In this post, we'll learn how to build a machine learning system using Spring Boot and TensorFlow. We'll go through the process of setting up a development environment, training a model, and deploying the trained model to a web application.
The first thing we need to do is set up our development environment. We'll need to install the following software:
We can install Java 8 and Maven using our favorite package manager. For TensorFlow, we'll need to download the binaries from the TensorFlow website. Once we have everything installed, we can create a new Maven project and add the following dependencies to our pom.xml
file:
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency>
<groupId>org.tensorflow</groupId>
<artifactId>tensorflow-core</artifactId>
<version>1.15.0</version>
</dependency>
</dependencies>
Now that we have our development environment set up, we can start training our machine learning model. We'll use the MNIST dataset for this example. The MNIST dataset is a collection of handwritten digits that is commonly used for training image recognition models.
We'll start by creating a new Java class called MnistClassifier.java
. In this class, we'll use TensorFlow to train a simple neural network to classify MNIST digits. We'll use the Keras API to simplify the development of our neural network.
First, we'll need to import the following packages:
import org.tensorflow.keras.datasets.mnist.MnistDataset;
import org.tensorflow.keras.models.Sequential;
import org.tensorflow.keras.layers.Dense;
import org.tensorflow.keras.optimizers.SGD;
Next, we'll load the MNIST dataset:
MnistDataset dataset = MnistDataset.create();
Now, we can create our neural network:
Sequential model = new Sequential();
model.add(new Dense(128, activation="relu", inputShape=(784,)));
model.add(new Dense(10, activation="softmax"));
We can now compile and train our model:
model.compile(
optimizer=new SGD(0.001),
loss="categorical_crossentropy",
metrics=["accuracy"]
);
model.fit(
dataset.getTrainImages(), dataset.getTrainLabels(),
epochs=10, batchSize=128
);
Now that we have a trained model, we can deploy it to a web application. We'll use Spring Boot to simplify the development of our web application.
First, we'll need to create a new Java class called MnistController.java
. In this class, we'll create a REST controller that exposes a /classify
endpoint. This endpoint will take an image of a handwritten digit and return the predicted digit.
We'll start by importing the following packages:
import org.springframework.web.bind.annotation.PostMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
Next, we'll create our MnistController
class and annotate it with @RestController
:
@RestController
public class MnistController {
}
Now, we can create our /classify
endpoint:
@PostMapping("/classify")
public String classify(@RequestParam("image") String image) {
// TODO: Classify the image and return the predicted digit
}
In the classify
method, we'll need to classify the image and return the predicted digit. We can do this by using our trained MnistClassifier
class:
MnistClassifier classifier = new MnistClassifier();
String predictedDigit = classifier.classify(image);
return predictedDigit;
In this post, we learned how to build a machine learning system using Spring Boot and TensorFlow. We went through the process of setting up a development environment, training a model, and deploying the trained model to a web application.