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DeepJava (DJ) is a DeepLearning framework. One might ask: why do we need yet another DeepLearning framework? Good question. There is, at least, one thing that makes DJ different: it is, mainly, for the educational purpose. What does this mean exactly:
- Codebase should be understandable (not fast). Anyone who has read a book about DeepLearning should be able to map main concepts from the book to the code in this framework (if concept already implemented);
- You can experiment. Educational purpose means that the framework open for the experiments. Do you have an idea how to represent a computational graph in a non-canonical way? You can try it here!
- Simple to use. DJ prioritizing the simplicity over the speed, such priority would not be possible with other frameworks.
Network that we are building is describe in the chapter 2.
Context context = new Context(
/* learningRate */ 0.2,
/* debug mode */ false);
InputNeuron inputFriend = new InputNeuron("friend");
InputNeuron inputVodka = new InputNeuron("vodka");
InputNeuron inputSunny = new InputNeuron("sunny");
ConnectedNeuron outputNeuron
= new ConnectedNeuron.Builder()
.bias(0.1)
.activationFunction(new Sigmoid())
.context(context)
.build();
inputFriend.connect(outputNeuron, wFriend);
inputVodka.connect(outputNeuron, wVodka);
inputSunny.connect(outputNeuron, wSunny);
// Sending input signal to the graph:
inputFriend.forwardSignalReceived(null, 1.);
inputVodka.forwardSignalReceived(null, 1.);
inputSunny.forwardSignalReceived(null, 1.);
// Getting result and calculating the error:
double result = outputNeuron.getForwardResult();
double expectedResult = 1.;
double errorDy = 2 * (expectedResult - result);
// Sending error back to the graph:
outputNeuron.backwardSignalReceived(errorDy);There are several ways:
- open bug/issue if you have found something or want us to do something;
- submit a PR;
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