Lab Guide: Image Recognition with TensorFlow and Keras

Objective

To provide students with hands-on experience in developing, training, and evaluating image recognition models using TensorFlow and Keras.

Prerequisites

Basic understanding of Python programming.
Familiarity with machine learning concepts.
Python and necessary libraries installed: TensorFlow and Keras.

Lab Outline

Introduction to Image Recognition: - Discussing the basics of image recognition and convolutional neural networks (CNN).

Setting Up the Environment: - Installing TensorFlow and Keras:

    pip install tensorflow keras

Image Data Preprocessing:

Step 1: Importing Necessary Libraries:

    import tensorflow as tf
    from tensorflow.keras import datasets, layers, models

Step 2: Loading and Preprocessing Image Data:

    (train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()

    # Normalize pixel values to be between 0 and 1
    train_images, test_images = train_images / 255.0, test_images / 255.0

Building a Convolutional Neural Network (CNN):

Step 3: Defining the CNN Architecture:

    model = models.Sequential([
        layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
        layers.MaxPooling2D((2, 2)),
        layers.Conv2D(64, (3, 3), activation='relu'),
        layers.MaxPooling2D((2, 2)),
        layers.Conv2D(64, (3, 3), activation='relu')
    ])

Step 4: Adding Dense Layers:

    model.add(layers.Flatten())
    model.add(layers.Dense(64, activation='relu'))
    model.add(layers.Dense(10))

Compiling and Training the Model:

Step 5: Compiling the Model:

    model.compile(optimizer='adam',
                  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                  metrics=['accuracy'])

Step 6: Training the Model:

    history = model.fit(train_images, train_labels, epochs=10, 
                        validation_data=(test_images, test_labels))

Evaluating the Model:

Step 7: Evaluating the Model and Visualizing Results:

    test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)

    import matplotlib.pyplot as plt

    plt.plot(history.history['accuracy'], label='accuracy')
    plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
    plt.xlabel('Epoch')
    plt.ylabel('Accuracy')
    plt.ylim([0.5, 1])
    plt.legend(loc='lower right')
    plt.show()

Lab Guide: Image Recognition with TensorFlow and Keras

Objective

Prerequisites

Lab Outline

Resources