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Simplified in place guassian code

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Brandon Rozek 2019-02-03 00:31:35 -05:00
parent a03abe2bb1
commit 39643f04e1
1 changed files with 2 additions and 6 deletions
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8
rltorch/network/NoisyLinear.py
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@ -28,17 +28,13 @@ class NoisyLinear(nn.Linear):
def forward(self, x): def forward(self, x):
# Fill s_normal_weight with values from the standard normal distribution # Fill s_normal_weight with values from the standard normal distribution
torch.randn(self.s_normal_weight.size(), out = self.s_normal_weight, self.s_normal_weight.normal_()
dtype = self.s_normal_weight.dtype, layout = self.s_normal_weight.layout, device = self.s_normal_weight.device)
# Multiply by the standard deviation to correct the spread of Gaussian noise
weight_noise = self.sigma_weight * self.s_normal_weight.clone().requires_grad_() weight_noise = self.sigma_weight * self.s_normal_weight.clone().requires_grad_()
bias = None bias = None
if self.bias is not None: if self.bias is not None:
# Fill s_normal_bias with values from standard normal # Fill s_normal_bias with values from standard normal
torch.randn(self.s_normal_bias.size(), out = self.s_normal_bias, self.s_normal_bias.normal_()
dtype = self.s_normal_bias.dtype, layout = self.s_normal_bias.layout, device = self.s_normal_bias.device)
# Add guassian noise to original bias
bias = self.bias + self.sigma_bias * self.s_normal_bias.clone().requires_grad_() bias = self.bias + self.sigma_bias * self.s_normal_bias.clone().requires_grad_()
return F.linear(x, self.weight + weight_noise, bias) return F.linear(x, self.weight + weight_noise, bias)