added README.md

README.md

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+---
+license: apache-2.0
+library_name: pytorch
+---
+
+# xor
+
+A multi-layer perceptron (MLP) that performs the XOR logical computation. It generates the following truth table:
+
+| A | B | C |
+| - | - | - |
+| 0 | 0 | 0 |
+| 0 | 1 | 1 |
+| 1 | 0 | 1 |
+| 1 | 1 | 0 |
+
+It takes as input two column vectors of zeros and ones. It outputs a single column vector of zeros and ones.
+
+Code: https://github.com/sambitmukherjee/handson-ml3-pytorch/blob/main/chapter10/xor.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 XOR(nn.Module, PyTorchModelHubMixin):
+    def __init__(self):
+        super().__init__()
+        self.layer0_weight = torch.tensor([[1., 1.], [1., 1.]])
+        self.layer0_bias = torch.tensor([-1.5, -0.5])
+        self.layer1_weight = torch.tensor([[-1.], [1.]])
+        self.layer1_bias = torch.tensor([-0.5])
+
+    def heaviside(self, x):
+        return (x >= 0).float()
+
+    def forward(self, x):
+        inputs = torch.cat([x['a'], x['b']], dim=1)
+        out = self.heaviside(inputs @ self.layer0_weight + self.layer0_bias)
+        out = self.heaviside(out @ self.layer1_weight + self.layer1_bias)
+        return out
+
+# Instantiate:
+logical_xor = XOR.from_pretrained("sadhaklal/xor")
+
+# Forward pass:
+output = logical_xor(batch)
+print(output)
+```