created README.md

README.md

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+---
+license: apache-2.0
+library_name: pytorch
+---
+
+# or
+
+A neuron that performs the OR logical computation.
+
+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 two column vectors 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'.
+
+![](https://raw.githubusercontent.com/sambitmukherjee/handson-ml3-pytorch/main/chapter10/Figure_10-3.png)
+
+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 two column vectors containing `0`s and `1`s.
+batch = {'a': torch.tensor([[0], [0], [1], [1]]), 'b': torch.tensor([[0], [1], [0], [1]])}
+
+class OR(nn.Module, PyTorchModelHubMixin):
+    def __init__(self):
+        super().__init__()
+        self.operation = "C = A OR B"
+
+    def forward(self, x):
+        a = x['a']
+        b = x['b']
+        inputs = torch.cat([a, a, b, b], axis=1)
+        column_sum = torch.sum(inputs, dim=1, keepdim=True)
+        output = (column_sum >= 2).long()
+        return output
+
+# Instantiate:
+logical_or = OR.from_pretrained("sadhaklal/or")
+
+# Forward pass:
+output = logical_or(batch)
+print(output)
+```