def distribution(self, x, mean, std):
"""
Gaussian Distribution Function
"""
exponent = R.exp(-((x - mean)**2 / (2 * std**2)))
gaussian_func = exponent / (R.square_root(2 * (3.1415) * std))
def distribution(self, x, mean, std):
"""
Gaussian Distribution Function
"""
numerator = R.square(x - mean)
denominator = R.Scalar(2) * R.square(std)
frac = R.div(numerator,denominator)
exponent = R.exp(R.Scalar(-1) * frac)
two_pi = R.Scalar(2) * R.pi()
gaussian_denominator = R.square_root(two_pi) * std
gaussian_func = R.div(exponent, gaussian_denominator)
return gaussian_func
def distribution(self, x, mean, std):
"""
Gaussian Distribution Function
exponent = np.exp(-((x-mean)**2 / (2*std**2)))
gauss_func = exponent / (np.sqrt(2*np.pi)*std)
"""
numerator = R.square(x - mean)
denominator = R.Scalar(2) * R.square(std)
frac = R.div(numerator,denominator)
exponent = R.exp(R.Scalar(-1) * frac)
two_pi = R.Scalar(2) * R.Scalar(3.141592653589793)
gaussian_denominator = R.square_root(two_pi) * std
gaussian_func = R.div(exponent, gaussian_denominator)
return gaussian_func
def softmax(x):
"""
Softmax Activation Function
"""
exp = R.exp(x)
return R.div(exp, R.sum(exp))
def tanh(x):
"""
Tanh Activation Function
"""
return R.div(R.sub(R.exp(x), R.exp(R.mul(R.minus_one(), x))),
R.add(R.exp(x), R.exp(R.mul(R.minus_one(), x))))
def sigmoid(x):
"""
Sigmoid Activation Function
"""
return R.div(R.one(), R.add(R.one(), R.exp(R.multiply(R.minus_one(), x))))
def sigmoid(x):
# x is already a R.Scalar or a R.Tensor
return (R.Scalar(1).div(R.Scalar(1).add(R.exp(x.multiply(R.Scalar(-1))))))
def __sigmoid(self, z):
return (R.Scalar(1).div(
R.Scalar(1).add(R.exp(z.multiply(R.Scalar(-1))))))
