Week 10 - Patterns that Modify Training: Transfer Learning and Hyperparameter Tuning Patterns. Resilience Patterns: Batch Serving

Lecture recording (Nov 12, 2024) here.

Lab recording (Nov 14, 2024) here.

Introduction

This week we will look at two patterns that modify training: the transfer learning pattern and the hyperparameter tuning pattern. The transfer learning pattern takes parts of a previously trained model, freezes the weights, and uses these nontrainable layers in a new model that solves a similar problem. This is needed when there is a lack of large datasets that are needed to train complex machine learning models. The hyperparameter tuning pattern inserts the training loop into an optimization method to find the optimal set of model hyperparameters.

We will also look at the first of resilience patterns: batch serving. The batch serving resilience design pattern focuses on ensuring the reliability and fault tolerance of batch processing systems. This pattern is particularly important in scenarios where large volumes of data are processed in scheduled batches, and any failure can result in significant delays or data loss.

Videos

Transfer Learning Design Patterns	Deep Learning Design Patterns - Jr Data Scientist - Part 7 - Transfer Learning
	Transfer Learning
The Hyperparameter Tuning Pattern	Deep Learning Design Patterns - Jr Data Scientist - Part 6 - Hyperparameter Tuning
	Hyperparameter Tuning in Practice

Assignment(s)

Assignment 5 - Multimodal Input: An Autonomous Driving System

The Transfer Learning Design Pattern

Transfer learning, used in machine learning, is the reuse of a pre-trained model on a new problem. In transfer learning, a machine exploits the knowledge gained from a previous task to improve generalization about another. For example, in training a classifier to predict whether an image contains food, you could use the knowledge it gained during training to recognize drinks. For more information see What Is Transfer Learning?.

The Rationale

The rationale behind the transfer learning design pattern stems from the observation that deep learning models trained on large-scale datasets can learn generic features that are useful for a wide range of tasks. Transfer learning leverages this idea by reusing pre-trained models as a starting point for new tasks.

The UML

Here is the UML diagram for the transfer learning pattern:

  +---------------------------------+
  |           Pre-trained Model     |
  +---------------------------------+
  |                                 |
  | - Trained on large-scale dataset|
  | - Captures generic features     |
  +---------------------------------+
               ^
               |
  +---------------------------------+
  |        New Task-Specific Model  |
  +---------------------------------+
  |                                 |
  | - Reuses pre-trained model      |
  | - Freezes pre-trained layers    |
  | - Adds new task-specific layers |
  | - Fine-tunes the model          |
  +---------------------------------+

Here are the components of the transfer learning design pattern:

Pre-trained Model: This component represents a deep learning model that has been pre-trained on a large-scale dataset (e.g., ImageNet). It captures generic features and knowledge learned from the original task. The pre-trained model serves as a starting point for the transfer learning process.
New Task-Specific Model: This component represents the model that is created for the new task using transfer learning. It consists of the pre-trained model as the base, with its layers frozen to preserve the learned features. New task-specific layers are added on top of the pre-trained layers. These additional layers are designed specifically for the new task and are trainable. The model is then fine-tuned on the new task-specific data to adapt the knowledge from the pre-trained layers to the target domain.

Code Example - Transfer Learning

In this code, we have two classes: PretrainedModel and NewTaskModel. The PretrainedModel class represents the pre-trained model, and it has methods to load the pre-trained model weights and extract features using the pre-trained layers. The NewTaskModel class represents the model for the new task. It has methods to add new task-specific layers and perform fine-tuning on the new task-specific data. In the main() function, we create an instance of PretrainedModel and load the pre-trained model weights. Then, we create an instance of NewTaskModel. The transfer learning process involves extracting features from the pre-trained model using pretrainedModel.extractFeatures(), adding task-specific layers using newTaskModel.addTaskSpecificLayers(), and fine-tuning the model on the new task-specific data using newTaskModel.fineTune(). Finally, you can use the trained new task-specific model for inference or any other desired tasks.

Note that this code is a basic representation to illustrate the concept of transfer learning in C++. In practice, you would need to adapt it to the specific deep learning framework or library you are using and incorporate additional functionalities as needed:
C++: TransferLearning.cpp.
C#: TransferLearning.cs.
Java: TransferLearning.java.
Python: TransferLearning.py.

Common Usage

The transfer learning design pattern is commonly used in various scenarios to leverage the knowledge gained from pre-trained models and apply it to new tasks or domains. The following are some common usages of the transfer learning design pattern:

Image Classification: Transfer learning is extensively used in image classification tasks. Pre-trained models trained on large-scale datasets, such as ImageNet, are used as a starting point. The learned features from the pre-trained model are extracted and used as input to train a new classifier for a specific set of classes or a different dataset.
Object Detection: Transfer learning can be applied to object detection tasks. Pre-trained models, such as Faster R-CNN or SSD, are used as feature extractors. The pre-trained model's convolutional layers are used to extract features from input images, and then additional layers are added to perform object detection on specific classes or datasets.
Natural Language Processing (NLP): In NLP tasks, transfer learning can be applied using pre-trained models like BERT or GPT. The pre-trained models are fine-tuned on specific NLP tasks, such as sentiment analysis, named entity recognition, or machine translation. The pre-trained models' language understanding capabilities are leveraged, and additional layers are added for task-specific fine-tuning.
Speech Recognition: Transfer learning can be used in speech recognition tasks. Pre-trained models, such as DeepSpeech or Listen, Attend and Spell (LAS), can be used to extract high-level features from audio data. These features are then used to train a new classifier or decoder for specific speech recognition tasks or datasets.
Anomaly Detection: Transfer learning can be applied to anomaly detection tasks, where pre-trained models are used to capture the normal behavior of a system or dataset. The pre-trained models' learned representations are used to detect deviations or anomalies from the normal behavior in new data.
Recommendation Systems: Transfer learning can be used in recommendation systems to leverage knowledge from pre-trained models. For example, pre-trained models trained on large-scale user behavior data can be used to initialize embeddings or feature representations in recommendation models, allowing them to benefit from the learned user preferences and patterns.

Code Problem - Sentiment Analysis

In this example, we have a PretrainedWordEmbeddings class that represents pre-trained word embeddings. It has methods to load the pre-trained embeddings from a file and retrieve the word embeddings for specific words. The SentimentAnalysisModel class represents the sentiment analysis model. It has a dependency on the PretrainedWordEmbeddings class. The model loads the pre-trained word embeddings and uses them for training and prediction. In the main() function, we create an instance of SentimentAnalysisModel, load the pre-trained word embeddings, and train the model using transfer learning by calling trainModel() with the training data file path. Finally, we use the trained model for sentiment prediction by calling predictSentiment() with an input text. The predicted sentiment class is returned and printed to the console.
PretrainedWordEmbeddings.h,
SentimentAnalysisModel.h,
SentimentAnalysisModel.cpp,
Sentiment.cpp.

Code Problem - Image Classification

In the following example, a pre-trained model is used to help with image classification.
PretrainedModel.h,
TaskSpecificModel.h,
ImageClassificationModel.h,
ImageClassificationMain.cpp.

The Hyperparameter Tuning Design Pattern

A Machine Learning model is defined as a mathematical model with a number of parameters that need to be learned from the data. By training a model with existing data, we are able to fit the model parameters. However, there is another kind of parameter, known as Hyperparameters, that cannot be directly learned from the regular training process. They are usually fixed before the actual training process begins. These parameters express important properties of the model such as its complexity or how fast it should learn. For more information see Hyperparameter Tuning - Brief Theory and What you won't find in the HandBook.

The Rationale

The rationale behind the Hyperparameter Tuning Design Pattern is to systematically explore different combinations of hyperparameter values to identify the configuration that produces the best results. By tuning the hyperparameters, we aim to improve the model's performance, generalization, ability, and robustness.

The UML

Here is the UML diagram for the hyperparameter tuning design pattern:

  ----------------------------------
  |    Hyperparameter Tuning       |
  |    Design Pattern              |
  ----------------------------------
  |                                |
  | + Define hyperparameters       |
  | + Define search space          |
  | + Define evaluation metric     |
  | + Select tuning strategy       |
  | + Train and evaluate models    |
  | + Select best hyperparameters  |
  | + Test the model               |
  ----------------------------------

The hyperparameter tuning design pattern usually involves the following steps:

Define the hyperparameters: Identify the hyperparameters that need to be tuned. Examples of common hyperparameters include learning rate, batch size, regularization strength, number of hidden units, and kernel size.
Define the search space: Determine the range or set of values for each hyperparameter that will be considered during the tuning process. This search space can be defined manually or automatically using techniques like grid search, random search, or Bayesian optimization.
Define the evaluation metric: Select an appropriate metric to evaluate the performance of the model. This could be accuracy, precision, recall, F1 score, or any other relevant metric depending on the problem domain.
Select a tuning strategy: Choose a strategy to explore the hyperparameter search space. Some common strategies include grid search, random search, and more advanced methods like Bayesian optimization, genetic algorithms, or gradient-based optimization.
Train and evaluate models: Train and evaluate a series of models with different hyperparameter configurations using cross-validation or a separate validation set. The evaluation metric defined earlier is used to compare and rank the performance of different models.
Select the best hyperparameters: Identify the hyperparameter configuration that achieved the best performance according to the evaluation metric. This configuration is typically selected as the final set of hyperparameters for the model.
Test the model: Finally, evaluate the model's performance on an independent test set using the selected hyperparameters to estimate its real-world performance.

Code Example - Hyperparameter Tuning

In this program, we demonstrate each step of the Hyperparameter Tuning Design Pattern.
We define the hyperparameters (learningRate, numHiddenUnits, regularizationStrength) in Step 1.
We define the search space for each hyperparameter (learningRates, hiddenUnits, regularizationStrengths) in Step 2.
The evaluation metric is the mean squared error (MSE) in this example, defined as bestMetric in Step 3.
We use nested loops to iterate over the search space of each hyperparameter in Step 4, representing the tuning strategy (in this case, grid search).
Within the loops, we train and evaluate the model using the current hyperparameters in Step 5. The evaluation metric (MSE) is calculated by the trainAndEvaluateModel function.
We select the best hyperparameters based on the metric value in Step 6. If the current metric is better than the previous best, we update the best hyperparameters and metric.
Finally, in Step 7, we print the best hyperparameters and the corresponding metric.
C++: Tuning.cpp.
C#: Tuning.cs.
Java: Tuning.java.
Python: Tuning.py.

Common Usage

The hyperparameter tuning pattern is widely used in the software industry for optimizing the performance of machine learning models and algorithms. The following are some common usages of the hyperparameter tuning pattern:

Model Selection: Hyperparameter tuning is used to select the best set of hyperparameters for a given machine learning model. By systematically exploring different combinations of hyperparameters, practitioners can identify the configuration that yields the best performance on a validation set or through cross-validation.
Algorithm Optimization: Hyperparameter tuning is employed to optimize the parameters of machine learning algorithms. For example, in gradient boosting algorithms like XGBoost or LightGBM, hyperparameters such as the learning rate, number of estimators, and maximum depth of trees can significantly impact the algorithm's performance. Tuning these hyperparameters helps achieve the best trade-off between accuracy and computational efficiency.
Neural Network Training: Hyperparameter tuning is crucial for training deep neural networks. Parameters such as learning rate, batch size, optimizer settings, and regularization techniques play a critical role in achieving good training convergence and preventing overfitting. Tuning these hyperparameters helps improve the model's generalization capabilities.
Feature Selection: Hyperparameter tuning is used to optimize the selection of features in machine learning pipelines. By exploring different subsets of features or applying feature selection algorithms, practitioners can identify the most relevant features that contribute to the model's performance.
Reinforcement Learning: Hyperparameter tuning is employed to optimize the hyperparameters of reinforcement learning algorithms, such as exploration-exploitation trade-offs, learning rates, discount factors, and exploration policies. Tuning these hyperparameters can lead to better convergence and more efficient learning in reinforcement learning tasks.
Time Series Forecasting: Hyperparameter tuning is used to optimize hyperparameters in time series forecasting models, such as the number of lagged variables, window sizes, and regularization parameters. Tuning these hyperparameters can improve the model's bility to capture temporal dependencies and make accurate predictions.

Code Problem - F1 Score

This code demonstrates the generation of the F1 score using the hyperparameter tuning design pattern. Here's a step-by-step explanation of the code:

The generateDataset function generates a synthetic binary classification dataset. It randomly generates samples with two features x and y and assigns labels based on whether x + y >= 0 or not.
The trainAndEvaluateSVM function trains and evaluates an SVM model using the given dataset and hyperparameters C and gamma. In this example, the function calculates the F1 score as the evaluation metric based on accuracy. It counts the true positives, false positives, and false negatives by comparing the predicted labels with the actual labels of the dataset.
The main function is the entry point of the program. It follows the hyperparameter tuning design pattern using the grid search strategy.
- Step 1: Define the hyperparameters C and gamma.
- Step 2: Define the search space for hyperparameters using vectors Cs and gammas.
- Step 3: Define the evaluation metric as the best F1 score found so far, initialized to the lowest possible value, and variables to store the best hyperparameters.
- Step 4: Select the tuning strategy, which is a nested loop for grid search. It iterates over all combinations of C and gamma values.
- Step 5: Train and evaluate SVM models using the current hyperparameters from the search space. It calls the trainAndEvaluateSVM function and passes the generated dataset along with the current C and gamma values.
- Step 6: Select the best hyperparameters based on the F1 score. If the current F1 score is higher than the best F1 score found so far, update the best F1 score and store the corresponding C and gamma values.
- Step 7: Print the best hyperparameters (C and gamma) and the best F1 score.

The code performs hyperparameter tuning by exhaustively searching the grid of hyperparameters and selecting the combination that yields the highest F1 score.
F1Score.cpp.

Code Example - Support Vector Machine Classification

Below is a complex example of the Hyperparameter Tuning Design Pattern using a hypothetical scenario of a support vector machine (SVM) for classification:
MLModel.h,
SVMClassifier.h,
HyperparameterTuningStrategy.h,
RandomSearchStrategy.h,
GridSearchStrategy.h,
HyperparameterTuningContext.h,
SVMMain.cpp.

The Batch Serving Design Pattern

The batch serving design pattern focuses on the efficient and reliable processing of large volumes of data in scheduled batches, rather than in real-time. This pattern is commonly used in data processing pipelines for tasks such as ETL (Extract, Transform, Load), data warehousing, and offline machine learning model training.

The Rationale

The batch serving design pattern enables efficient, scalable processing of large data volumes by scheduling and distributing workloads across multiple nodes, optimizing resource utilization and minimizing costs. It enhances reliability and fault tolerance through robust error handling mechanisms, ensuring data consistency and integrity. Additionally, batch processing simplifies data pipeline management, testing, and debugging compared to real-time systems. It is particularly suited for use cases like ETL processes, analytics, reporting, and machine learning model training.

The UML

The following is a very primitive diagram of the batch serving design pattern.

  +-----------------------------------------------+
  |                    Data Source                |
  +-----------------------------------------------+
                     |
                     v
  +-----------------------------------------------+
  |                 Data Ingestion                |
  |  - Collect data from various sources          |
  |  - Store in centralized repository            |
  +-----------------------------------------------+
                     |
                     v
  +-----------------------------------------------+
  |          Job Scheduler (e.g., Airflow)        |
  |  - Manage and orchestrate batch jobs          |
  |  - Schedule jobs at regular intervals         |
  +-----------------------------------------------+
                     |
                     v
  +-----------------------------------------------+
  |         Batch Processing Framework            |
  |        (e.g., Hadoop, Spark)                  |
  |  - Distribute data processing                 |
  |  - Ensure fault tolerance and scalability     |
  +-----------------------------------------------+
                     |
                     v
  +-----------------------------------------------+
  |            Data Transformation                |
  |  - Clean, normalize, and aggregate data       |
  |  - Apply business logic                       |
  +-----------------------------------------------+
                     |
                     v
  +-----------------------------------------------+
  |               Output Storage                  |
  |  - Store processed data for consumption       |
  |  - Use suitable data stores                   |
  +-----------------------------------------------+
                     |
                     v
  +-----------------------------------------------+
  |      Monitoring and Logging                   |
  |  - Track job performance                      |
  |  - Log execution details and errors           |
  +-----------------------------------------------+
                     |
                     v
  +-----------------------------------------------+
  |      Fault Tolerance and Recovery             |
  |  - Implement retry logic                      |
  |  - Use checkpointing                          |
  |  - Ensure idempotent operations               |
  +-----------------------------------------------+

Code Example - Batch Serving

Here is a simple example to demonstrate the batch serving design pattern. In this example, we'll simulate a scenario where we process a batch of numbers by squaring each number in the batch.
C++: BatchServing.cpp.
C#: BatchServing.cs.
Java: BatchServing.java.
Python: BatchServing.py.

Common Usage

The batch serving design pattern is commonly used in various domains and applications where processing multiple requests or tasks in groups (batches) can significantly improve efficiency, reduce latency, and optimize resource utilization. Here are some common areas where this pattern is employed:

Data Processing and ETL (Extract, Transform, Load) Pipelines: Used in data warehousing and big data processing to handle large volumes of data efficiently. Batches of data are extracted from sources, transformed, and loaded into storage systems.
Machine Learning and AI: Training machine learning models often involves processing data in batches to improve performance and convergence. Inference and prediction tasks can also benefit from batch processing, where predictions for multiple inputs are made in one go.
Database Operations: Bulk inserts, updates, and deletes in databases can be optimized by batching operations to reduce the number of transactions and improve throughput. Batch queries can also be used to retrieve large datasets more efficiently.
Network Communications: Reduces the overhead of network latency by sending and receiving data in batches rather than individually. Common in APIs, message queues, and other client-server communications.
File Processing: Large files can be processed in smaller chunks or batches to manage memory usage and improve performance. Common in log file analysis, video processing, and other file-based operations.
Financial Transactions: Batch processing is used for handling large numbers of transactions, such as in banking and stock trading systems. Helps in reducing the processing time and improving reliability.
Email and Notification Systems: Sending emails or notifications in batches can help manage load and avoid spamming servers. Often used in marketing campaigns and alert systems.
Image and Video Processing: Tasks such as resizing, filtering, and encoding can be performed on batches of images or video frames. Improves efficiency in multimedia applications.
Distributed Systems and Cloud Computing: Batch processing frameworks like Apache Hadoop, Apache Spark, and Google Cloud Dataflow are designed to handle large-scale data processing tasks. Commonly used for data analytics, batch processing jobs, and ETL processes.
Web Applications: Batch processing is used for tasks such as data aggregation, reporting, and background jobs. Frameworks like Celery (Python), Sidekiq (Ruby), and others facilitate batch job processing.

Code Problem - Batch Prediction

This example perform batch predictions using a linear regression model: BatchPrediction.cpp.
data is defined as a constant vector of vectors of doubles. This represents our dataset directly in the code.
The LinearRegression class represents a simple linear regression model.
The model is initialized with a vector of coefficients and an intercept.
The predict() method makes a prediction for a single feature vector.
The batchPredict() method makes predictions for a batch of feature vectors.

Microchip Route and Place (out of scope)

For a discussion of machine learning applied to the very difficult problem of routing and placing digital electronic components on a micro-chip, see Route and Place.