import torch
from .Activations._Activation import Activation
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class Identity(Activation):
"""
The identity layer.
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.name = "Identity"
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def forward(self, input, training=False, **kwargs):
"""
Returns the input.
Args:
input (torch.Tensor of shape (n_samples, n_features)): The input to the dense layer. Must be a torch.Tensor of the spesified shape given by layer.input_shape.
training (bool, optional): The boolean flag deciding if the model is in training mode. Defaults to False.
Returns:
torch.Tensor: The same tensor as the input
"""
if not isinstance(input, torch.Tensor):
raise TypeError("input must be a torch.Tensor.")
if input.shape[1:] != self.input_shape:
raise ValueError(f"input is not the same shape as the spesified input_shape ({input.shape[1:], self.input_shape}).")
if not isinstance(training, bool):
raise TypeError("training must be a boolean.")
return input
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def backward(self, dCdy, **kwargs):
"""
Returns the gradient.
Args:
dCdy (torch.Tensor): The gradient given by the next layer.
Returns:
torch.Tensor: The same tensor as the input gradient
"""
if not isinstance(dCdy, torch.Tensor):
raise TypeError("dCdy must be a torch.Tensor.")
if dCdy.shape[1:] != self.output_shape:
raise ValueError(f"dCdy is not the same shape as the spesified output_shape ({dCdy.shape[1:], self.output_shape}).")
return dCdy