Conny
Conny is a neural network library. Instead of organizing networks into layers, it allows for arbitrary connections including recurrent ones. This makes it a good tool to experiment with new topologies.
Data Layout
All neurons are stores as a one-dimensional vector. This works since in backpropagation through time, gradients are only based on the previous activations. This allows for arbitrary connections between neurons. The network and its state are still stored in a compact way allowing for efficient algebra routines on the CPU and GPU.
| Variable | Type | Dimensions | Storage |
|---|---|---|---|
| Activation Function | int8 | N | Dense |
| Current activation | float32 | N | Dense |
| Previous activation | float32 | N | Dense |
| Weights | float32 | N x N | Sparse |
| Gradient | float32 | N x N | Sparse |
N refers to the total number of neurons.
The activation function is stored as an enumeration value.
Instructions
virtualenv .
source bin/activate
pip install -U pip
pip install -r requirements.txt
py.test test
