def MSRA(self, scale=2.0):
"""
Register as a variable with msra initializer.
"""
filler = pb.TensorFiller()
filler.tensor = self.name
filler.type = 'msra'
filler.scale = scale
ws.CreateFiller(filler)
return self
def Xavier(self, scale=3.0):
"""
Register as a variable with xavier initializer.
"""
filler = pb.TensorFiller()
filler.tensor = self.name
filler.type = 'xavier'
filler.scale = scale
ws.CreateFiller(filler)
return self
def Constant(self, value=0):
"""Register as a variable with constant initializer.
Parameters
----------
value : basic numerical type
The constant value.
"""
filler = pb.TensorFiller()
filler.tensor = self.name
filler.type = 'constant'
filler.value = value
ws.CreateFiller(filler)
return self
def Uniform(self, low=-1, high=1):
"""Register as a variable with uniform initializer.
Parameters
----------
low : basic numerical type
The lower bound of uniform distribution.
high : basic numerical type
The higher bound of uniform distribution.
"""
filler = pb.TensorFiller()
filler.tensor = self.name
filler.type = 'uniform'
filler.low = low
filler.high = high
ws.CreateFiller(filler)
return self
def Normal(self, mu=0, sigma=1):
"""Register as a variable with normal initializer.
Parameters
----------
mu : basic numerical type
The mu of normal distribution.
sigma : basic numerical type
The sigma of normal distribution.
"""
filler = pb.TensorFiller()
filler.tensor = self.name
filler.type = 'normal'
filler.mean = mu
filler.std = sigma
ws.CreateFiller(filler)
return self
def Fill(self, type, **kwargs):
"""Fill self with the specific type of filler.
Parameters
----------
type : str
The type of the filler.
Returns
-------
Tensor
Self, with filler registered implicitly in the backend.
"""
filler = pb.TensorFiller()
filler.tensor = self._name
filler.type = type.lower()
if filler.type == 'constant':
filler.value = kwargs['value'] if 'value' in kwargs else 0
elif filler.type == 'normal' or filler.type == 'gaussian':
filler.mean = kwargs['mean'] if 'mean' in kwargs else 0
filler.std = kwargs['std'] if 'std' in kwargs else 1
filler.type = 'normal'
elif filler.type == 'uniform':
filler.low = kwargs['low'] if 'low' in kwargs else 0
filler.high = kwargs['high'] if 'high' in kwargs else 1
filler.type = 'uniform'
elif filler.type == 'truncated_normal' or filler.type == 'truncatednormal':
filler.mean = kwargs['mean'] if 'mean' in kwargs else 0
filler.std = kwargs['std'] if 'std' in kwargs else 1
filler.low = filler.mean - 2.0 * filler.std
filler.high = filler.mean + 2.0 * filler.std
filler.type = 'truncated_normal'
elif filler.type == 'parameterized_truncated_normal':
filler.mean = kwargs['mean'] if 'mean' in kwargs else 0
filler.std = kwargs['std'] if 'std' in kwargs else 1
filler.low = kwargs['low'] if 'low' in kwargs else -2.0
filler.high = kwargs['high'] if 'high' in kwargs else 2.0
ws.CreateFiller(filler)
return self
def _no_parameter_filler(self, type):
filler = pb.TensorFiller()
filler.tensor = self.name
filler.type = type
ws.CreateFiller(filler)
return self
