ultimate
A scikit-learn compatible neural network implementation
Installation
pip install ultimate
Why Ultimate?
- Support feature importance
- Support missing values
- Support am2x/a2m2x activation functions
- Support softmax/hardmax/mse/hardmse loss functions
- Support fc/add/conv/star operations
How To Use?
# let's use a simple example to learn how to use from ultimate.mlp import MLP import numpy as np # generate sample X = np.linspace(-np.pi, np.pi, num=5000).reshape(-1, 1) Y = np.sin(X) print(X.shape, Y.shape) # fit and predict mlp = MLP(layer_size=[X.shape[1], 8, 8, 8, 1], rate_init=0.02, loss_type="mse", epoch_train=100, epoch_decay=10, verbose=1) mlp.fit(X, Y) pred = mlp.predict(X) # show the result import matplotlib.pyplot as plt plt.plot(X, pred) plt.show()
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Release files for ultimate 2.75.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| ultimate-2.75.2-py2.py3-none-any.whl | Python 3, Python 2 | none | any | Details |
Release files / ultimate-2.75.2-py2.py3-none-any.whl
| Download URL | ultimate-2.75.2-py2.py3-none-any.whl |
|---|---|
| Size | 104.0 kB |
| Tags | Python 2 Python 3 |
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