Assert fan_in > 0 in Kaiming/Xavier init and handle degenerate rng range

src/nn/linear.rs

@@ -94,6 +94,7 @@ impl Module for Linear {
 
 /// Uniform over `±1/√fan_in`, the bias init shared with the conv layers.
 pub(super) fn uniform_bias(out_features: usize, fan_in: usize) -> Tensor {
+    assert!(fan_in > 0, "uniform_bias: fan_in must be positive, got 0");
     let bound = 1.0 / (fan_in as f32).sqrt();
     Tensor::uniform(&[out_features], -bound, bound)
 }

src/rng.rs

@@ -22,6 +22,9 @@ pub fn with_rng<T>(f: impl FnOnce(&mut StdRng) -> T) -> T {
 
 /// `n` samples from the uniform distribution on `[lo, hi)`.
 pub fn uniform(n: usize, lo: f32, hi: f32) -> Vec<f32> {
+    if lo == hi {
+        return vec![lo; n];
+    }
     with_rng(|rng| (0..n).map(|_| rng.random_range(lo..hi)).collect())
 }
 

src/tensor/init.rs

@@ -43,12 +43,20 @@ impl Tensor {
 
     /// Kaiming (He) uniform, bounds `±√(6 / fan_in)`. For ReLU-family layers.
     pub fn kaiming_uniform(shape: &[usize], fan_in: usize) -> Tensor {
+        assert!(
+            fan_in > 0,
+            "kaiming_uniform: fan_in must be positive, got 0 for shape {shape:?}"
+        );
         let bound = (6.0 / fan_in as f32).sqrt();
         Tensor::uniform(shape, -bound, bound)
     }
 
     /// Xavier (Glorot) uniform, for `tanh` and `sigmoid` layers.
     pub fn xavier_uniform(shape: &[usize], fan_in: usize, fan_out: usize) -> Tensor {
+        assert!(
+            fan_in + fan_out > 0,
+            "xavier_uniform: fan_in + fan_out must be positive, got 0 for shape {shape:?}"
+        );
         let bound = (6.0 / (fan_in + fan_out) as f32).sqrt();
         Tensor::uniform(shape, -bound, bound)
     }

tests/linear.rs

@@ -63,3 +63,27 @@ fn no_bias_preserves_forward_shape_and_trainable_weight() {
     output.sum().backward();
     assert_eq!(layer.weight.grad().unwrap().shape(), &[3, 2]);
 }
+
+#[test]
+#[should_panic(expected = "fan_in must be positive")]
+fn linear_zero_in_features_panics_with_clear_message() {
+    let _ = Linear::new(0, 10);
+}
+
+#[test]
+#[should_panic(expected = "fan_in must be positive")]
+fn linear_no_bias_zero_in_features_panics() {
+    let _ = Linear::no_bias(0, 10);
+}
+
+#[test]
+#[should_panic(expected = "fan_in + fan_out must be positive")]
+fn xavier_zero_fan_panics() {
+    let _ = Tensor::xavier_uniform(&[3, 3], 0, 0);
+}
+
+#[test]
+fn rng_uniform_degenerate_range_returns_constant() {
+    let vals = fastnn::rng::uniform(5, 3.0, 3.0);
+    assert_eq!(vals, vec![3.0; 5]);
+}