def reset_parameters(self):
gain = cal_gain(self.activation)
nn.init.xavier_normal_(self.linear.weight, gain=gain)
if isinstance(self.res_fc, nn.Linear):
nn.init.xavier_normal_(self.res_fc.weight, gain=gain)
if self.res_fc.bias is not None:
nn.init.zeros_(self.res_fc.bias)
def reset_parameters(self):
gain = cal_gain(self.activation)
nn.init.xavier_uniform_(self.linear1.weight, gain=gain)
if self.linear1.bias is not None:
nn.init.zeros_(self.linear1.bias)
nn.init.xavier_uniform_(self.linear2.weight, gain=gain)
if self.linear2.bias is not None:
nn.init.zeros_(self.linear2.bias)
def reset_parameters(self):
gain = cal_gain(self.activation)
nn.init.xavier_uniform_(self.fcs[0].weight, gain=gain)
if self.fcs[0].bias is not None:
nn.init.zeros_(self.fcs[0].bias)
nn.init.xavier_uniform_(self.fcs[-1].weight)
if self.fcs[-1].bias is not None:
nn.init.zeros_(self.fcs[-1].bias)
def reset_parameters(self):
gain = cal_gain("leaky_relu")
nn.init.xavier_normal_(self.fc.weight, gain=gain)
nn.init.xavier_normal_(self.attn_fc.weight, gain=gain)
if isinstance(self.res_fc, nn.Linear):
nn.init.xavier_normal_(self.res_fc.weight, gain=cal_gain(self.activation))
def reset_parameters(self):
gain = cal_gain(self.activation)
if self.project:
nn.init.xavier_normal_(self.linear.weight, gain=gain)
if isinstance(self.res_fc, nn.Linear):
nn.init.xavier_normal_(self.res_fc.weight, gain=gain)
def reset_parameters(self):
gain = cal_gain(F.sigmoid)
nn.init.xavier_uniform_(self.s, gain=gain)