conx

ConX Neural Networks

The On-Ramp to Deep Learning

Built in Python 3 on Keras 2.

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Read the documentation at conx.readthedocs.io <http://conx.readthedocs.io/>__

Ask questions on the mailing list: conx-users <https://groups.google.com/forum/#!forum/conx-users>__

Implements Deep Learning neural network algorithms using a simple interface with easy visualizations and useful analytics. Built on top of Keras, which can use either TensorFlow <https://www.tensorflow.org/>, Theano <http://www.deeplearning.net/software/theano/>, or CNTK <https://www.cntk.ai/pythondocs/>__.

A network can be specified to the constructor by providing sizes. For example, Network("XOR", 2, 5, 1) specifies a network named "XOR" with a 2-node input layer, 5-unit hidden layer, and a 1-unit output layer. However, any complex network can be constructed using the net.connect() method.

Computing XOR via a target function:

.. code:: python

import conx as cx

dataset = [[[0, 0], [0]],
           [[0, 1], [1]],
           [[1, 0], [1]],
           [[1, 1], [0]]]

net = cx.Network("XOR", 2, 5, 1, activation="sigmoid")
net.dataset.load(dataset)
net.compile(error='mean_squared_error',
            optimizer="sgd", lr=0.3, momentum=0.9)
net.train(2000, report_rate=10, accuracy=1.0)
net.test(show=True)

Creates dynamic, rendered visualizations like this:

Examples

See conx-notebooks <https://github.com/Calysto/conx-notebooks/blob/master/00_Index.ipynb>__ and the documentation <http://conx.readthedocs.io/en/latest/>__ for additional examples.

Installation

| See How To Run Conx <https://github.com/Calysto/conx-notebooks/tree/master/HowToRun#how-to-run-conx>__ | to see options on running virtual machines, in the cloud, and personal | installation.

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