conx 3.0.2

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. Built on top of Keras, which can use either TensorFlow or Theano.

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 or Tensorflow. Pick one:

pip install theano

or

pip install tensorflow

To use Theano as the Keras backend rather than TensorFlow, edit (or create) ~/.keras/kerson.json to:

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

Examples

See the examples and notebooks folders for additional examples.