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---
license: apache-2.0
library_name: pytorch
---
# identity
A neuron that performs the IDENTITY logical computation. It generates the following truth table:
| A | C |
| - | - |
| 0 | 0 |
| 1 | 1 |
It is inspired by McCulloch & Pitts' 1943 paper 'A Logical Calculus of the Ideas Immanent in Nervous Activity'.
It doesn't contain any parameters.
It takes as input a single column vector of zeros and ones. It outputs a single column vector of zeros and ones.
Its mechanism is outlined in Figure 10-3 of Aurelien Geron's book 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'.
Like all the other neurons in Figure 10-3, it is activated when at least two of its input connections are active.
Code: https://github.com/sambitmukherjee/handson-ml3-pytorch/blob/main/chapter10/logical_computations_with_neurons.ipynb
## Usage
```
import torch
import torch.nn as nn
from huggingface_hub import PyTorchModelHubMixin
# Let's create a column vector containing two values - `0` and `1`:
batch = torch.tensor([[0], [1]])
class IDENTITY(nn.Module, PyTorchModelHubMixin):
def __init__(self):
super().__init__()
self.operation = "C = A"
def forward(self, x):
a = x
inputs = torch.cat([a, a], dim=1)
column_sum = torch.sum(inputs, dim=1, keepdim=True)
output = (column_sum >= 2).long()
return output
# Instantiate:
identity = IDENTITY.from_pretrained("sadhaklal/identity")
# Forward pass:
output = identity(batch)
print(output)
``` |