⚡️ Speed up function sigmoid_stable by 232%

Here is an optimized version of your function. The original code computes `np.exp(x)` twice for the `x < 0` case. To optimize, we can precompute the exponent, reducing redundant computation.



This computes `np.exp(-np.abs(x))` once and reuses it for both cases, improving speed and efficiency for large arrays.

Author: codeflash-ai[bot]

codeflash/process/infer.py

@@ -2,7 +2,8 @@
 
 
 def sigmoid_stable(x):
-    return np.where(x >= 0, 1 / (1 + np.exp(-x)), np.exp(x) / (1 + np.exp(x)))
+    exp_x = np.exp(-np.abs(x))
+    return np.where(x >= 0, 1 / (1 + exp_x), exp_x / (1 + exp_x))
 
 
 def postprocess(logits: np.array, max_detections: int = 8):