updated README.md

README.md

@@ -2,8 +2,113 @@
 tags:
 - model_hub_mixin
 - pytorch_model_hub_mixin
+pipeline_tag: tabular-regression
+library_name: pytorch
+datasets:
+- gvlassis/california_housing
+metrics:
+- rmse
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
+# wide-and-deep-net-california-housing-v3
+
+A wide & deep neural network trained on the California Housing dataset.
+
+It takes eight features: `'MedInc'`, `'HouseAge'`, `'AveRooms'`, `'AveBedrms'`, `'Population'`, `'AveOccup'`, `'Latitude'` and `'Longitude'`. It predicts `'MedHouseVal'`.
+
+The first five features (`'MedInc'`, `'HouseAge'`, `'AveRooms'`, `'AveBedrms'` and `'Population'`) flow through the wide path.
+
+The last six features (`'AveRooms'`, `'AveBedrms'`, `'Population'`, `'AveOccup'`, `'Latitude'` and `'Longitude'`) flow through the deep path.
+
+Note: The features `'AveRooms'`, `'AveBedrms'` and `'Population'` flow through both the wide path and the deep path.
+
+This model is a PyTorch adaptation of the TensorFlow model in Chapter 10 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_10-15.png)
+
+Code: https://github.com/sambitmukherjee/handson-ml3-pytorch/blob/main/chapter10/wide_and_deep_net_california_housing_v3.ipynb
+
+Experiment tracking: https://wandb.ai/sadhaklal/wide-and-deep-net-california-housing
+
+## Usage
+
+```
+from sklearn.datasets import fetch_california_housing
+
+housing = fetch_california_housing(as_frame=True)
+
+from sklearn.model_selection import train_test_split
+
+X_train_full, X_test, y_train_full, y_test = train_test_split(housing['data'], housing['target'], test_size=0.25, random_state=42)
+X_train, X_valid, y_train, y_valid = train_test_split(X_train_full, y_train_full, test_size=0.25, random_state=42)
+
+X_means, X_stds = X_train.mean(axis=0), X_train.std(axis=0)
+X_train = (X_train - X_means) / X_stds
+X_valid = (X_valid - X_means) / X_stds
+X_test = (X_test - X_means) / X_stds
+
+import torch
+
+device = torch.device("cpu")
+
+from dataclasses import dataclass
+from typing import Optional
+
+@dataclass
+class WideAndDeepNetOutput:
+    main_output: torch.Tensor
+    aux_output: torch.Tensor
+    main_loss: Optional[torch.Tensor] = None
+    aux_loss: Optional[torch.Tensor] = None
+    loss: Optional[torch.Tensor] = None
+
+import torch.nn as nn
+from huggingface_hub import PyTorchModelHubMixin
+
+class WideAndDeepNet(nn.Module, PyTorchModelHubMixin):
+    def __init__(self):
+        super().__init__()
+        self.hidden1 = nn.Linear(6, 30)
+        self.hidden2 = nn.Linear(30, 30)
+        self.main_head = nn.Linear(35, 1)
+        self.aux_head = nn.Linear(30, 1)
+        self.main_loss_fn = nn.MSELoss(reduction='sum')
+        self.aux_loss_fn = nn.MSELoss(reduction='sum')
+
+    def forward(self, input_wide, input_deep, label=None):
+        act = torch.relu(self.hidden1(input_deep))
+        act = torch.relu(self.hidden2(act))
+        concat = torch.cat([input_wide, act], dim=1)
+        main_output = self.main_head(concat)
+        aux_output = self.aux_head(act)
+        if label is not None:
+            main_loss = self.main_loss_fn(main_output.squeeze(), label)
+            aux_loss = self.aux_loss_fn(aux_output.squeeze(), label)
+            loss = 0.9 * main_loss + 0.1 * aux_loss
+            return WideAndDeepNetOutput(main_output, aux_output, main_loss, aux_loss, loss)
+        else:
+            return WideAndDeepNetOutput(main_output, aux_output)
+
+model = WideAndDeepNet.from_pretrained("sadhaklal/wide-and-deep-net-california-housing-v3")
+model.to(device)
+model.eval()
+
+# Let's predict on 3 unseen examples from the test set:
+print(f"Ground truth housing prices: {y_test.values[:3]}")
+new = {
+    'input_wide': torch.tensor(X_test.values[:3, :5], dtype=torch.float32), 
+    'input_deep': torch.tensor(X_test.values[:3, 2:], dtype=torch.float32)
+}
+new = {k: v.to(device) for k, v in new.items()}
+with torch.no_grad():
+    output = model(**new)
+print(f"Predicted housing prices: {output.main_output.squeeze()}")
+```
+
+## Metric
+
+RMSE on the test set: 0.574
+
+---
+
+This model has been pushed to the Hub using the [PyTorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration.