[init] initial commit

diff --git a/.gitignore b/.gitignore
new file mode 100644 (file)
index 0000000..505a3b1
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,10 @@
+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv

diff --git a/.python-version b/.python-version
new file mode 100644 (file)
index 0000000..6324d40
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.14

diff --git a/README.md b/README.md
new file mode 100644 (file)
index 0000000..e69de29

diff --git a/main.py b/main.py
new file mode 100644 (file)
index 0000000..03908b8
--- /dev/null
+++ b/main.py
@@ -0,0 +1,75 @@
+from sklearn.datasets import load_wine
+from sklearn.model_selection import train_test_split
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+
+
+class Model(torch.nn.Module):
+    def __init__(self, n_features: int, n_classes: int) -> None:
+        super(Model, self).__init__()
+        self.fc1 = torch.nn.Linear(n_features, n_features * 4)
+        self.fc2 = torch.nn.Linear(n_features * 4, n_classes)
+        self.act = torch.nn.Tanh()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.fc1(x)
+        x = self.act(x)
+        return self.fc2(x)
+
+
+def get_grad_dist(model: torch.nn.Module) -> None:
+    grads = []
+    for param in model.parameters():
+        if param.grad is not None:
+            grads += param.grad.view(-1).tolist()
+    return grads
+
+
+def main():
+    data = load_wine()
+    X = torch.Tensor(data.data)
+    y = torch.LongTensor(data.target)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1337)
+    model = Model(len(X[0]), 3)
+    optimizer = torch.optim.SGD(model.parameters(), lr=1e-2)
+    criterion = torch.nn.CrossEntropyLoss()
+    grads = []
+    for epoch in range(0, 5):
+        model.train()
+        epoch_loss = 0.0
+        for X, y in zip(X_train, y_train):
+            optimizer.zero_grad()
+            y_hat = model(X)
+            loss = criterion(y_hat, y)
+            epoch_loss += loss.item()
+            loss.backward()
+            optimizer.step()
+            grads += get_grad_dist(model)
+
+        print(f"Loss: {epoch_loss / len(X_train)}")
+    
+    grads_abs = np.abs(np.array(grads))
+    grads_abs = grads_abs[grads_abs > 0]
+    print(f"Min magnitude: {min(grads_abs)}, Max: {max(grads_abs)}, Mean: {np.mean(grads_abs)}")
+    log_min = np.log10(grads_abs.min())
+    log_max = np.log10(grads_abs.max())
+    bins = np.logspace(log_min, log_max, 50)
+    counts, bin_edges = np.histogram(grads_abs, bins=bins)
+    #bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
+    bin_centers = np.sqrt(bin_edges[:-1] * bin_edges[1:])
+    bin_widths = np.diff(bin_edges)
+    probability_density = counts / (np.sum(counts) * bin_widths)
+    fig, ax = plt.subplots(figsize=(12, 6))
+    ax.loglog(bin_centers, probability_density, linewidth=3)
+    ax.set_xlabel('Gradient Magnitude', fontsize=12)
+    ax.set_ylabel('Probability density', fontsize=12)
+    ax.set_title('Distribution of Gradient Magnitudes', fontsize=14)
+    ax.grid(True, which='both', alpha=0.3)
+    plt.tight_layout()
+    integral = np.sum(probability_density * bin_widths)
+    print(f"Integral of PDF: {integral}")  # Should be close to 1
+    plt.show()
+
+if __name__ == "__main__":
+    main()

diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644 (file)
index 0000000..1967fa7
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,18 @@
+[project]
+name = "grad-att-dist"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.14"
+dependencies = [
+    "matplotlib>=3.10.7",
+    "numpy>=2.3.5",
+    "scikit-learn>=1.7.2",
+    "torch>=2.9.1",
+]
+
+[dependency-groups]
+dev = [
+    "pyrefly>=0.43.1",
+    "ruff>=0.14.7",
+]

diff --git a/uv.lock b/uv.lock
new file mode 100644 (file)
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+revision = 3
+requires-python = ">=3.14"
+
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