Omega-AI: Omega-AI：基于java打造的深度学习框架，帮助你快速搭建神经网络，实现训练 ...

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开源软件名称：

Omega-AI

开源软件地址：

https://gitee.com/iangellove/omega-ai

开源软件介绍：

自己打造一个深度学习框架 for java

前言

从16年开始利用空余时间研究深度学习的方面，由于工作的原因，最熟悉的编程语言就是java，所以框架的编程语言自然而然就使用了java。自己打造框架的初衷就是为了更加深入了解各个算法、模型、实现的原理和思路。

框架介绍

Omega-AI：基于java打造的深度学习框架，帮助你快速搭建神经网络，实现训练或测试模型，支持多线程运算，框架目前支持BP神经网络和卷积神经网络的构建。

源码地址：

https://gitee.com/iangellove/omega-ai

https://github.com/iangellove/Omega-AI

Demo展示

基于卷积神经网络mnist手写数字识别在这里插入图片描述

功能介绍

支持的网络层类型：

Fullylayer 全连接层

ConvolutionLayer 卷积层

PoolingLayer 池化层

激活函数层

SoftmaxLayer (softmax激活函)

ReluLayer

LeakyReluLayer

TanhLayer

SigmodLayer

归一化层

BNLayer (Batch Normalization)

DropoutLayer

优化器

Momentum

Adam

训练器

BGDOptimizer (批量梯度下降法)

MBSGDOptimizer (小批量随机梯度下降)

SGDOptimizer（随机梯度下降算法）

损失函数(loss function)

SquareLoss (平方差损失函数)

CrossEntropyLoss (交叉熵损失函数)

学习率更新器（LearnRateUpdate）

NONE (固定学习率)

LR_DECAY (decay)

GD_GECAY (gd_decay)

数据加载器

.bin (二进制数据文件)

.idx3-ubyte

.txt

使用说明

自带的数据集

iris（鸢尾花数据集）

mnist（手写数字数据集）

cifat_10 （cifat_10数据集）

数据集成绩

iris 训练次数8 测试数据集准确率100%

mnist 训练次数8 测试数据集准确率98.6%

cifat_10 训练次数8 测试数据集准确率76.6%

事例代码

bp iris demo

public void bpNetwork_iris() {		// TODO Auto-generated method stub		/**		 * 读取训练数据集		 */		String iris_train = "/dataset/iris/iris.txt";				String iris_test = "/dataset/iris/iris_test.txt";				String[] labelSet = new String[] {"1","-1"};				DataSet trainData = DataLoader.loalDataByTxt(iris_train, ",", 1, 1, 4, 2,labelSet);		DataSet testData = DataLoader.loalDataByTxt(iris_test, ",", 1, 1, 4, 2,labelSet);				System.out.println("train_data:"+JsonUtils.toJson(trainData));			BPNetwork netWork = new BPNetwork(new SoftmaxWithCrossEntropyLoss());				InputLayer inputLayer = new InputLayer(1,1,4);				FullyLayer hidden1 = new FullyLayer(4, 40);				ReluLayer active1 = new ReluLayer();				FullyLayer hidden2 = new FullyLayer(40, 20);				ReluLayer active2 = new ReluLayer();				FullyLayer hidden3 = new FullyLayer(20, 2);		SoftmaxWithCrossEntropyLayer hidden4 = new SoftmaxWithCrossEntropyLayer(2);				netWork.addLayer(inputLayer);		netWork.addLayer(hidden1);		netWork.addLayer(active1);		netWork.addLayer(hidden2);		netWork.addLayer(active2);		netWork.addLayer(hidden3);		netWork.addLayer(hidden4);		try {						MBSGDOptimizer optimizer = new MBSGDOptimizer(netWork, 8, 0.00001d, 10, LearnRateUpdate.NONE);					optimizer.train(trainData);						optimizer.test(testData);					} catch (Exception e) {			// TODO Auto-generated catch block			e.printStackTrace();		}	}

cnn mnist demo

public void cnnNetwork_mnist() {		// TODO Auto-generated method stub				try {			/**			 * 读取训练数据集			 */			String mnist_train_data = "/dataset/mnist/train-images.idx3-ubyte";						String mnist_train_label = "/dataset/mnist/train-labels.idx1-ubyte";						String mnist_test_data = "/dataset/mnist/t10k-images.idx3-ubyte";						String mnist_test_label = "/dataset/mnist/t10k-labels.idx1-ubyte";						String[] labelSet = new String[] {"0","1","2","3","4","5","6","7","8","9"};						Resource trainDataRes = new ClassPathResource(mnist_train_data);			Resource trainLabelRes = new ClassPathResource(mnist_train_label);						Resource testDataRes = new ClassPathResource(mnist_test_data);						Resource testLabelRes = new ClassPathResource(mnist_test_label);						DataSet trainData = DataLoader.loadDataByUByte(trainDataRes.getFile(), trainLabelRes.getFile(), labelSet, 1, 1 , 784, true);						DataSet testData = DataLoader.loadDataByUByte(testDataRes.getFile(), testLabelRes.getFile(), labelSet, 1, 1 , 784, true);			int channel = 1;						int height = 28;						int width = 28;						CNN netWork = new CNN(new SoftmaxWithCrossEntropyLoss(), UpdaterType.momentum);						netWork.learnRate = 0.001d;						InputLayer inputLayer = new InputLayer(channel, 1, 784);						ConvolutionLayer conv1 = new ConvolutionLayer(channel, 6, width, height, 5, 5, 2, 1, false);						BNLayer bn1 = new BNLayer();						LeakyReluLayer active1 = new LeakyReluLayer();						PoolingLayer pool1 = new PoolingLayer(conv1.oChannel, conv1.oWidth, conv1.oHeight, 2, 2, 2, PoolingType.MAX_POOLING);						ConvolutionLayer conv2 = new ConvolutionLayer(pool1.oChannel, 12, pool1.oWidth, pool1.oHeight, 5, 5, 0, 1, false);						BNLayer bn2 = new BNLayer();						LeakyReluLayer active2 = new LeakyReluLayer();						DropoutLayer drop1 = new DropoutLayer(0.5d);									PoolingLayer pool2 = new PoolingLayer(conv2.oChannel, conv2.oWidth, conv2.oHeight, 2, 2, 2, PoolingType.MAX_POOLING);			int fInputCount = pool2.oChannel * pool2.oWidth * pool2.oHeight;						int inputCount = (int) (Math.sqrt((fInputCount) + 10) + 10);						FullyLayer full1 = new FullyLayer(fInputCount, inputCount, false);			BNLayer bn3 = new BNLayer();						LeakyReluLayer active3 = new LeakyReluLayer();						FullyLayer full2 = new FullyLayer(inputCount, 10);						SoftmaxWithCrossEntropyLayer softmax = new SoftmaxWithCrossEntropyLayer(10);			netWork.addLayer(inputLayer);			netWork.addLayer(conv1);			netWork.addLayer(bn1);			netWork.addLayer(active1);			netWork.addLayer(pool1);			netWork.addLayer(conv2);			netWork.addLayer(bn2);			netWork.addLayer(active2);			netWork.addLayer(drop1);			netWork.addLayer(pool2);			netWork.addLayer(full1);			netWork.addLayer(bn3);			netWork.addLayer(active3);			netWork.addLayer(full2);			netWork.addLayer(softmax);			MBSGDOptimizer optimizer = new MBSGDOptimizer(netWork, 10, 0.0001d, 96, LearnRateUpdate.NONE);			long start = System.currentTimeMillis();						optimizer.train(trainData);						optimizer.test(testData);						System.out.println(((System.currentTimeMillis() - start) / 1000) + "s.");					} catch (Exception e) {			// TODO: handle exception			e.printStackTrace();		}			}

cnn cifar10 demo

public void cnnNetwork_cifar10() {		// TODO Auto-generated method stub				try {	    	String[] labelSet = new String[] {"airplane","automobile","bird","cat","deer","dog","frog","horse","ship","truck"};	    				String[] train_data_filenames = new String[] {					"/dataset/cifar-10/data_batch_1.bin",					"/dataset/cifar-10/data_batch_2.bin",					"/dataset/cifar-10/data_batch_3.bin",					"/dataset/cifar-10/data_batch_4.bin",					"/dataset/cifar-10/data_batch_5.bin"			};						String test_data_filename = "/dataset/cifar-10/test_batch.bin";						DataSet trainData = DataLoader.getImagesToDataSetByBin(train_data_filenames, 10000, 3, 32, 32, 10, true, labelSet);	    				DataSet testData = DataLoader.getImagesToDataSetByBin(test_data_filename, 10000, 3, 32, 32, 10, true, labelSet);						System.out.println("data is ready.");						int channel = 3;						int height = 32;						int width = 32;						CNN netWork = new CNN(new SoftmaxWithCrossEntropyLoss(), UpdaterType.adam);						netWork.learnRate = 0.001d;						InputLayer inputLayer = new InputLayer(channel, height, width);			netWork.addLayer(inputLayer);						ConvolutionLayer conv1 = new ConvolutionLayer(channel, 16, width, height, 3, 3, 1, 1,false);			netWork.addLayer(conv1);						BNLayer bn1 = new BNLayer();			netWork.addLayer(bn1);						ReluLayer active1 = new ReluLayer();			netWork.addLayer(active1);			PoolingLayer pool1 = new PoolingLayer(conv1.oChannel, conv1.oWidth, conv1.oHeight, 2, 2, 2, PoolingType.MAX_POOLING);			netWork.addLayer(pool1);									ConvolutionLayer conv3 = new ConvolutionLayer(pool1.oChannel, 32, pool1.oWidth, pool1.oHeight, 3, 3, 1, 1,false);			netWork.addLayer(conv3);						BNLayer bn3 = new BNLayer();			netWork.addLayer(bn3);						ReluLayer active3 = new ReluLayer();			netWork.addLayer(active3);						PoolingLayer pool2 = new PoolingLayer(conv3.oChannel, conv3.oWidth, conv3.oHeight, 2, 2, 2, PoolingType.MAX_POOLING);			netWork.addLayer(pool2);						ConvolutionLayer conv4 = new ConvolutionLayer(pool2.oChannel, 64, pool2.oWidth, pool2.oHeight, 3, 3, 1, 1,false);			netWork.addLayer(conv4);						BNLayer bn4 = new BNLayer();			netWork.addLayer(bn4);						ReluLayer active4 = new ReluLayer();			netWork.addLayer(active4);						PoolingLayer pool3 = new PoolingLayer(conv4.oChannel, conv4.oWidth, conv4.oHeight, 2, 2, 2, PoolingType.MAX_POOLING);			netWork.addLayer(pool3);			int fInputCount = pool3.oChannel * pool3.oWidth * pool3.oHeight;						FullyLayer full1 = new FullyLayer(fInputCount, 256, true);			netWork.addLayer(full1);						ReluLayer active9 = new ReluLayer();			netWork.addLayer(active9);			DropoutLayer drop1 = new DropoutLayer(0.5d);			netWork.addLayer(drop1);			FullyLayer full2 = new FullyLayer(full1.oWidth, 10, true);			netWork.addLayer(full2);						SoftmaxWithCrossEntropyLayer softmax = new SoftmaxWithCrossEntropyLayer(10);			netWork.addLayer(softmax);						MBSGDOptimizer optimizer = new MBSGDOptimizer(netWork, 30, 0.001d, 64, LearnRateUpdate.NONE);			long start = System.currentTimeMillis();						optimizer.train(trainData);						optimizer.test(testData);						System.out.println(((System.currentTimeMillis() - start) / 1000) + "s.");					} catch (Exception e) {			// TODO: handle exception			e.printStackTrace();		}			}