lstm java code
时间: 2023-08-02 08:05:26 浏览: 64
下面是一个简单的 Java LSTM 网络的代码示例,使用 DeepLearning4j 框架:
```java
import org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.GradientNormalization;
import org.deeplearning4j.nn.conf.LearningRatePolicy;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.GravesLSTM;
import org.deeplearning4j.nn.conf.layers.RnnOutputLayer;
import org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.lossfunctions.LossFunctions;
public class LSTMDemo {
public static void main(String[] args) throws Exception {
// Define LSTM network configuration
int lstmLayerSize = 200;
int numHiddenLayers = 2;
int numEpochs = 100;
double learningRate = 0.001;
int truncatedBPTTLength = 22;
int seed = 123;
NeuralNetConfiguration.Builder lstmBuilder = new NeuralNetConfiguration.Builder()
.seed(seed)
.weightInit(WeightInit.XAVIER)
.updater(new Adam(learningRate))
.list()
.layer(0, new GravesLSTM.Builder()
.nIn(1)
.nOut(lstmLayerSize)
.activation(Activation.TANH)
.build())
.pretrain(false)
.backprop(true)
.build();
NeuralNetConfiguration.ListBuilder builder = new NeuralNetConfiguration.Builder()
.seed(seed)
.weightInit(WeightInit.XAVIER)
.updater(new Adam(learningRate))
.list()
.layer(new GravesLSTM.Builder()
.nIn(lstmLayerSize)
.nOut(lstmLayerSize)
.activation(Activation.TANH)
.build());
for (int i = 0; i < numHiddenLayers - 1; i++) {
builder.layer(new GravesLSTM.Builder()
.nIn(lstmLayerSize)
.nOut(lstmLayerSize)
.activation(Activation.TANH)
.build());
}
builder.layer(new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX)
.nIn(lstmLayerSize)
.nOut(1)
.build())
.pretrain(false)
.backprop(true)
.build();
MultiLayerNetwork net = new MultiLayerNetwork(builder.build());
net.init();
// Load training data
double[][] input = new double[100][1];
double[][] output = new double[100][1];
for (int i = 0; i < 100; i++) {
input[i][0] = Math.sin(i * 0.1);
output[i][0] = Math.sin((i + 1) * 0.1);
}
DataSetIterator iterator = new ListDataSetIterator(
new DoubleDataSetIterator(input, output)
.next(10)
.build());
// Train LSTM network
net.setListeners(new ScoreIterationListener(1));
net.fit(iterator, numEpochs);
}
}
```
在这个示例中,我们定义了一个包含两个 LSTM 层和一个 RnnOutputLayer 的 LSTM 网络。我们使用 sine 函数生成了一些样本数据来训练网络,并使用 ListDataSetIterator 将其加载到网络中进行训练。通过调整参数,您可以在 Java 中实现更复杂的 LSTM 网络。
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