On-Ramp to Deep Learning. Built on Keras
Project description
# ConX Neural Networks
## The On-Ramp to Deep Learning
Built in Python 3 on Keras 2.
[](https://mybinder.org/v2/gh/Calysto/conx/master?filepath=binder%2Findex.ipynb) [](https://circleci.com/gh/Calysto/conx/tree/master) [](https://codecov.io/gh/Calysto/conx) [](http://conx.readthedocs.io/en/latest/?badge=latest) [](https://badge.fury.io/py/conx)
Read the documentation at [conx.readthedocs.io](http://conx.readthedocs.io/)
Ask questions on the mailing list: [conx-users](https://groups.google.com/forum/#!forum/conx-users)
Implements Deep Learning neural network algorithms using a simple interface with easy visualizations and useful analytics. Built on top of Keras, which can use either [TensorFlow](https://www.tensorflow.org/), [Theano](http://www.deeplearning.net/software/theano/), or [CNTK](https://www.cntk.ai/pythondocs/).
A network can be 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. However, any complex network can be constructed using the `net.connect()` method.
Computing XOR via a target function:
```python
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.dataset.load(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:
<img src="https://raw.githubusercontent.com/Calysto/conx-notebooks/master/network.png" width="500"></img>
## Examples
See [conx-notebooks](https://github.com/Calysto/conx-notebooks/blob/master/00_Index.ipynb) and the [documentation](http://conx.readthedocs.io/en/latest/) for additional examples.
## Installation
See [How To Run Conx](https://github.com/Calysto/conx-notebooks/tree/master/HowToRun#how-to-run-conx)
to see options on running virtual machines, in the cloud, and personal
installation.
## The On-Ramp to Deep Learning
Built in Python 3 on Keras 2.
[](https://mybinder.org/v2/gh/Calysto/conx/master?filepath=binder%2Findex.ipynb) [](https://circleci.com/gh/Calysto/conx/tree/master) [](https://codecov.io/gh/Calysto/conx) [](http://conx.readthedocs.io/en/latest/?badge=latest) [](https://badge.fury.io/py/conx)
Read the documentation at [conx.readthedocs.io](http://conx.readthedocs.io/)
Ask questions on the mailing list: [conx-users](https://groups.google.com/forum/#!forum/conx-users)
Implements Deep Learning neural network algorithms using a simple interface with easy visualizations and useful analytics. Built on top of Keras, which can use either [TensorFlow](https://www.tensorflow.org/), [Theano](http://www.deeplearning.net/software/theano/), or [CNTK](https://www.cntk.ai/pythondocs/).
A network can be 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. However, any complex network can be constructed using the `net.connect()` method.
Computing XOR via a target function:
```python
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.dataset.load(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:
<img src="https://raw.githubusercontent.com/Calysto/conx-notebooks/master/network.png" width="500"></img>
## Examples
See [conx-notebooks](https://github.com/Calysto/conx-notebooks/blob/master/00_Index.ipynb) and the [documentation](http://conx.readthedocs.io/en/latest/) for additional examples.
## Installation
See [How To Run Conx](https://github.com/Calysto/conx-notebooks/tree/master/HowToRun#how-to-run-conx)
to see options on running virtual machines, in the cloud, and personal
installation.
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