Metadata-Version: 1.1
Name: conx
Version: 3.0.1
Summary: Neural network library on Keras
Home-page: https://github.com/Calysto/conx
Author: Douglas S. Blank
Author-email: doug.blank@gmail.com
License: UNKNOWN
Description: # conx
        
        Neural Network Library for Cognitive Scientists
        
        Built in Python on Keras
        
        [![CircleCI](https://circleci.com/gh/Calysto/conx/tree/master.svg?style=svg)](https://circleci.com/gh/Calysto/conx/tree/master) [![codecov](https://codecov.io/gh/Calysto/conx/branch/master/graph/badge.svg)](https://codecov.io/gh/Calysto/conx)
        
        Networks implement neural network algorithms. Networks can have as many hidden layers as you desire.
        
        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:
        
        ```python
        from conx import Network, SGD
        
        dataset = [[[0, 0], [0]],
                  [[0, 1], [1]],
                  [[1, 0], [1]],
                  [[1, 1], [0]]]
        
        net = Network("XOR", 2, 2, 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
        
        ```shell
        pip install conx -U
        ```
        
        You will need to decide whether to use Theano or Tensorflow. Pick one:
        
        ```shell
        pip install theano
        ```
        
        or
        
        ```shell
        pip install tensorflow
        ```
        
        To use Theano as the Keras backend rather than TensorFlow, edit (or create) `~/.keras/kerson.json` to:
        
        ```json
        {
            "backend": "theano",
            "image_data_format": "channels_last",
            "epsilon": 1e-07,
            "floatx": "float32"
        }
        ```
        
        ## Examples
        
        See the examples and notebooks folders for additional examples.
        
Platform: UNKNOWN
Classifier: Framework :: IPython
Classifier: License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)
Classifier: Programming Language :: Python :: 3
