Fine tuning vs. full training vs. training from scratch

Introduction

Many methods have been proven to be effective in improving model quality, efficiency, and resource consumption in Deep Learning. The distinction between fine-tuning, full training, and training from scratch can help you decide which approach is right for your project. Then, we'll review them individually and see where and when to use them, using code snippets to illustrate their advantages and disadvantages.

Learning objectives:

• Understand the differences between fine-tuning, full training, and training from scratch in Deep Learning.
• Identify appropriate use cases for training a model from scratch.
• Recognize when to use comprehensive training on large, established data sets.
• Learn the advantages and disadvantages of each training approach.
• Gain practical knowledge through example code snippets for each training method.
• Evaluate the resource requirements and performance implications of each approach.
• Apply the appropriate training strategy for specific Deep Learning projects.

What is training from scratch?

It means building and training a new model on the fly using your data set. Starting with random initial weights and continuing through the entire training process.

Use cases

• Unique data: When the data set used is unique and very different from any current data set.
• Innovative architectures: While designing new model architectures or testing new methods.
• Investigation and development: This is used in academic research or for advanced applications where models based on all possible databases are insufficient.

Advantages

• Flexible: You can completely control the model architecture and training process to adapt them to the particularities of your data.
• Custom solutions: Regarding highly specialized tasks, such as those where there may be no pre-trained models available.

Example code

Here is an example that uses PyTorch to train a simple neural network from scratch:

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms

# Define a simple neural network
class SimpleNN(nn.Module):
    def __init__(self):
        super(SimpleNN, self).__init__()
        self.fc1 = nn.Linear(28*28, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 10)

    def forward(self, x):
        x = torch.flatten(x, 1)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# Load the dataset
transform = transforms.Compose((transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))))
train_dataset = datasets.MNIST(root="./data", train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)

# Initialize the model, loss function, and optimizer
model = SimpleNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Training loop
for epoch in range(10):
    for images, labels in train_loader:
        optimizer.zero_grad()
        output = model(images)
        loss = criterion(output, labels)
        loss.backward()
        optimizer.step()
    print(f"Epoch {epoch+1}, Loss: {loss.item()}")

What is Complete Training?

Full training generally refers to training a model from scratch but on a large, well-established data set. This approach is common for developing fundamental models such as VGG, ResNet or GPT.

Use cases

• Fundamental models: Training large models intended to be used as pre-trained models for other tasks.
• Comparative evaluation: Compare different architectures or techniques on standard data sets to establish benchmarks.
• Industrial applications: Creating robust and generalized models for widespread industrial use.

Advantages

• High performance: These models can achieve state-of-the-art performance on specific tasks. They often serve as the backbone of many applications and are optimized for specialized tasks.
• Standardization: Helps establish reference models. Models trained on large and diverse data sets can generalize well across various tasks and domains.

Disadvantages

• Requires resources: It requires a lot of time and computational power. Training models like ResNet or GPT-3 involve multiple GPUs or TPUs over several days or weeks.
• Required experience: Tuning hyperparameters and ensuring proper convergence requires in-depth knowledge. This includes understanding model architecture, data preprocessing, and optimization techniques.

Example code

Below is an example that uses TensorFlow to train a CNN on the CIFAR-10 dataset:

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

# Load the CIFAR-10 dataset
(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()

# Normalize the images
train_images, test_images = train_images / 255.0, test_images / 255.0

# Define a CNN model
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)
))

# Compile the model
model.compile(optimizer="adam",
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=('accuracy'))

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

What is fine tuning?

Use a pre-trained model and make minor modifications to make it suitable for a particular task. You generally freeze the first few layers and train the rest on your data set.

Use cases

• Transfer learning: Fine tuning is necessary if your data set is small or if you have limited hardware resources. Uses knowledge from pre-trained models.
• Domain adaptation: Convert a general model to work in a specialized domain (for example, medical imaging and sentiment analysis).

Benefits

• Efficiency: Consumes less computational power and time. Training from scratch would require more resources, but tuning can be done with fewer resources.
• Model performance: The model works well in many cases, even with little data. Pretrained layers learn general features that are useful for most tasks.

Cons

• Less flexibility: You don't fully control the initial layers of the model. It depends on the architecture and training of a pre-trained model.
• Risk of overfitting: Training a model to work with such a limited amount of data must be approached with caution to avoid overfitting the system. Overfitting can occur with fine tuning if the new data set is too small or too similar to the previously trained data.

Example code

Below is an example that uses Keras to tune a pre-trained VGG16 model on a custom data set:

import tensorflow as tf
from tensorflow.keras.applications import VGG16
from tensorflow.keras import layers, models
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Load the pre-trained VGG16 model and freeze its layers
base_model = VGG16(weights="imagenet", include_top=False, input_shape=(150, 150, 3))
for layer in base_model.layers:
    layer.trainable = False

# Add custom layers on top of the base model
model = models.Sequential((
    base_model,
    layers.Flatten(),
    layers.Dense(256, activation='relu'),
    layers.Dense(1, activation='sigmoid')
))

# Compile the model
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=('accuracy'))

# Load and preprocess the dataset
train_datagen = ImageDataGenerator(rescale=0.5)
train_generator = train_datagen.flow_from_directory(
    'path_to_train_data',
    target_size=(150, 150),
    batch_size=20,
    class_mode="binary"
)

# Fine-tune the model
history = model.fit(train_generator, epochs=10, steps_per_epoch=100)

Fine tuning vs. full training vs. training from scratch

Similarities Between Fine Tuning, Complete Training, and Training from Scratch

• Machine learning models: All three methods involve machine learning models for various tasks.
• Training process: Each method involves training a neural network, although data and initial conditions may vary.
• Improvement: All methods require optimization algorithms to minimize the loss function.
• Performance evaluation: All three methods require evaluating the performance of the model using metrics such as precision, loss, etc.

How to decide which one is best for you?

1. Data set size and quality:

• Training from scratch: It is best to have a large, unique data set that is significantly different from existing data sets.
• Complete training: This is ideal if you can access large, well-established data sets and the resources to train a model from scratch.
• Fine tuning: It is suitable for small data sets or for leveraging knowledge from a pre-trained model.

2. Available resources:

• Training from scratch: Requires significant computational resources and time.
• Complete training: It is resource intensive and often requires multiple GPUs/TPUs and considerable training time.
• Fine tuning: It requires fewer resources, can be done with limited hardware and in less time.

3. Project objectives:

• Training from scratch: This is for projects that need custom solutions and novel model architectures.
• Complete training: This is to create fundamental models that can be used as benchmarks or for generalized applications.
• Fine tuning: For domain-specific tasks where a pre-trained model can be adapted to improve performance.

4. Experience level:

• Training from scratch: Requires in-depth knowledge of machine learning, model architecture, and optimization techniques.
• Complete training: Requires experience in hyperparameter tuning, model architecture, and an extensive computational setup.
• Fine tuning: More accessible for professionals with intermediate knowledge, taking advantage of pre-trained models to achieve good performance with fewer resources.

Taking these factors into account, you can determine the most appropriate training method for your deep learning project.

Conclusion

Your specific case, data availability, computing resources, and target performance all influence whether you should perform fine tuning, full training, or training from scratch. Training from scratch is flexible but requires substantial resources and large data sets. Comprehensive training on established data sets is good for developing basic models and benchmarking. Fine tuning efficiently uses pre-trained models and fine-tunes them for particular tasks with limited data.

Knowing these differences, you can choose the right approach for your machine learning project that maximizes performance and resource utilization. Whether you're building a new model, comparing architectures, or modifying existing ones, the right training strategy will be critical to achieving your machine learning ambitions.

Frequent questions