Deep Learning for Simple Folk

Built in Python 3 on Keras 2.

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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:

from conx import Network, SGD

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

net = Network("XOR", 2, 5, 1, activation="sigmoid")
            optimizer=SGD(lr=0.3, momentum=0.9))
net.train(2000, report_rate=10, accuracy=1)

Creates dynamic, rendered visualizations like this:


conx requires Python3, and some other Python modules that are installed automatically with pip.

Note: you may need to use pip3, or admin privileges (eg, sudo), or a user environment.

pip install conx -U

You will need to decide whether to use Theano, TensorFlow, or CNTK. Pick one. See for installing CNTK on Windows or Linux. All platforms can also install either of the others using pip:

pip install theano


pip install tensorflow

Use with Jupyter Notebooks

To use the Network.dashboard() and camera functions, you will need to install and enable ipywidgets:

With pip:

pip install ipywidgets
jupyter nbextension enable --py widgetsnbextension

With conda

conda install -c conda-forge ipywidgets

Installing ipywidgets with conda will also enable the extension for you.

Changing Keras Backends

To use a Keras backend other than TensorFlow, edit (or create) ~/.keras/kerson.json, like:

    "backend": "theano",
    "image_data_format": "channels_last",
    "epsilon": 1e-07,
    "floatx": "float32"


See the notebooks folder and the documentation for additional examples.