets import load_iris
+from sklearn.preprocessing import StandardScaler
+from torch.utils.data import DataLoader, TensorDataset
+
+class Iris_MLP(nn.Module):
+    def __init__(self, hidden_dim):
+        super().__init__()
+        self.layers = nn.Sequential(
+            nn.Linear(4, hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, 3),
+        )
+    def forward(self, x):
+        return self.layers(x)
+
+class Iris_Linear(nn.Module):
+    def __init__(self, weight, bias):
+        super().__init__()
+        self.fc = nn.Linear(4, 3)
+        self.fc.weight.data = weight
+        self.fc.bias.data = bias
+    def forward(self, x):
+        return self.fc(x)
+
+iris = load_iris()
+scaler = StandardScaler()
+X = scaler.fit_transform(iris.data).astype('float32') # pyright: ignore
+y = iris.target.astype('int64') # pyright: ignore
+
+dataset = TensorDataset(torch.from_numpy(X), torch.from_numpy(y))
+trainloader = DataLoader(dataset, batch_size=16, shuffle=True)
+
+def train_model(name: str, dim):
+    net = Iris_MLP(hidden_dim=dim)
+    loss_fn = nn.CrossEntropyLoss()
+    optimizer = torch.optim.Adam(net.parameters(), lr=1e-2)
+
+    print(f"Training {name} ({dim} neurons)...")
+    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("Base Network", 10).eval()
+
+    with torch.no_grad():
+        torch_net_a.layers[0].weight.fill_(0.1) # pyright: ignore
+        torch_net_a.layers[0].bias.fill_(10.0) # pyright: ignore
+
+        W1 = torch_net_a.layers[0].weight.data
+        b1 = torch_net_a.layers[0].bias.data
+        W2 = torch_net_a.layers[2].weight.data
+        b2 = torch_net_a.layers[2].bias.data
+
+        W_collapsed = torch.matmul(W2, W1) # pyright: ignore
+        b_collapsed = torch.matmul(W2, b1) + b2 # pyright: ignore
+
+        torch_net_b = Iris_Linear(W_collapsed, b_collapsed).eval()
+
+    torch.onnx.export(torch_net_a, (torch.randn(1, 4),), "iris_a.onnx")
+    torch.onnx.export(torch_net_b, (torch.randn(1, 4),), "iris_b.onnx")
diff --git a/examples/iris/iris_stably_inactive.py b/examples/iris/iris_stably_inactive.py
new file mode 100644
index 0000000..684f967
--- /dev/null
+++ b/examples/iris/iris_stably_inactive.py
@@ -0,0 +1,57 @@
+import torch, torch.nn as nn
+from sklearn.datasets import load_iris
+from sklearn.preprocessing import StandardScaler
+from torch.utils.data import DataLoader, TensorDataset
+
+class Iris_MLP(nn.Module):
+    def __init__(self, hidden_dim):
+        super().__init__()
+        self.layers = nn.Sequential(
+            nn.Linear(4, hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, 3),
+        )
+    def forward(self, x):
+        return self.layers(x)
+
+iris = load_iris()
+scaler = StandardScaler()
+X = scaler.fit_transform(iris.data).astype('float32') # pyright: ignore
+y = iris.target.astype('int64') # pyright: ignore
+
+dataset = TensorDataset(torch.from_numpy(X), torch.from_numpy(y))
+trainloader = DataLoader(dataset, batch_size=16, shuffle=True)
+
+def train_model(name: str, dim):
+    net = Iris_MLP(hidden_dim=dim)
+    loss_fn = nn.CrossEntropyLoss()
+    optimizer = torch.optim.Adam(net.parameters(), lr=1e-2)
+
+    print(f"Training {name} ({dim} neurons)...")
+    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", 10).eval()
+    
+    with torch.no_grad():
+        torch_net_a.layers[0].weight[9] = -1.0 # pyright: ignore
+        torch_net_a.layers[0].bias[9] = -10.0 # pyright: ignore
+        
+        torch_net_b = Iris_MLP(10).eval()
+        torch_net_b.load_state_dict(torch_net_a.state_dict())
+        
+        torch_net_b.layers[2].weight[:, 9] = 0.0 # pyright: ignore
+        
+    torch.onnx.export(torch_net_a, (torch.randn(1, 4),), "iris_a.onnx")
+    torch.onnx.export(torch_net_b, (torch.randn(1, 4),), "iris_b.onnx")
diff --git a/examples/iris/iris_wide_to_deep.py b/examples/iris/iris_wide_to_deep.py
new file mode 100644
index 0000000..34a82ae
--- /dev/null
+++ b/examples/iris/iris_wide_to_deep.py
@@ -0,0 +1,83 @@
+import torch, torch.nn as nn
+from sklearn.datasets import load_iris
+from sklearn.preprocessing import StandardScaler
+from torch.utils.data import DataLoader, TensorDataset
+
+class Wide_Iris_MLP(nn.Module):
+    def __init__(self, hidden_dim):
+        super().__init__()
+        self.layers = nn.Sequential(
+            nn.Linear(4, hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, 3),
+        )
+    def forward(self, x):
+        return self.layers(x)
+
+class Deep_Block(nn.Module):
+    def __init__(self, w1, b1, w2):
+        super().__init__()
+        self.fc1 = nn.Linear(4, 1)
+        self.fc1.weight.data = w1.clone().unsqueeze(0)
+        self.fc1.bias.data = b1.clone().unsqueeze(0)
+        self.fc2 = nn.Linear(1, 3, 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_Iris_MLP(nn.Module):
+    def __init__(self, wide_net):
+        super().__init__()
+        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_Block(w1[j], b1[j], w2[:, j]) for j in range(num_neurons)
+        ])
+        self.final_bias = nn.Parameter(b2.clone())
+
+    def forward(self, x):
+        s = torch.zeros(x.shape[0], 3, device=x.device)
+        _, final_s = self.blocks((x, s))
+        return final_s + self.final_bias
+
+iris = load_iris()
+scaler = StandardScaler()
+X = scaler.fit_transform(iris.data).astype('float32') # pyright: ignore
+y = iris.target.astype('int64') # pyright: ignore
+
+dataset = TensorDataset(torch.from_numpy(X), torch.from_numpy(y))
+trainloader = DataLoader(dataset, batch_size=16, shuffle=True)
+
+def train_model(name:str, dim):
+    net = Wide_Iris_MLP(hidden_dim=dim)
+    loss_fn = nn.CrossEntropyLoss()
+    optimizer = torch.optim.Adam(net.parameters(), lr=1e-2)
+
+    print(f"Training {name} ({dim} neurons)...")
+    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("Wide Network", 10).eval()
+    torch_net_b = Deep_Iris_MLP(torch_net_a).eval()
+    
+    torch.onnx.export(torch_net_a, (torch.randn(1, 4),), "iris_a.onnx")
+    torch.onnx.export(torch_net_b, (torch.randn(1, 4),), "iris_b.onnx")
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__":
diff --git a/examples/mnist/mnist_deep_to_wide.py b/examples/mnist/mnist_deep_to_wide.py
new file mode 100644
index 0000000..6c4dde9
--- /dev/null
+++ b/examples/mnist/mnist_deep_to_wide.py
@@ -0,0 +1,85 @@
+import torch, torch.nn as nn
+from torchvision.datasets import MNIST
+from torch.utils.data import DataLoader
+from torchvision import transforms
+
+class Deep_MNIST_Block(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.fc1 = nn.Linear(784, 1)
+        self.fc2 = nn.Linear(1, 10, bias=False)
+        
+    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, num_blocks):
+        super().__init__()
+        self.flatten = nn.Flatten()
+        self.blocks = nn.Sequential(*[Deep_MNIST_Block() for _ in range(num_blocks)])
+        self.final_bias = nn.Parameter(torch.zeros(10))
+
+    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
+
+class Wide_MNIST_MLP(nn.Module):
+    def __init__(self, deep_net):
+        super().__init__()
+        self.flatten = nn.Flatten()
+        num_neurons = len(deep_net.blocks)
+        self.layers = nn.Sequential(
+            nn.Linear(784, num_neurons),
+            nn.ReLU(),
+            nn.Linear(num_neurons, 10),
+        )
+        with torch.no_grad():
+            w1_all = []
+            b1_all = []
+            w2_all = []
+            
+            for block in deep_net.blocks:
+                w1_all.append(block.fc1.weight.data)
+                b1_all.append(block.fc1.bias.data)
+                w2_all.append(block.fc2.weight.data)
+                
+            self.layers[0].weight.copy_(torch.cat(w1_all, dim=0)) # pyright: ignore
+            self.layers[0].bias.copy_(torch.cat(b1_all, dim=0)) # pyright: ignore
+            self.layers[2].weight.copy_(torch.cat(w2_all, dim=1)) # pyright: ignore
+            self.layers[2].bias.copy_(deep_net.final_bias.data) # pyright: ignore
+
+    def forward(self, x):
+        x = self.flatten(x)
+        return self.layers(x)
+
+train_dataset = MNIST('./', download=True, transform=transforms.ToTensor(), train=True)
+trainloader = DataLoader(train_dataset, batch_size=128, shuffle=True)
+
+def train_deep_model(name: str, num_blocks):
+    net = Deep_MNIST_MLP(num_blocks=num_blocks)
+    loss_fn = nn.CrossEntropyLoss()
+    optimizer = torch.optim.Adam(net.parameters(), lr=1e-3)
+
+    print(f"Training {name} ({num_blocks} blocks)...")
+    for epoch in range(10):
+        global loss
+        for inputs, targets in trainloader:
+            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_deep_model("Deep Network", 8).eval()
+    torch_net_b = Wide_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")
diff --git a/examples/mnist/mnist_stably_active.py b/examples/mnist/mnist_stably_active.py
new file mode 100644
index 0000000..267d682
--- /dev/null
+++ b/examples/mnist/mnist_stably_active.py
@@ -0,0 +1,69 @@
+import torch, torch.nn as nn
+from torchvision.datasets import MNIST
+from torch.utils.data import DataLoader
+from torchvision import transforms
+
+class MNIST_MLP(nn.Module):
+    def __init__(self, hidden_dim):
+        super().__init__()
+        self.flatten = nn.Flatten()
+        self.layers = nn.Sequential(
+            nn.Linear(784, hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, 10),
+        )
+    def forward(self, x):
+        x = self.flatten(x)
+        return self.layers(x)
+
+class MNIST_Linear(nn.Module):
+    def __init__(self, weight, bias):
+        super().__init__()
+        self.flatten = nn.Flatten()
+        self.fc = nn.Linear(784, 10)
+        self.fc.weight.data = weight
+        self.fc.bias.data = bias
+    def forward(self, x):
+        x = self.flatten(x)
+        return self.fc(x)
+
+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 = 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("Base Network", 6).eval()
+
+    with torch.no_grad():
+        torch_net_a.layers[0].weight.fill_(0.01) # pyright: ignore
+        torch_net_a.layers[0].bias.fill_(5.0) # pyright: ignore
+
+        W1 = torch_net_a.layers[0].weight.data
+        b1 = torch_net_a.layers[0].bias.data
+        W2 = torch_net_a.layers[2].weight.data
+        b2 = torch_net_a.layers[2].bias.data
+
+        W_collapsed = torch.matmul(W2, W1) # pyright: ignore
+        b_collapsed = torch.matmul(W2, b1) + b2 # pyright: ignore
+
+        torch_net_b = MNIST_Linear(W_collapsed, b_collapsed).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")
diff --git a/examples/mnist/mnist_stably_inactive.py b/examples/mnist/mnist_stably_inactive.py
new file mode 100644
index 0000000..ad81461
--- /dev/null
+++ b/examples/mnist/mnist_stably_inactive.py
@@ -0,0 +1,52 @@
+import torch, torch.nn as nn
+from torchvision.datasets import MNIST
+from torch.utils.data import DataLoader
+from torchvision import transforms
+
+class MNIST_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 = MNIST('./', download=True, transform=transforms.ToTensor(), train=True)
+trainloader = DataLoader(train_dataset, batch_size=128, shuffle=True)
+
+def train_model(name: str, dim):
+    net = 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("Base Network", 6).eval()
+    
+    with torch.no_grad():
+        torch_net_a.layers[1].weight[5] = -1.0 # pyright: ignore
+        torch_net_a.layers[1].bias[5] = -1.0 # pyright: ignore
+        
+        torch_net_b = MNIST_MLP(6).eval()
+        torch_net_b.load_state_dict(torch_net_a.state_dict())
+        
+        torch_net_b.layers[3].weight[:, 5] = 0.0 # pyright: ignore
+        
+    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")
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")
diff --git a/vein.py b/vein.py
index c017428..baa9da5 100644
--- a/vein.py
+++ b/vein.py
@@ -178,12 +178,10 @@ def inpla_export(model: onnx.ModelProto, bounds: Optional[Dict[str, List[float]]
             script.append(f"{v} ~ {sink};")
 
     def op_flatten(node):
-        out_name, in_name = node.output[0], node.input[0]
-        if out_name in interactions:
-            interactions[in_name] = interactions[out_name]
+        op_identity(node)
 
     def op_reshape(node):
-        op_flatten(node)
+        op_identity(node)
 
     def op_add(node):
         out_name = node.output[0]
@@ -242,6 +240,36 @@ def inpla_export(model: onnx.ModelProto, bounds: Optional[Dict[str, List[float]]
                 interactions[in_a][i].append(f"Add({sink}, Mul({v_b}, Concrete(-1.0)))")
                 interactions[in_b][i].append(f"{v_b}")
 
+    def op_slice(node):
+        in_name, out_name = node.input[0], node.output[0]
+        if out_name in interactions:
+            starts = initializers.get(node.input[1])
+            steps = initializers.get(node.input[4]) if len(node.input) > 4 else None
+            
+            start = int(starts.flatten()[0]) if starts is not None else 0
+            step = int(steps.flatten()[0]) if steps is not None else 1
+            
+            in_dim = get_dim(in_name) or 1
+            if in_name not in interactions:
+                interactions[in_name] = [[] for _ in range(in_dim)]
+
+            for i, terms in enumerate(interactions[out_name]):
+                input_index = start + (i * step)
+                if input_index < in_dim:
+                    interactions[in_name][input_index].extend(terms)
+
+    def op_squeeze(node):
+        op_identity(node)
+
+    def op_unsqueeze(node):
+        op_identity(node)
+
+    def op_identity(node):
+        in_name, out_name = node.input[0], node.output[0]
+        if out_name in interactions:
+            interactions[in_name] = interactions[out_name]
+
+
     graph, initializers = model.graph, get_initializers(model.graph)
     wire_gen = NameGen("w")
     interactions: Dict[str, List[List[str]]] = {}
@@ -253,7 +281,11 @@ def inpla_export(model: onnx.ModelProto, bounds: Optional[Dict[str, List[float]]
         "Reshape": op_reshape,
         "MatMul": op_matmul,
         "Add": op_add,
-        "Sub": op_sub
+        "Sub": op_sub,
+        "Slice": op_slice,
+        "Squeeze": op_squeeze,
+        "Unsqueeze": op_unsqueeze,
+        "Identity": op_identity
     }
 
     if graph.output:
@@ -284,7 +316,8 @@ def inpla_run(model: str) -> str:
         temp_path = f.name
     try:
         res = subprocess.run(["./inpla", "-f", temp_path, "-foptimise-tail-calls"], capture_output=True, text=True)
-        if res.stderr: print(res.stderr)
+        if res.stderr:
+            raise RuntimeError(res.stderr)
         return res.stdout
     finally:
         if os.path.exists(temp_path):