Canela

Canela is a simple convolutional neural network library. It is, by design, completely unoptimized: The code is easy to understand but slow.

Setup and Installation

To setup Canela, first setup your environment

make

Then, to start hacking:

source env.sh

To run the tests and build the examples:

make all

Dependencies

You will need a C++11 compiler.

Canela relies on googletest and uses the build system from libarchlab. The Makefile will install both of these locally for you.

The utilities depend on and libpng and libjpeg which should be installed by default on most systems.

Finding Your Way Around

Here's where you'll find the parts of Cenala:

CNN -- The core Canela source code. Checkout CNN/README.md for details.
tests -- the Canela test suite
examples -- example code.
util -- Utility and helper functions (e.g., image loaders)
datasets -- sample data sets.

Basic Data Types

Canela relies heavily on several basic data types:

tensor_t : 4D array for storing inputs and outputs (defined in CNN/tensor_t.hpp).
model_t : A container for layer_t objects in a CNN model and high-level algorithms for training and classification (CNN/model_t.hpp)
layer_t : Base class for CNN layers. It defines a consistent interface for layers that model_t uses to to training and classification. (CNN/layer_t.hpp)
tdsize : The size of a tensor (x,y,z). It is a synonym for point_t. (defined in `CNN/types_t.hpp')
range_t : Represents a rectangular range of a tensor (defined in CNN/range_t.hpp).

Layer Types

Canela defines three main types of CNN layers: Fully-connected neural networks (CNN/fc_layer_t.hpp), convolutional layers (CNN/conv_layer_t.hpp), and pooling layers (CNN/pool_layer_t.hpp).

In addition it also has several types of "auxillary layers" that implement common features of CNNs: the relu layer (CNN/relu_layer_t.hpp) implement relu and neural net drop out is implemented as dropout_layer_t (CNN/drop_layer_t.hpp).

Credits

Canela is based on https://github.com/can1357/simple_cnn.

Tahara26 / CSE141pp-SimpleCNN