Skip to main content

A scikit-learn compatible neural network implementation

Project description


A scikit-learn compatible neural network implementation


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), Y)
pred = mlp.predict(X)

# show the result
import matplotlib.pyplot as plt  
plt.plot(X, pred)

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.

Files for ultimate, version 2.75.2
Filename, size File type Python version Upload date Hashes
Filename, size ultimate-2.75.2-py2.py3-none-any.whl (104.0 kB) File type Wheel Python version py2.py3 Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page