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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 conx.readthedocs.io

Ask questions on the mailing list: conx-users

Implements Deep Learning neural network algorithms using a simple interface with easy visualizations and useful analytical. Built on top of Keras, which can use either TensorFlow, Theano, or CNTK.

The network is 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.

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")
net.set_dataset(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 and the documentation for additional examples.

Installation

to see options on running virtual machines, in the cloud, and personal
installation.

Project details


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Filename, size & hash SHA256 hash help File type Python version Upload date
conx-3.6.9-py2.py3-none-any.whl (95.3 kB) Copy SHA256 hash SHA256 Wheel py2.py3 May 4, 2018
conx-3.6.9.tar.gz (89.8 kB) Copy SHA256 hash SHA256 Source None May 4, 2018

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