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On-Ramp to Deep Learning. Built on Keras

Project description

# ConX Neural Networks

## The On-Ramp to Deep Learning

Built in Python 3 on Keras 2.

[![Binder](]( [![CircleCI](]( [![codecov](]( [![Documentation Status](]( [![PyPI version](](

Read the documentation at [](

Ask questions on the mailing list: [conx-users](!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](, [Theano](, or [CNTK](

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:

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")
optimizer="sgd", lr=0.3, momentum=0.9)
net.train(2000, report_rate=10, accuracy=1.0)

Creates dynamic, rendered visualizations like this:

<img src="" width="500"></img>

## Examples

See [conx-notebooks]( and the [documentation]( for additional examples.

## Installation

See [How To Run Conx](
to see options on running virtual machines, in the cloud, and personal

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