深度学习激活函数图像绘制（基于Python编程语言实现）

def sigmoid(x): result = 1 / (1 + math.e ** (-x)) return result def tanh(x): # result = np.exp(x)-np.exp(-x)/np.exp(x)+np.exp(-x) result = (math.e ** (x) - math.e ** (-x)) / (math.e ** (x) + math.e ** (-x)) return result def relu(x): result = np.maximum(0, x) return result def elu(x, alpha=1): a = x[x > 0] b = alpha * (math.e ** (x[x < 0]) - 1) result = np.concatenate((b, a), axis=0) return result def leaky(x): a = x[x > 0] b = 0.1 * x[