conx 3.1.1

Deep Learning for Simple Folk. Built on Keras

Deep Learning for Simple Folk

Built in Python on Keras.

Read the documentation at conx.readthedocs.io

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.

Example

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")
net.set_dataset(dataset)
net.compile(loss='mean_squared_error',
            optimizer=SGD(lr=0.3, momentum=0.9))
net.train(2000, report_rate=10, accuracy=1)
net.test()

Install

pip install conx -U

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

pip install theano

or

pip install tensorflow

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

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"
}

Examples

See the notebooks folder and the documentation for additional examples.