ultimate
Project description
# ultimate Another neural network implemention for python
## Installation pip install ultimate
## Why Ultimate? + Support feature importance + Support missing values + Support am2x/a2m2x activation functions + Support hardmse/hardmax loss functions
## How To Use? <pre> # 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)
# train and predict mlp = MLP(layer_size=[X.shape[1], 8, 8, 8, 1], loss_type=”mse”) mlp.train(X, Y, epoch_train=100, epoch_decay=10, verbose=1) pred = mlp.predict(X)
# show the result import matplotlib.pyplot as plt plt.plot(X, pred) plt.show() </pre>
## Examples + [Feature Importance](https://www.kaggle.com/anycode/feature-importance-using-nn) + [Image Regression](https://www.kaggle.com/anycode/image-regression) + [Iris Classification](https://www.kaggle.com/anycode/iris-classification) + [MNIST Recognition](https://www.kaggle.com/anycode/mnist-recognition)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distribution
Hashes for ultimate-1.24.3-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 40a50deef266827974db753e971bfe104792ced33fe9d3e6df70646ff6696bc9 |
|
MD5 | 3a87f0170fe4af4c762177db26a4759b |
|
BLAKE2b-256 | 687fa38556abb20b7675325de53d26e4bcd66b22cf18ecef421b249d6111fa60 |