With the recent advancements in machine learning, more and more developers are looking to build applications that can take advantage of these technologies. In this post, we'll take a look at how to build a machine learning application using Spring Boot and TensorFlow.
We'll start by looking at what machine learning is and how it can be used to build applications. We'll then take a look at how to set up a Spring Boot project and integrate TensorFlow. Finally, we'll go through a simple example of how to use TensorFlow to build a machine learning model.
Machine learning is a subfield of artificial intelligence that deals with the construction and study of algorithms that can learn from data. These algorithms can be used to build models that can make predictions about new data.
There are two main types of machine learning: supervised and unsupervised. Supervised learning is where the data is labeled and the algorithm is trained to learn the mapping from the input data to the output labels. Unsupervised learning is where the data is not labeled and the algorithm is trained to learn the structure of the data.
Machine learning can be used to build applications that can automatically learn and improve from data. For example, a machine learning application could be used to automatically classify images or detect objects in images.
Other examples of machine learning applications include:
We'll start by setting up a Spring Boot project. Spring Boot is a framework that makes it easy to create stand-alone, production-grade Spring-based applications.
We'll use the Spring Initializr to create our project. The Spring Initializr is a web-based tool that can be used to generate a Spring Boot project.
We'll select the following options:
Once the project has been generated, we can import it into our IDE of choice.
TensorFlow is an open source machine learning platform that can be used to build and train machine learning models.
To add TensorFlow to our project, we'll need to add the following dependency to our pom.xml file:
<dependency>
<groupId>org.tensorflow</groupId>
<artifactId>tensorflow-core</artifactId>
<version>1.10.0</version>
</dependency>
Now that we have our project set up, we can start building our machine learning model.
We'll start by creating a new class called MyModel. This class will contain our machine learning model.
public class MyModel {
}
Next, we'll need to add the following import statements:
import org.tensorflow.Tensor;
import org.tensorflow.TensorFlow;
Now, we can start building our model. We'll start by defining our input and output tensors. The input tensor will be a 2D tensor of shape [1, 2]. The output tensor will be a 1D tensor of shape [1].
public class MyModel {
private static final int INPUT_SIZE = 2;
private static final int OUTPUT_SIZE = 1;
private static final Tensor<Float> input = Tensor.create(new float[][] {
{1.0f, 2.0f}
}, Float.class);
private static final Tensor<Float> output = Tensor.create(new float[] {
3.0f
}, Float.class);
}
Next, we'll define our model. We'll use a simple linear regression model.
public class MyModel {
private static final int INPUT_SIZE = 2;
private static final int OUTPUT_SIZE = 1;
private static final Tensor<Float> input = Tensor.create(new float[][] {
{1.0f, 2.0f}
}, Float.class);
private static final Tensor<Float> output = Tensor.create(new float[] {
3.0f
}, Float.class);
private static final LinearRegressionModel model = LinearRegressionModel.create(
input, output);
}
Now, we can train our model. We'll train our model for 1000 iterations.
public class MyModel {
private static final int INPUT_SIZE = 2;
private static final int OUTPUT_SIZE = 1;
private static final Tensor<Float> input = Tensor.create(new float[][] {
{1.0f, 2.0f}
}, Float.class);
private static final Tensor<Float> output = Tensor.create(new float[] {
3.0f
}, Float.class);
private static final LinearRegressionModel model = LinearRegressionModel.create(
input, output);
public static void main(String[] args) {
for (int i = 0; i < 1000; i++) {
model.train();
}
}
}
Finally, we can use our trained model to make predictions.
public class MyModel {
private static final int INPUT_SIZE = 2;
private static final int OUTPUT_SIZE = 1;
private static final Tensor<Float> input = Tensor.create(new float[][] {
{1.0f, 2.0f}
}, Float.class);
private static final Tensor<Float> output = Tensor.create(new float[] {
3.0f
}, Float.class);
private static final LinearRegressionModel model = LinearRegressionModel.create(
input, output);
public static void main(String[] args) {
for (int i = 0; i < 1000; i++) {
model.train();
}
Tensor<Float> prediction = model.predict(input);
System.out.println(prediction.data());
}
}
In this post, we've seen how to build a machine learning application using Spring Boot and TensorFlow. We've started by looking at what machine learning is and how it can be used to build applications. We've then taken a look at how to set up a Spring Boot project and integrate TensorFlow. Finally, we've gone through a simple example of how to use TensorFlow to build a machine learning model.