Source code for analogvnn.nn.activation.Tanh
from typing import Optional
import torch
from torch import Tensor, nn
from analogvnn.nn.activation.Activation import Activation
__all__ = ['Tanh']
[docs]class Tanh(Activation):
"""Implements the tanh activation function."""
@staticmethod
[docs] def forward(x: Tensor) -> Tensor:
"""Forward pass of the tanh activation function.
Args:
x (Tensor): the input tensor.
Returns:
Tensor: the output tensor.
"""
return torch.tanh(x)
[docs] def backward(self, grad_output: Optional[Tensor]) -> Optional[Tensor]:
"""Backward pass of the tanh activation function.
Args:
grad_output (Optional[Tensor]): the gradient of the output tensor.
Returns:
Optional[Tensor]: the gradient of the input tensor.
"""
x = self.inputs
grad = 1 - torch.pow(torch.tanh(x), 2)
return grad_output * grad
@staticmethod
[docs] def initialise(tensor: Tensor) -> Tensor:
"""Initialisation of tensor using xavier uniform, gain associated with tanh.
Args:
tensor (Tensor): the tensor to be initialized.
Returns:
Tensor: the initialized tensor.
"""
return nn.init.xavier_uniform(tensor, gain=nn.init.calculate_gain('tanh'))
@staticmethod
[docs] def initialise_(tensor: Tensor) -> Tensor:
"""In-place initialisation of tensor using xavier uniform, gain associated with tanh.
Args:
tensor (Tensor): the tensor to be initialized.
Returns:
Tensor: the initialized tensor.
"""
return nn.init.xavier_uniform_(tensor, gain=nn.init.calculate_gain('tanh'))