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activation.py
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activation.py
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import numpy as np
class ActivationFunction:
@staticmethod
def function(x: np.ndarray) -> np.ndarray:
raise NotImplementedError("Subclasses must implement the 'function' method.")
@staticmethod
def derivative(x: np.ndarray) -> np.ndarray:
raise NotImplementedError("Subclasses must implement the 'derivative' method.")
class Sigmoid(ActivationFunction):
@staticmethod
def function(x: np.ndarray) -> np.ndarray:
return 1 / (1 + np.exp(-x))
@staticmethod
def derivative(x: np.ndarray) -> np.ndarray:
return x * (1 - x)
class Tanh(ActivationFunction):
@staticmethod
def function(x: np.ndarray) -> np.ndarray:
return np.tanh(x)
@staticmethod
def derivative(x: np.ndarray) -> np.ndarray:
return 1 - x ** 2
class ReLU(ActivationFunction):
@staticmethod
def function(x: np.ndarray) -> np.ndarray:
return np.maximum(0, x)
@staticmethod
def derivative(x: np.ndarray) -> np.ndarray:
return np.where(x > 0, 1, 0)
class LeakyReLU(ActivationFunction):
@staticmethod
def function(x: np.ndarray) -> np.ndarray:
return np.maximum(0.01 * x, x)
@staticmethod
def derivative(x: np.ndarray) -> np.ndarray:
return np.where(x > 0, 1, 0.01)
class Softmax(ActivationFunction):
@staticmethod
def function(x: np.ndarray) -> np.ndarray:
return np.exp(x) / np.sum(np.exp(x), axis=1, keepdims=True)
@staticmethod
def derivative(x: np.ndarray) -> np.ndarray:
return x * (1 - x)
class Linear(ActivationFunction):
@staticmethod
def function(x: np.ndarray) -> np.ndarray:
return x
@staticmethod
def derivative(x: np.ndarray) -> np.ndarray:
return 1
class ELU(ActivationFunction):
@staticmethod
def function(x: np.ndarray) -> np.ndarray:
return np.where(x > 0, x, 0.01 * (np.exp(x) - 1))
@staticmethod
def derivative(x: np.ndarray) -> np.ndarray:
return np.where(x > 0, 1, 0.01 * np.exp(x))