Added sigmoid like activation functions

neural_network/activation_functions/sigmoid_like.py

@@ -0,0 +1,56 @@
+import numpy as np
+
+
+def sigmoid(vector: np.ndarray) -> np.ndarray:
+    """
+    The standard sigmoid function.
+    Args:
+        vector: (np.ndarray): The input array.
+    Returns:
+        np.ndarray: The result of the sigmoid activation applied to the input array.
+
+    >>> np.linalg.norm(np.array([0.5, 0.66666667, 0.83333333]) \
+        - sigmoid(vector=np.array([0, np.log(2), np.log(5)]))) < 10**(-5)
+    True
+    """
+    return 1 / (1 + np.exp(-1 * vector))
+
+
+def swish(vector: np.ndarray, beta: float) -> np.ndarray:
+    """
+    Swish activation: https://arxiv.org/abs/1710.05941v2
+    Args:
+        vector: (np.ndarray): The input array.
+        beta: (float)
+    Returns:
+        np.ndarray: The result of the swish activation applied to the input array.
+
+    >>> np.linalg.norm(np.array([0.5, 1., 1.5]) \
+            - swish(np.array([1, 2, 3]), 0)) < 10**(-5)
+    True
+    >>> np.linalg.norm(np.array([0, 0.66666667, 1.6]) \
+            - swish(np.array([0, 1, 2]), np.log(2))) < 10**(-5)
+    True
+    """
+    return vector / (1 + np.exp(-beta * vector))
+
+
+def sigmoid_linear_unit(vector: np.ndarray) -> np.ndarray:
+    """
+    SiLU activation: https://arxiv.org/abs/1606.08415
+    Args:
+        vector: (np.ndarray): The input array.
+    Returns:
+        np.ndarray: The result of the sigmoid linear unit applied to the input array.
+
+    >>> np.linalg.norm(np.array([0, 0.7310585, 0.462098]) \
+            - sigmoid_linear_unit(np.array([0, 1, np.log(2)]))) < 10**(-5)
+    True
+    """
+    return vector / (1 + np.exp(-1 * vector))
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()