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,
concentration=None,
rate=None,
seed=None,
dtype=mstype.float32,
name="Gamma"):
"""
Constructor of Gamma.
"""
param = dict(locals())
param['param_dict'] = {'concentration': concentration, 'rate': rate}
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(concentration, (int, float)):
raise TypeError("Input concentration can't be scalar")
if isinstance(rate, (int, float)):
raise TypeError("Input rate can't be scalar")
super(Gamma, self).__init__(seed, dtype, name, param)
self._concentration = self._add_parameter(concentration,
'concentration')
self._rate = self._add_parameter(rate, 'rate')
if self._concentration is not None:
check_greater_zero(self._concentration, "concentration")
if self._rate is not None:
check_greater_zero(self._rate, "rate")
# ops needed for the class
self.log = log_generic
self.square = P.Square()
self.sqrt = P.Sqrt()
self.squeeze = P.Squeeze(0)
self.cast = P.Cast()
self.dtypeop = P.DType()
self.fill = P.Fill()
self.shape = P.Shape()
self.select = P.Select()
self.greater = P.Greater()
self.lgamma = nn.LGamma()
self.digamma = nn.DiGamma()
self.igamma = nn.IGamma()
'skip': ['backward']}),
('ReduceLogSumExp', {
'block': nn.ReduceLogSumExp((0,), False),
'desc_inputs': [Tensor(np.array([3, 4, 5, 6]).astype(np.float32))],
'skip': ['backward']}),
('LGamma', {
'block': nn.LGamma(),
'desc_inputs': [Tensor(np.array([3, 4, 5, 6]).astype(np.float32))],
'skip': ['backward']}),
('IGamma', {
'block': nn.IGamma(),
'desc_inputs': [Tensor(np.array([3, 4, 5, 6]).astype(np.float32)),
Tensor(np.array([3, 4, 5, 6]).astype(np.float32))],
'skip': ['backward']}),
('DiGamma', {
'block': nn.DiGamma(),
'desc_inputs': [Tensor(np.array([3, 4, 5, 6]).astype(np.float32))],
'skip': ['backward']}),
('LBeta', {
'block': nn.LBeta(),
'desc_inputs': [Tensor(np.array([3, 4, 5, 6]).astype(np.float32)),
Tensor(np.array([3, 4, 5, 6]).astype(np.float32))],
'skip': ['backward']}),
('FlattenNet', {
'block': FlattenNet(),
'desc_inputs': [Tensor(np.ones([1, 2, 3, 4], np.float32))],
}),
('PReLUNet', {
'block': PReLUNet(),
'desc_inputs': [Tensor(np.ones([1, 3, 4, 4], np.float32))],
}),
