Spaces:
Sleeping
Sleeping
File size: 1,339 Bytes
8c348c5 6307b4f 8c348c5 6307b4f 8c348c5 6307b4f 8c348c5 6307b4f 8c348c5 6307b4f 8c348c5 6307b4f 8c348c5 6307b4f 8c348c5 6307b4f 8c348c5 6307b4f 8c348c5 6307b4f 6d0453c 6307b4f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 |
import numpy as np
from abc import abstractmethod, ABC
class Activation(ABC):
@abstractmethod
def forward(self, X: np.ndarray) -> np.ndarray:
pass
@abstractmethod
def backward(self, X: np.ndarray) -> np.ndarray:
pass
class Relu(Activation):
def forward(self, X: np.ndarray) -> np.ndarray:
return np.maximum(0, X)
def backward(self, X: np.ndarray) -> np.ndarray:
return np.where(X > 0, 1, 0)
class TanH(Activation):
def forward(self, X: np.ndarray) -> np.ndarray:
return np.tanh(X)
def backward(self, X: np.ndarray) -> np.ndarray:
return 1 - self.forward(X) ** 2
class Sigmoid(Activation):
def forward(self, X: np.ndarray) -> np.ndarray:
return 1.0 / (1.0 + np.exp(-X))
def backward(self, X: np.ndarray) -> np.ndarray:
s = self.forward(X)
return s - (1 - s)
class SoftMax(Activation):
def forward(self, X: np.ndarray) -> np.ndarray:
exps = np.exp(
X - np.max(X, axis=1, keepdims=True)
) # Avoid numerical instability
return exps / np.sum(exps, axis=1, keepdims=True)
def backward(self, X: np.ndarray) -> np.ndarray:
return X
ACTIVATIONS: dict[str, Activation] = {
"Relu": Relu(),
"Sigmoid": Sigmoid(),
"TanH": TanH(),
"SoftMax": SoftMax(),
}
|