A Java implement of Deep Neural Network.
public void buildNetwork(int numOfTrainData){
//首先构建神经网络对象，并设置参数
network = new Network();
network.setThreadNum(8);
network.setBatch(20);
network.setLrAttenuation(0.9f);
network.setLoss(new MSELoss());
optimizer = new SGDOptimizer(0.1f);
network.setOptimizer(optimizer);
buildConvNetwork();
network.prepare();
}
private void buildConvNetwork(){
InputLayer layer1 = new InputLayer(network,28,28,1);
network.addLayer(layer1);
Conv2dLayer conv1 = new Conv2dLayer(network,28,28,1,8,3,1);
conv1.setActivationFunc(new ReluActivationFunc());
network.addLayer(conv1);
PoolMaxLayer pool1 = new PoolMaxLayer(network,28,28,8,2,2);
network.addLayer(pool1);
Conv2dLayer conv2 = new Conv2dLayer(network,14,14,8,8,3,1);
conv2.setActivationFunc(new ReluActivationFunc());
network.addLayer(conv2);
PoolMeanLayer pool2 = new PoolMeanLayer(network,14,14,8,2,2);
network.addLayer(pool2);
FullConnectionLayer fc1 = new FullConnectionLayer(network,7*7*8,256);
fc1.setActivationFunc(new ReluActivationFunc());
network.addLayer(fc1);
FullConnectionLayer fc2 = new FullConnectionLayer(network,256,10);
fc2.setActivationFunc(new ReluActivationFunc());
network.addLayer(fc2);
SoftMaxLayer sflayer = new SoftMaxLayer(network,10);
network.addLayer(sflayer);
}public void buildAddNetwork() {
InputLayer layer1 = new InputLayer(network,2,1,1);
network.addLayer(layer1);
RecurrentLayer rl = new RecurrentLayer(network,RecurrentLayer.RecurrentType.RNN,2,2,100);
network.addLayer(rl);
FullConnectionLayer fc = new FullConnectionLayer(network,100,2);
network.addLayer(fc);
}
public void buildNetwork(){
//首先构建神经网络对象，并设置参数
network = new Network();
network.setThreadNum(4);
network.setBatch(100);
network.setLrDecay(0.7f);
network.setLoss(new MSELoss());//CrossEntropyLoss
optimizer = new SGDOptimizer(0.9f);
network.setOptimizer(optimizer);
buildAddNetwork();
network.prepare();
}Pull request is welcome.
QQ group: 704153141
1.without any dependency
2.Basic layer: input layer, conv2d layer,deepwise conv2d layer, pooling layer(MAX and MEAN), full connect layer, softmax layer, recurrent layer
3.Loss function: Cross Entropy,log like-hood ,MSE loss
4.Optimize method: SGD(SGD without momentum),SGDM(SGD with momentum)
5.active funcs:sigmod , tanh, relu
6.L1 and L2 regularization is supported.
7.Support for multi-threaded acceleration
mnist test is offered(2017).
cifar10 test is offered(2018-12-23).
Can achieve 99% accuracy in mnist dataset(10 conv2d + pool max + 10 conv2d + pool mean + 256 fc + 10 fc + softmax).
BSD 3-Clause