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On-Ramp to Deep Learning. Built on Keras

Project description

The On-Ramp to Deep Learning

Built in Python 3 on Keras 2.

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Read the documentation at

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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:


See conx-notebooks and the documentation for additional examples.


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Filename, size & hash SHA256 hash help File type Python version Upload date
conx-3.7.5-py2.py3-none-any.whl (112.4 kB) Copy SHA256 hash SHA256 Wheel py2.py3 Oct 9, 2018
conx-3.7.5.tar.gz (104.4 kB) Copy SHA256 hash SHA256 Source None Oct 9, 2018

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