torch.nn.functional.glu# torch.nn.functional.glu(input, dim=-1) → Tensor[source]# The gated linear unit. Computes: GLU(a,b)=a⊗σ(b)\text{GLU}(a, b) = a \otimes \sigma(b) GLU(a,b)=a⊗σ(b)where input is split in half along dim to form a and b, σ\sigmaσ is the sigmoid function and ⊗\otimes⊗ is the element-wise product between matrices. See Language Modeling with Gated Convolutional Networks. Parameters input (Tensor) – input tensor dim (int) – dimension on which to split the input. Default: -1 Return type Tensor
torch.nn.functional.glu# torch.nn.functional.glu(input, dim=-1) → Tensor[source]# The gated linear unit. Computes: GLU(a,b)=a⊗σ(b)\text{GLU}(a, b) = a \otimes \sigma(b) GLU(a,b)=a⊗σ(b)where input is split in half along dim to form a and b, σ\sigmaσ is the sigmoid function and ⊗\otimes⊗ is the element-wise product between matrices. See Language Modeling with Gated Convolutional Networks. Parameters input (Tensor) – input tensor dim (int) – dimension on which to split the input. Default: -1 Return type Tensor