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--- |
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tags: |
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- model_hub_mixin |
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- pytorch_model_hub_mixin |
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pipeline_tag: tabular-regression |
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library_name: pytorch |
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datasets: |
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- gvlassis/california_housing |
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metrics: |
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- rmse |
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--- |
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# wide-and-deep-net-california-housing-v2 |
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A wide & deep neural network trained on the California Housing dataset. |
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It takes eight features: `'MedInc'`, `'HouseAge'`, `'AveRooms'`, `'AveBedrms'`, `'Population'`, `'AveOccup'`, `'Latitude'` and `'Longitude'`. It predicts `'MedHouseVal'`. |
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The first five features (`'MedInc'`, `'HouseAge'`, `'AveRooms'`, `'AveBedrms'` and `'Population'`) flow through the wide path. |
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The last six features (`'AveRooms'`, `'AveBedrms'`, `'Population'`, `'AveOccup'`, `'Latitude'` and `'Longitude'`) flow through the deep path. |
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Note: The features `'AveRooms'`, `'AveBedrms'` and `'Population'` flow through both the wide path and the deep path. |
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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'. |
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Code: https://github.com/sambitmukherjee/handson-ml3-pytorch/blob/main/chapter10/wide_and_deep_net_california_housing_v2.ipynb |
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Experiment tracking: https://wandb.ai/sadhaklal/wide-and-deep-net-california-housing |
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## Usage |
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``` |
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from sklearn.datasets import fetch_california_housing |
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housing = fetch_california_housing(as_frame=True) |
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from sklearn.model_selection import train_test_split |
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X_train_full, X_test, y_train_full, y_test = train_test_split(housing['data'], housing['target'], test_size=0.25, random_state=42) |
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X_train, X_valid, y_train, y_valid = train_test_split(X_train_full, y_train_full, test_size=0.25, random_state=42) |
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X_means, X_stds = X_train.mean(axis=0), X_train.std(axis=0) |
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X_train = (X_train - X_means) / X_stds |
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X_valid = (X_valid - X_means) / X_stds |
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X_test = (X_test - X_means) / X_stds |
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import torch |
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device = torch.device("cpu") |
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import torch.nn as nn |
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from huggingface_hub import PyTorchModelHubMixin |
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class WideAndDeepNet(nn.Module, PyTorchModelHubMixin): |
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def __init__(self): |
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super().__init__() |
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self.hidden1 = nn.Linear(6, 30) |
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self.hidden2 = nn.Linear(30, 30) |
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self.output = nn.Linear(35, 1) |
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def forward(self, input_wide, input_deep, label=None): |
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act = torch.relu(self.hidden1(input_deep)) |
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act = torch.relu(self.hidden2(act)) |
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concat = torch.cat([input_wide, act], axis=1) |
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return self.output(concat) |
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model = WideAndDeepNet.from_pretrained("sadhaklal/wide-and-deep-net-california-housing-v2") |
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model.to(device) |
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model.eval() |
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# Let's predict on 3 unseen examples from the test set: |
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print(f"Ground truth housing prices: {y_test.values[:3]}") |
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new = { |
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'input_wide': torch.tensor(X_test.values[:3, :5], dtype=torch.float32), |
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'input_deep': torch.tensor(X_test.values[:3, 2:], dtype=torch.float32) |
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} |
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new = {k: v.to(device) for k, v in new.items()} |
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with torch.no_grad(): |
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preds = model(**new) |
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print(f"Predicted housing prices: {preds.squeeze()}") |
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``` |
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## Metric |
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RMSE on the test set: 0.5606 |
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--- |
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This model has been pushed to the Hub using the [PyTorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration. |
