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144 def sigmoid(self, z): 145 146 z = z * -1 147 z = np.exp(z) 148 x = 1.0 / (1.0 + z) 149 150 return x
63 def _sigmoid(self, z): 64 """Compute logistic function (sigmoid)""" 65 return 1. / (1. + np.exp(-np.clip(z, -250, 250)))
22 def __sigmoid_derivative(self, x): 23 return x*(1-x)
939 def sigmoid(*args): 940 return _cntk_py.sigmoid(*args)
29 def sigmoid(x): 30 """Sigmoid function""" 31 return 1. / (1. + np.exp(-x))
12 def sigmoid(x): 13 return 1.0 / (1.0 + np.exp(-x))
34 def sigmoid(x): 35 return 1.0 / (1.0 + np.exp(-x)) # sigmoid "squashing" function to interval [0,1]
92 def sigmoid(x): 93 """ 94 Sigmoid function of x, aka 1/(1+exp(-x)). 95 96 Parameters 97 ---------- 98 x: a real number 99 100 Returns 101 ------- 102 res: a real number 103 The result of sigmoid function 104 """ 105 return 1 / (1 + numpy.exp(-x))
6 def sigmoid(x): 7 return float(1) / (1 + math.exp(-x))
32 def d_sigmoid(x): 33 return sigmoid(x)*(1 - sigmoid(x))