Linear model with: • Callbacks • Metrics • Preprocessing functions • Simple integration
Quick Start
import fsh
X = [[1, 2], [3, 4]]
y = [[5], [6]]
#array converting is integrated! but you can do it yourself!
X = fsh.fsh.preprocessing.to_array(X)
y = fsh.fsh.preprocessing.to_array(y)
callback = fsh.fsh.callbacks.History()
model = fsh.fsh.Linear(n_features=2, lr=0.01)
# Training
model.train(X, y, epochs=100, callbacks=[callback], loss_fn=fsh.fsh.metrics.mse)
model.predict(X)
1 maintainer
Metadata
1 maintainer
Release files for gradforge 2.0.2
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| File | Size | Uploaded | Provenance | |
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| gradforge-2.0.2.tar.gz | 9.9 kB | Without provenance | Details |
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| File | Interpreter | ABI | Platform | Reset |
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| gradforge-2.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 26.4 kB
Release files / gradforge-2.0.2.tar.gz
| Download URL | gradforge-2.0.2.tar.gz |
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| Size | 9.9 kB |
| Tags | Source |
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Release files / gradforge-2.0.2-py3-none-any.whl
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| Tags | Python 3 |
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