def __init__(self):
super(DiGamma, self).__init__()
# const numbers
self.k_lanczos_gamma = 7
self.k_base_lanczos_coeff = 0.99999999999980993227684700473478
self.k_lanczos_coefficients = [676.520368121885098567009190444019,
-1259.13921672240287047156078755283,
771.3234287776530788486528258894,
-176.61502916214059906584551354,
12.507343278686904814458936853,
-0.13857109526572011689554707,
9.984369578019570859563e-6,
1.50563273514931155834e-7]
self.nan = np.nan
self.pi = np.pi
self.lanczos_gamma_plus_one_half = self.k_lanczos_gamma + 0.5
self.log_lanczos_gamma_plus_one_half = np.log(self.lanczos_gamma_plus_one_half)
# operations
self.log1p = P.Log1p()
self.abs = P.Abs()
self.shape = P.Shape()
self.dtype = P.DType()
self.fill = P.Fill()
self.floor = P.Floor()
self.equal = P.Equal()
self.less = P.Less()
self.select = P.Select()
self.sin = P.Sin()
self.cos = P.Cos()
self.logicaland = P.LogicalAnd()
def __init__(self,
concentration1=None,
concentration0=None,
seed=None,
dtype=mstype.float32,
name="Beta"):
"""
Constructor of Beta.
"""
param = dict(locals())
param['param_dict'] = {
'concentration1': concentration1,
'concentration0': concentration0
}
valid_dtype = mstype.float_type
Validator.check_type_name("dtype", dtype, valid_dtype,
type(self).__name__)
# As some operators can't accept scalar input, check the type here
if isinstance(concentration0, float):
raise TypeError("Input concentration0 can't be scalar")
if isinstance(concentration1, float):
raise TypeError("Input concentration1 can't be scalar")
super(Beta, self).__init__(seed, dtype, name, param)
self._concentration1 = self._add_parameter(concentration1,
'concentration1')
self._concentration0 = self._add_parameter(concentration0,
'concentration0')
if self._concentration1 is not None:
check_greater_zero(self._concentration1, "concentration1")
if self._concentration0 is not None:
check_greater_zero(self._concentration0, "concentration0")
# ops needed for the class
self.log = log_generic
self.log1p = P.Log1p()
self.neg = P.Neg()
self.pow = P.Pow()
self.squeeze = P.Squeeze(0)
self.cast = P.Cast()
self.fill = P.Fill()
self.shape = P.Shape()
self.select = P.Select()
self.logicaland = P.LogicalAnd()
self.greater = P.Greater()
self.digamma = nn.DiGamma()
self.lbeta = nn.LBeta()
def __init__(self, power=0., name='PowerTransform'):
param = dict(locals())
param['param_dict'] = {'power': power}
super(PowerTransform, self).__init__(name=name, param=param)
self._power = self._add_parameter(power, 'power')
check_greater_equal_zero(self._power, 'Power')
self.pow = P.Pow()
self.dtypeop = P.DType()
self.cast = P.Cast()
self.exp = exp_generic
self.expm1 = P.Expm1()
self.log = log_generic
self.log1p = P.Log1p()
def __init__(self,
loc=None,
scale=None,
seed=None,
dtype=mstype.float32,
name="Logistic"):
"""
Constructor of Logistic.
"""
param = dict(locals())
param['param_dict'] = {'loc': loc, 'scale': scale}
valid_dtype = mstype.float_type
Validator.check_type_name("dtype", dtype, valid_dtype,
type(self).__name__)
super(Logistic, self).__init__(seed, dtype, name, param)
self._loc = self._add_parameter(loc, 'loc')
self._scale = self._add_parameter(scale, 'scale')
if self._scale is not None:
check_greater_zero(self._scale, "scale")
# ops needed for the class
self.cast = P.Cast()
self.const = P.ScalarToArray()
self.consttensor = P.ScalarToTensor()
self.dtypeop = P.DType()
self.exp = exp_generic
self.expm1 = P.Expm1()
self.fill = P.Fill()
self.less = P.Less()
self.log = log_generic
self.log1p = P.Log1p()
self.logicalor = P.LogicalOr()
self.erf = P.Erf()
self.greater = P.Greater()
self.sigmoid = P.Sigmoid()
self.squeeze = P.Squeeze(0)
self.select = P.Select()
self.shape = P.Shape()
self.softplus = self._softplus
self.sqrt = P.Sqrt()
self.uniform = C.uniform
self.threshold = np.log(np.finfo(np.float32).eps) + 1.
self.tiny = np.finfo(np.float).tiny
self.sd_const = np.pi / np.sqrt(3)
def __init__(self):
super(LBeta, self).__init__()
# const numbers
self.log_2pi = np.log(2 * np.pi)
self.minimax_coeff = [-0.165322962780713e-02,
0.837308034031215e-03,
-0.595202931351870e-03,
0.793650666825390e-03,
-0.277777777760991e-02,
0.833333333333333e-01]
# operations
self.log = P.Log()
self.log1p = P.Log1p()
self.less = P.Less()
self.select = P.Select()
self.shape = P.Shape()
self.dtype = P.DType()
self.lgamma = LGamma()
def __init__(self):
super(Log1pNet, self).__init__()
self.log1p = P.Log1p()
def __init__(self):
super().__init__()
# self.softplus = P.Softplus()
self.log1p = P.Log1p()
self.exp = P.Exp()
def __init__(self, strategy1, strategy2):
super().__init__()
self.matmul = P.MatMul().set_strategy(strategy1)
self.log1p = P.Log1p().set_strategy(strategy2)
self.matmul2 = P.MatMul().set_strategy(strategy1)
def __init__(self):
super(NetLog1p, self).__init__()
self.log1p = P.Log1p()
