Table of Contents
- Introduction
- Prerequisites
- Installation
- Creating a Neural Network
- Training the Model
- Evaluating the Model
- Conclusion
Introduction
In this tutorial, we will explore the popular deep learning libraries TensorFlow 2.0 and Keras in Python. Deep learning has revolutionized the field of artificial intelligence by enabling computers to learn complex patterns and make predictions from large datasets. By the end of this tutorial, you will have a good understanding of how to build and train neural networks using TensorFlow 2.0 and Keras.
Prerequisites
Before getting started, you should have a basic understanding of Python programming and some familiarity with machine learning concepts. It would also be beneficial to have some knowledge of linear algebra and calculus, although it is not necessary for following along with the tutorial.
Installation
To begin, we need to install TensorFlow 2.0 and Keras. Open your command line interface and run the following command:
python
pip install tensorflow keras
This will install both libraries along with their dependencies.
Creating a Neural Network
To demonstrate the power of TensorFlow 2.0 and Keras, let’s create a simple neural network that can classify images of handwritten digits from the famous MNIST dataset. Open your favorite Python editor and create a new file called digit_classifier.py.
First, we need to import the necessary modules:
python
import tensorflow as tf
from tensorflow import keras
Next, let’s define the architecture of our neural network. In this case, we will use a sequential model, which is the simplest kind of neural network. Add the following code to your file:
python
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(10, activation='softmax')
])
In the above code, we define a sequential model using the Sequential class from Keras. The model consists of three layers: the input layer, a hidden layer with 128 neurons and a ReLU activation function, and an output layer with 10 neurons and a softmax activation function. The input shape of (28, 28) corresponds to the size of the MNIST images.
Training the Model
Now that we have defined the architecture of our neural network, we need to train it on the MNIST dataset. Add the following code to your file: ```python model.compile(optimizer=’adam’, loss=’sparse_categorical_crossentropy’, metrics=[‘accuracy’])
model.fit(x_train, y_train, epochs=5)
``` In the above code, we first specify the optimizer, loss function, and evaluation metric for our model using the `compile` method. We use the Adam optimizer, which is a popular choice for deep learning tasks. The loss function `sparse_categorical_crossentropy` is suitable for multiclass classification problems. Finally, we train the model on the training data using the `fit` method.
Evaluating the Model
Once the model is trained, we can evaluate its performance on the test dataset. Add the following code to your file:
python
test_loss, test_acc = model.evaluate(x_test, y_test)
print('Test accuracy:', test_acc)
In the above code, we use the evaluate method to compute the loss and accuracy of the model on the test data. The results are then printed to the console.
Conclusion
In this tutorial, we have seen how to use TensorFlow 2.0 and Keras to build and train a simple neural network for image classification. We started by installing the necessary libraries and then defined the architecture of our model. After training the model on the MNIST dataset, we evaluated its performance on the test data.