import torch
from ._Activation import Activation
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class Tanh(Activation):
"""
The hyperbolic tangent activation function.
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.name = "Tanh"
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def forward(self, input, **kwargs):
"""
Calculates the hyperbolic tangent function for every element of the input matrix.
Args:
input (torch.Tensor of shape (batch_size, ...)): The input to the layer. Must be a torch.Tensor of any shape.
Returns:
torch.Tensor: The output tensor after applying the activation function of the same shape as the input.
"""
if not isinstance(input, torch.Tensor):
raise TypeError("input must be a torch.Tensor.")
self.input = input
output = torch.tanh(input)
return output
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def backward(self, dCdy, **kwargs):
"""
Calculates the gradient of the loss function with respect to the input of the layer.
Args:
dCdy (torch.Tensor of the same shape as returned from the forward method): The gradient given by the next layer.
Returns:
torch.Tensor of shape (batch_size, ...): The new gradient after backpropagation through the layer.
"""
if not isinstance(dCdy, torch.Tensor):
raise TypeError("dCdy must be a torch.Tensor.")
if dCdy.shape != self.input.shape:
raise ValueError(f"dCdy is not the same shape as the spesified output_shape ({dCdy.shape[1:], self.output_shape}).")
dCdx = dCdy * (1 - torch.tanh(self.input) ** 2)
return dCdx