Advanced example using TensorFlow with guide

We will create a deep neural network for image classification with the CIFAR-10 dataset. This example will demonstrate advanced techniques such as custom training loops, data augmentation, and learning rate scheduling.

This is the  tensorflow page if you want to get more information

https://www.tensorflow.org

Step-by-Step Guide:

1. Import Required Libraries:

import tensorflow as tf

from tensorflow.keras import layers, models, optimizers, losses

from tensorflow.keras.preprocessing.image import ImageDataGenerator

import numpy as np

2. Load and Preprocess Data:

# Load the CIFAR-10 dataset

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

# Normalize the images to the range [0, 1]

train_images, test_images = train_images / 255.0, test_images / 255.0

# Convert labels to one-hot encoding

train_labels = tf.keras.utils.to_categorical(train_labels, 10)

test_labels = tf.keras.utils.to_categorical(test_labels, 10)

3. Create a Data Augmentation Generator:

datagen = ImageDataGenerator(

    rotation_range=15,

    width_shift_range=0.1,

    height_shift_range=0.1,

    horizontal_flip=True

)

datagen.fit(train_images)

4. Build the Model:

def build_model():

    model = models.Sequential()

    model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))

    model.add(layers.BatchNormalization())

    model.add(layers.Conv2D(32, (3, 3), activation='relu'))

    model.add(layers.BatchNormalization())

    model.add(layers.MaxPooling2D((2, 2)))

    model.add(layers.Dropout(0.2))

    model.add(layers.Conv2D(64, (3, 3), activation='relu'))

    model.add(layers.BatchNormalization())

    model.add(layers.Conv2D(64, (3, 3), activation='relu'))

    model.add(layers.BatchNormalization())

    model.add(layers.MaxPooling2D((2, 2)))

    model.add(layers.Dropout(0.3))

    model.add(layers.Conv2D(128, (3, 3), activation='relu'))

    model.add(layers.BatchNormalization())

    model.add(layers.Conv2D(128, (3, 3), activation='relu'))

    model.add(layers.BatchNormalization())

    model.add(layers.MaxPooling2D((2, 2)))

    model.add(layers.Dropout(0.4))

    model.add(layers.Flatten())

    model.add(layers.Dense(128, activation='relu'))

    model.add(layers.BatchNormalization())

    model.add(layers.Dropout(0.5))

    model.add(layers.Dense(10, activation='softmax'))

    return model

5. Custom Learning Rate Scheduler:

def lr_schedule(epoch):

    lr = 1e-3

    if epoch > 75:

        lr *= 0.5e-3

    elif epoch > 50:

        lr *= 1e-3

    elif epoch > 25:

        lr *= 1e-2

    return lr

6. Compile and Train the Model with Custom Training Loop:

model = build_model()

model.compile(optimizer=optimizers.Adam(learning_rate=lr_schedule(0)),

              loss=losses.CategoricalCrossentropy(),

              metrics=['accuracy'])

# Custom training loop

epochs = 100

batch_size = 64

steps_per_epoch = train_images.shape[0] // batch_size

for epoch in range(epochs):

    print(f'Epoch {epoch + 1}/{epochs}')

    lr = lr_schedule(epoch)

    tf.keras.backend.set_value(model.optimizer.lr, lr)

    print(f'Learning rate: {lr}')

    # Train the model using the data generator

    model.fit(datagen.flow(train_images, train_labels, batch_size=batch_size),

              steps_per_epoch=steps_per_epoch, epochs=1, verbose=1)

    # Evaluate the model

    loss, accuracy = model.evaluate(test_images, test_labels, verbose=0)

    print(f'Test loss: {loss}, Test accuracy: {accuracy}')

Explanation:

1. Data Loading and Preprocessing:

   - Load the CIFAR-10 dataset.

   - Normalize the images.

   - Convert labels to one-hot encoding.

2. Data Augmentation:

   - Use ImageDataGenerator for real-time data augmentation.

3. Model Building:

   - Build a deep convolutional neural network with batch normalization and dropout layers to improve regularization.

4. Learning Rate Scheduling:

   - Implement a custom learning rate scheduler to adjust the learning rate during training.

5. Custom Training Loop:

   - Train the model using a custom training loop, which allows for more control over the training process, such as updating the learning rate at each epoch and evaluating the model on the test set.