db09dc1fb503ab8f7de69fa23e8d38742bda8e90,ch07/lib/dqn_model.py,NoisyLinear,forward,#NoisyLinear#Any#,25

Before Change

        nn.init.uniform(self.bias, -std, std)

    def forward(self, input):
        torch.randn(self.epsilon_weight.size(), out=self.epsilon_weight)
        bias = self.bias
        if bias is not None:
            torch.randn(self.epsilon_bias.size(), out=self.epsilon_bias)
            bias = bias + self.sigma_bias * Variable(self.epsilon_bias)
        return F.linear(input, self.weight + self.sigma_weight * Variable(self.epsilon_weight), bias)

After Change

        self.epsilon_weight.normal_()
        bias = self.bias
        if bias is not None:
            self.epsilon_bias.normal_()
            bias = bias + self.sigma_bias * self.epsilon_bias
        return F.linear(input, self.weight + self.sigma_weight * self.epsilon_weight, bias)

Instances