def reset_parameters(self):
if self.in_channels <= 0:
pass
elif self.weight_initializer == 'glorot':
inits.glorot(self.weight)
elif self.weight_initializer == 'uniform':
bound = 1.0 / math.sqrt(self.weight.size(-1))
torch.nn.init.uniform_(self.weight.data, -bound, bound)
elif self.weight_initializer == 'kaiming_uniform':
inits.kaiming_uniform(self.weight,
fan=self.in_channels,
a=math.sqrt(5))
elif self.weight_initializer is None:
inits.kaiming_uniform(self.weight,
fan=self.in_channels,
a=math.sqrt(5))
else:
raise RuntimeError(f"Linear layer weight initializer "
f"'{self.weight_initializer}' is not supported")
if self.bias is None or self.in_channels <= 0:
pass
elif self.bias_initializer == 'zeros':
inits.zeros(self.bias)
elif self.bias_initializer is None:
inits.uniform(self.in_channels, self.bias)
else:
raise RuntimeError(f"Linear layer bias initializer "
f"'{self.bias_initializer}' is not supported")
def reset_parameters(self):
if self.in_channels > 0:
if self.weight_initializer == 'glorot':
inits.glorot(self.weight)
elif (self.weight_initializer == 'kaiming_uniform'
or self.weight_initializer is None):
inits.kaiming_uniform(self.weight,
fan=self.in_channels,
a=math.sqrt(5))
else:
raise RuntimeError(
f"Linear layer weight initializer "
f"'{self.weight_initializer}' is not supported")
if self.in_channels > 0 and self.bias is not None:
if self.bias_initializer == 'zeros':
inits.zeros(self.bias)
elif self.bias_initializer is None:
inits.uniform(self.in_channels, self.bias)
else:
raise RuntimeError(
f"Linear layer bias initializer "
f"'{self.bias_initializer}' is not supported")
def reset_parameters(self):
inits.kaiming_uniform(self.weight, fan=self.in_channels,
a=math.sqrt(5))
inits.uniform(self.in_channels, self.bias)
