TensorFlow Image Classification Tutorial

Introduction

Welcome to our TensorFlow Image Classification Tutorial! In this hands-on guide, you'll learn how to build and train a convolutional neural network (CNN) for image classification using the CIFAR-10 dataset. This tutorial will walk you through the process of creating a deep learning model that can recognize and categorize images into predefined classes.

Prerequisites

Basic understanding of Python programming
Familiarity with neural networks and deep learning concepts
TensorFlow 2.x installed on your system
Jupyter Notebook or Google Colab for running the code

Tutorial

Step 1: Load and Preprocess the Data

We'll start by loading the CIFAR-10 dataset and preprocessing the images:


import tensorflow as tf
from tensorflow.keras.datasets import cifar10

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

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

Step 2: Build the CNN Model

Next, we'll create our CNN model using Keras:


from tensorflow.keras import layers, models

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

Step 3: Compile the Model

Now, let's compile our model with an optimizer, loss function, and metrics:


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

Step 4: Train the Model

It's time to train our model on the training data:


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

Step 5: Evaluate the Model

Finally, let's evaluate our model's performance on the test set:


test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
print(f"\nTest accuracy: {test_acc}")

Exercises

To deepen your understanding, try these exercises:

Adjust the model architecture (e.g., add more layers or change the number of filters).
Experiment with different optimization algorithms (e.g., SGD, RMSprop).
Implement data augmentation to improve model performance.
Use transfer learning with a pre-trained model like VGG16 or ResNet.
Visualize the activations of different layers to understand what the model is learning.