h4cker/ai_research/labs/tf_keras.md

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

1. Basic understanding of Python programming.
2. Familiarity with machine learning concepts.
3. 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()

Resources

1. TensorFlow Documentation
2. Keras Documentation