Tensorflow is an open-source library for machine learning. In android, we have limited computing power as well as resources. So we are using TensorFlow light which is specifically designed to operate on devices with limited power. In this post, we going to see a classification example called the iris dataset. The dataset contains 3 classes of 50 instances each, where each class refers to the type of iris plant.
Attribute information:
- sepal length in cm
- sepal width in cm
- petal length in cm
- petal width in cm
Based on the information given in the input, we will predict whether the plant is Iris Setosa, Iris Versicolour, or Iris Virginica. You can refer to this link for more information.
Step by step Implementation
Step 1:
Download the iris data set (file name: iris.data) from this (https://archive.ics.uci.edu/ml/machine-learning-databases/iris/) link.
Step 2:
Create a new python file with a name iris in the Jupyter notebook. Put the iris.data file in the same directory where iris.ipynb resides. Copy the following code in the Jupyter notebook file.
iris.ipynb
Python
import pandas as pdimport numpy as npimport tensorflow as tffrom sklearn.preprocessing import LabelEncoderfrom keras.utils import to_categorical# reading the csb into data framedf = pd.read_csv('iris.data')# specifying the columns values into x and y variable# iloc range based selecting 0 to 4 (4) valuesX = df.iloc[:, :4].valuesy = df.iloc[:, 4].values# normalizing labelsle = LabelEncoder()# performing fit and transform data on yy = le.fit_transform(y)y = to_categorical(y)from tensorflow.keras.layers import Densefrom tensorflow.keras.models import Sequentialmodel = Sequential()# input layer# passing number neurons =64# relu activation# shape of neuron 4model.add(Dense(64, activation='relu', input_shape=[4]))# processing layer# adding another denser layer of size 64model.add(Dense(64))# creating 3 output neuronmodel.add(Dense(3, activation='softmax'))# compiling modelmodel.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['acc'])# training the model for fixed number of iterations (epoches)model.fit(X, y, epochs=200)from tensorflow import liteconverter = lite.TFLiteConverter.from_keras_model(model)tfmodel = converter.convert()open('iris.tflite', 'wb').write(tfmodel) |
Step 3:
After executing the line open(‘iris.tflite’,’wb’).write(tfmodel) a new file named iris.tflite will get created in the same directory where iris.data resides.
A) Open Android Studio. Create a new kotlin-android project. (You can refer here for creating a project).
B) Right-click on app > New > Other >TensorFlow Lite Model
C) Click on the folder icon.
D) Navigate to iris.tflite file
E) Click on OK
F) Your model will look like this after clicking on the finish. (It may take some time to load).
Copy the code and paste it in the click listener of a button in MainActivity.kt.(It is shown below).
Step 5: Create XML layout for prediction
Navigate to the app > res > layout > activity_main.xml and add the below code to that file. Below is the code for the activity_main.xml file.
XML
<?xml version="1.0" encoding="utf-8"?><androidx.constraintlayout.widget.ConstraintLayout android:layout_width="match_parent" android:layout_height="match_parent" tools:context=".MainActivity"> <ScrollView android:layout_width="match_parent" android:layout_height="match_parent" android:layout_marginBottom="50dp"> <LinearLayout android:layout_width="match_parent" android:layout_height="match_parent" android:orientation="vertical"> <!-- creating edittexts for input--> <EditText android:id="@+id/tf1" android:layout_width="175dp" android:layout_height="wrap_content" android:layout_gravity="center" android:layout_marginTop="70dp" android:ems="10" android:inputType="numberDecimal" /> <EditText android:id="@+id/tf2" android:layout_width="175dp" android:layout_height="wrap_content" android:layout_gravity="center" android:layout_marginTop="20dp" android:ems="10" android:inputType="numberDecimal" /> <EditText android:id="@+id/tf3" android:layout_width="175dp" android:layout_height="wrap_content" android:layout_gravity="center" android:layout_marginTop="20dp" android:ems="10" android:inputType="numberDecimal" /> <EditText android:id="@+id/tf4" android:layout_width="175dp" android:layout_height="wrap_content" android:layout_gravity="center" android:layout_marginTop="20dp" android:ems="10" android:inputType="numberDecimal" /> <!-- creating Button for input--> <!-- after clicking on button we will see prediction--> <Button android:id="@+id/button" android:layout_width="wrap_content" android:layout_height="wrap_content" android:layout_gravity="center" android:layout_marginTop="100dp" android:text="Button" app:layout_constraintBottom_toTopOf="@+id/textView" app:layout_constraintEnd_toEndOf="parent" app:layout_constraintHorizontal_bias="0.0" app:layout_constraintStart_toStartOf="parent" /> <!-- creating textview on which we will see prediction--> <TextView android:id="@+id/textView" android:layout_width="wrap_content" android:layout_height="wrap_content" android:layout_gravity="center" android:layout_marginTop="50dp" android:text="TextView" android:textSize="20dp" app:layout_constraintEnd_toEndOf="parent" /> </LinearLayout> </ScrollView></androidx.constraintlayout.widget.ConstraintLayout> |
Step 6: Working with the MainActivity.kt file
Go to the MainActivity.kt file and refer to the following code. Below is the code for the MainActivity.kt file. Comments are added inside the code to understand the code in more detail.
Kotlin
import androidx.appcompat.app.AppCompatActivityimport android.os.Bundleimport android.view.Viewimport android.widget.Buttonimport android.widget.EditTextimport android.widget.TextViewimport com.example.gfgtfdemo.ml.Irisimport org.tensorflow.lite.DataTypeimport org.tensorflow.lite.support.tensorbuffer.TensorBufferimport java.nio.ByteBufferclass MainActivity : AppCompatActivity() { override fun onCreate(savedInstanceState: Bundle?) { super.onCreate(savedInstanceState) setContentView(R.layout.activity_main) // getting the object edit texts var ed1: EditText = findViewById(R.id.tf1); var ed2: EditText = findViewById(R.id.tf2); var ed3: EditText = findViewById(R.id.tf3); var ed4: EditText = findViewById(R.id.tf4); // getting the object of result textview var txtView: TextView = findViewById(R.id.textView); var b: Button = findViewById<Button>(R.id.button); // registering listener b.setOnClickListener(View.OnClickListener { val model = Iris.newInstance(this) // getting values from edit text and converting to float var v1: Float = ed1.text.toString().toFloat(); var v2: Float = ed2.text.toString().toFloat(); var v3: Float = ed3.text.toString().toFloat(); var v4: Float = ed4.text.toString().toFloat(); /*************************ML MODEL CODE STARTS HERE******************/ // creating byte buffer which will act as input for model var byte_buffer: ByteBuffer = ByteBuffer.allocateDirect(4 * 4) byte_buffer.putFloat(v1) byte_buffer.putFloat(v2) byte_buffer.putFloat(v3) byte_buffer.putFloat(v4) // Creates inputs for reference. val inputFeature0 = TensorBuffer.createFixedSize(intArrayOf(1, 4), DataType.FLOAT32) inputFeature0.loadBuffer(byte_buffer) // Runs model inference and gets result. val outputs = model.process(inputFeature0) val outputFeature0 = outputs.outputFeature0AsTensorBuffer.floatArray // setting the result to the output textview txtView.setText( "Iris-setosa : =" + outputFeature0[0].toString() + "\n" + "Iris-versicolor : =" + outputFeature0[1].toString() + "\n" + "Iris-virginica: =" + outputFeature0[2].toString() ) // Releases model resources if no longer used. model.close() }) }} |
Output:
You can download this project from here.
