cnn-v1

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Legacy version of CNN neural net toolkit (now called dynet)

// repository documentation

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# cnn-v1
Legacy version of CNN neural net toolkit  (now called [dynet](https://github.com/clab/dynet))

# cnn
C++ neural network library

#### Important: Eigen version requirement

You need the [development version of the Eigen library](https://bitbucket.org/eigen/eigen) for this software to function. **If you use any of the released versions, you may get assertion failures or compile errors.**

#### Building

First you need to fetch the dependent libraries

git submodule init
    git submodule update

In `src`, you need to first use [`cmake`](http://www.cmake.org/) to generate the makefiles

mkdir build
    cd build
    cmake .. -DEIGEN3_INCLUDE_DIR=/path/to/eigen

Then to compile, run

make -j 2

To see that things have built properly, you can run

./examples/xor

which will train a multilayer perceptron to predict the xor function.

#### Building without Eigen installed

If you don't have Eigen installed, the instructions below will fetch and compile
both `Eigen` and `cnn`. Eigen does not have to be compiled, so “installing” it is easy.
        
    git clone https://github.com/clab/cnn.git
    hg clone https://bitbucket.org/eigen/eigen/

cd cnn/
    mkdir build
    cd build
    cmake .. -DEIGEN3_INCLUDE_DIR=../eigen
    make -j 2

#### Debugging build problems

If you want to see the compile commands that are used, you can run

make VERBOSE=1

#### Training Models

An illustation of how models are trained (for a simple logistic regression model) is below:

```c++
// *** First, we set up the structure of the model
// Create a model, and an SGD trainer to update its parameters.
Model mod;
SimpleSGDTrainer sgd(&mod);
// Create a "computation graph," which will define the flow of information.
ComputationGraph cg;
// Initialize a 1x3 parameter vector, and add the parameters to be part of the
// computation graph.
Expression W = parameter(cg, mod.add_parameters({1, 3}));
// Create variables defining the input and output of the regression, and load them
// into the computation graph. Note that we don't need to set concrete values yet.
vector<cnn::real> x_values(3);
Expression x = input(cg, {3}, &x_values);
cnn::real y_value;
Expression y = input(cg, &y_value);
// Next, set up the structure to multiply the input by the weight vector,  then run
// the output of this through a logistic sigmoid function (logistic regression).
Expression y_pred = logistic(W*x);
// Finally, we create a function to calculate the loss. The model will be optimized
// to minimize the value of the final function in the computation graph.
Expression l = binary_log_loss(y_pred, y);
// We are now done setting up the graph, and we can print out its structure:
cg.PrintGraphviz();

// *** Now, we perform a parameter update for a single example.
// Set the input/output to the values specified by the training data:
x_values = {0.5, 0.3, 0.7};
y_value = 1.0;
// "forward" propagates values forward through the computation graph, and returns
// the loss.
cnn::real loss = as_scalar(cg.forward());
// "backward" performs back-propagation, and accumulates the gradients of the
// parameters within the "Model" data structure.
cg.backward();
// "sgd.update" updates parameters of the model that was passed to its constructor.
// Here 1.0 is the scaling factor that allows us to control the size of the update.
sgd.update(1.0);
```

Note that this very simple example that doesn't cover things like memory initialization, reading/writing models, recurrent/LSTM networks, or adding biases to functions. The best way to get an idea of how to use cnn for real is to look in the `example` directory, particularly starting with the simplest `xor` example.

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