卷积神经网络java代码
时间: 2023-09-18 11:05:43 浏览: 34
以下是一个简单的卷积神经网络的 Java 代码示例。请注意,这只是一个示例,实际应用中可能需要进行更多的调整和优化。
```java
public class ConvolutionalNeuralNetwork {
private int imageSize;
private int numClasses;
private double learningRate;
private int batchSize;
private int numIterations;
private double[][][] filters;
private double[] biases;
private Random random;
public ConvolutionalNeuralNetwork(int imageSize, int numClasses, double learningRate, int batchSize, int numIterations) {
this.imageSize = imageSize;
this.numClasses = numClasses;
this.learningRate = learningRate;
this.batchSize = batchSize;
this.numIterations = numIterations;
this.filters = new double[3][3][32]; // 3x3 filters, 32 channels
this.biases = new double[32];
this.random = new Random();
initializeFiltersAndBiases();
}
private void initializeFiltersAndBiases() {
// Initialize filters with random values
for (int i = 0; i < filters.length; i++) {
for (int j = 0; j < filters[0].length; j++) {
for (int k = 0; k < filters[0][0].length; k++) {
filters[i][j][k] = random.nextDouble() * 2 - 1;
}
}
}
// Initialize biases with zeros
Arrays.fill(biases, 0);
}
public void train(double[][][] input, int[] labels) {
for (int iteration = 0; iteration < numIterations; iteration++) {
double[][][][] batchInputs = new double[batchSize][imageSize][imageSize][3];
int[] batchLabels = new int[batchSize];
// Create a random batch of inputs and labels
for (int i = 0; i < batchSize; i++) {
int index = random.nextInt(input.length);
batchInputs[i] = input[index];
batchLabels[i] = labels[index];
}
// Forward pass
double[][][] conv1 = convolution(batchInputs, filters, biases, 1, "valid");
double[][][] relu1 = relu(conv1);
double[][][] pool1 = pooling(relu1, 2, 2, "valid");
double[] flatten1 = flatten(pool1);
double[] fc1 = fullyConnected(flatten1, numClasses, "softmax");
// Backward pass
double[] lossGradient = softmaxGradient(fc1, batchLabels);
double[] fc1Gradient = fullyConnectedGradient(flatten1, lossGradient, numClasses);
double[][][] pool1Gradient = unpooling(fc1Gradient, pool1, 2, 2, "valid");
double[][][] relu1Gradient = reluGradient(pool1Gradient, relu1);
double[][][] conv1Gradient = convolutionGradient(batchInputs, relu1Gradient, filters, 1, "valid");
updateFiltersAndBiases(conv1Gradient, lossGradient);
}
}
public int predict(double[][][] input) {
double[][][] conv1 = convolution(new double[][][][] {input}, filters, biases, 1, "valid");
double[][][] relu1 = relu(conv1);
double[][][] pool1 = pooling(relu1, 2, 2, "valid");
double[] flatten1 = flatten(pool1);
double[] fc1 = fullyConnected(flatten1, numClasses, "softmax");
return argmax(fc1);
}
private double[][][] convolution(double[][][][] input, double[][][] filters, double[] biases, int stride, String padding) {
// Perform convolution operation
// ...
return output;
}
private double[][][] convolutionGradient(double[][][][] input, double[][][] outputGradient, double[][][] filters, int stride, String padding) {
// Compute gradients for convolution operation
// ...
return inputGradient;
}
private double[][][] relu(double[][][] input) {
// Apply ReLU activation function
// ...
return output;
}
private double[][][] reluGradient(double[][][] outputGradient, double[][][] input) {
// Compute gradients for ReLU activation function
// ...
return inputGradient;
}
private double[][][] pooling(double[][][] input, int poolSize, int stride, String padding) {
// Perform pooling operation
// ...
return output;
}
private double[][][] unpooling(double[][][] outputGradient, double[][][] input, int poolSize, int stride, String padding) {
// Compute gradients for pooling operation
// ...
return inputGradient;
}
private double[] flatten(double[][][] input) {
// Convert 3D array to 1D array
// ...
return output;
}
private double[] fullyConnected(double[] input, int outputSize, String activation) {
// Perform fully connected layer operation
// ...
return output;
}
private double[] fullyConnectedGradient(double[] input, double[] outputGradient, int outputSize) {
// Compute gradients for fully connected layer operation
// ...
return inputGradient;
}
private double[] softmax(double[] input) {
// Apply softmax activation function
// ...
return output;
}
private double[] softmaxGradient(double[] output, int[] labels) {
// Compute gradients for softmax activation function
// ...
return inputGradient;
}
private int argmax(double[] input) {
// Find index of maximum value in array
// ...
return index;
}
private void updateFiltersAndBiases(double[][][] conv1Gradient, double[] lossGradient) {
// Update filters and biases using gradients and learning rate
// ...
}
}
```