What is the best way to choose an ANN architecture?

I have a rule of thumb: CNNs for images, Transformers for sequence problems, FF neural nets on top of embeddings, GNNs for naturally graph-structured data. These aren’t hard rules though. CNNs have been great and interpretable (if not deep but wide) for biological sequence modeling problems. Experimentation is key. What’s more important is ensuring you have a path to delivering value to your collaborators.

For image recognition, you should use CNN for simple tasks and consider more advanced architectures like VGG, ResNet or EfficientNet for complex jobs. Incase of sequential data such as time series or speech recognition, RNNs are suitable and LSTM or GRU architectures handle dependencies well. For tabular data use Feedforward Neural Networks (FNNs) or basic Multilayer Perceptrons (MLPs). Adding multiple hidden layers enhances the network's ability to capture intricate relationships. In natural language processing (NLP) tasks, Transformer architectures like BERT or GPT are effective, with adjustments in model size based on data volume and task complexity.Additionally, for anomaly detection, consider Autoencoders.

Selecting an Artificial Neural Network (ANN) architecture involves considering factors such as the nature of the data, the complexity of the task, and computational resources available. Conducting experiments with various architectures, optimizing hyperparameters through techniques like grid search, and leveraging domain knowledge contribute to identifying the most suitable ANN architecture for a specific application.

Choosing an architecture involves understanding the data and problem, which leads to the use of CNNs, RNNs, or simpler networks. Input and output data dictate layer design, with activation functions tailored to the requirements. Hardware and performance goals influence model complexity, balancing accuracy against resource demands. The key is avoiding overfitting to ensure the generalization of the black box models; this involves regular hyperparameter tuning and architecture adjustments. The "No Free Lunch" theorem, states we start with a simple model, progressively refining by experimentation and avoiding overfitting. The model should perform well on both training and unseen data, emphasizing continuous improvement and adaptability.

Designing ANNs is part science, part art - balancing model complexity, training time and performance. Perfect your ANN blueprint with these evidence-based tips: - Start simple with few layers/neurons, increasing complexity until overfitting - Prefer wider layers over deeper stacks to minimize vanishing gradients - Log and analyze validation accuracy at each iteration to identify the best fit - Use search techniques like evolutionary algorithms to efficiently explore architectures - Choose Rectified Linear Unit (ReLU) activation unless evidence supports alternatives - Regularize via dropout or early stopping to prevent overfitting Don't get lost in infinite design options, let scientific principles guide you!

Choosing the appropriate architecture for an ANN depends on the nature and structure of the input and output data. For example, CNNs are typically used for image data. RNNs or Transformer-based models are more suited for sequential data like text. RNNs/LSTM for time-series data. The size and shape of the input data will determine the input layer's configuration. In a neural network designed for classification tasks, the output layer typically has a number of nodes corresponding to the number of classes we want to classify the input into. The complexity and size of input data influence the network's layers and neurons. A more complex task or larger input data might require a deeper network with more layers.

CNNs with deeper layers or architectures are designed for handling large-scale data (e.g., high-resolution images). RNNs or transformers that can handle sequences of different lengths (e.g., text of varying lengths).

In general, the wisdom is, just for flat feature sets use vanilla NN, for images, use CNN (can also be used for audio classification), and use transformers for NLP. It also depends on your business needs and on other questions. For example, you need two different kinds of output. In that case, do you want to build a single model that outputs two values, or do you want to create separate models that produce two different outputs and so on (i.e., multi-task learning)? Can you also benefit from a zero-sum game design to improve your performance (i.e., GAN), or can you introduce some form of unsupervised learning? There are also a few rules of thumbs/scaling laws that you also want to investigate

Selecting the right Artificial Neural Network (ANN) architecture depends on the specific task and dataset. Consider factors such as the complexity of the problem, available data size, and computational resources. For image recognition, convolutional neural networks (CNNs) excel, while recurrent neural networks (RNNs) are suitable for sequence data. Transformer architectures, like BERT, are effective for natural language processing. Experiment with architectures, evaluate performance on validation data, and fine-tune based on results. Tailoring the ANN to your specific needs ensures optimal performance.

Input and output size of the network depends on the available datasets input and required labels of output. In general neurons of the input layer are the number of input data. And neurons in the output layer are the number of outputs associated with input data. In case we have a binary classification problem then one neuron in the output layer is enough. But in case the problem has more than 2 outputs then the number of neurons is equal to the number of outputs. For binary classification there is no required hidden layer. One can plot the data and check the visual graph. Depending on the number of lines required to separate the classes in the visual graph one can choose the number of hidden layers.

It is crucial to find the right balance between the number of parameters and operations in your network (model complexity) and the amount and diversity of your data (data complexity). Matching the model complexity to the data complexity enables the network to learn patterns and generalize effectively. If the model is too simple, it may underfit the data and miss important features, while if it is overly complex, it may overfit the data and memorize noise.

Aligning an ANN's complexity with the input data's complexity is a crucial aspect of model design. A simple model on complex data will likely lead to underfitting as the model might not have enough parameters to capture the variations in different conditions present in the data. A Complex model on simple data will likely lead to overfitting as a complex model might start memorizing specific features (including noise) rather than learning generalizable features to distinguish between various conditions. The ideal approach is to maintain the balanced complexity by choosing a model that is complex enough to learn significant features but not so complex that it memorizes each feature.

Depending on the Complexity of the task one can choose the shallow or deep network. If we have a smaller size dataset or simple task than a complex model i.e deep network cannot be used otherwise it leads to overfitting. Hence one should use a simple network with less parameters so less likely to fit noise and overfitting can be avoided. Larger size data required complex models with more parameters to identify the patterns and get better accuracy. Activation function plays important role in considering layers performance. It adds non linearity to understand the patterns and gives generalization.

1. Model Complexity: Relates to the network's parameters and operations. High complexity increases learning capacity but may lead to overfitting. 2. Data Complexity: Concerns the dataset's size, diversity, and complexity. Complex datasets justify more intricate models. 3. Matching Model to Data: Involves aligning the model's capacity with the dataset's complexity to avoid underfitting with simple models and overfitting with complex ones. 4. Measuring Model Complexity: The VC dimension is used to assess a model's capacity, indicating the maximum points it can correctly classify. 5. Practical Considerations: Focus on validation accuracy and learning curves. Use cross-validation, early stopping, and dropout for maintaining model balance.

Consider ensemble methods like bagging, boosting, or stacking. These approaches combine multiple models to mitigate overfitting and improve generalization by leveraging diverse architectures.

Optimizers are crucial tools in training neural networks. Optimization involves adjusting network parameters such as weights and learning rates to minimize the loss function. The loss function quantifies the discrepancy between the network’s prediction and actual value for given input data, and optimizers aim to diminish this difference by fine-tuning the weights and biases. There are a variety of optimizers, each offering unique ways of adjusting parameters, which can significantly influence the speed and efficiency of training. For instance, SGD makes small, consistent adjustments. In contrast, optimizers like Adam employ advanced techniques such as momentum or adaptive learning rates to accelerate training and evade local minima.

Taming the Learning Beast: Dropout: The Pruning Master: This technique strategically removes neurons during training, forcing the network to rely on others. This prevents codependency and encourages robust feature learning L1/L2: The Lasso and Ridge of the Network: These techniques penalize large weight values, keeping the network lean and efficient. L1, the ruthless editor, eliminates unnecessary weights. L2, the gentle guide, shrinks weights, making the network less vulnerable to noise Optimizing the Journey: Choosing the right optimizer, like a skilled guide, navigates the learning landscape efficiently. Adam and RMSProp offer stability and smooth updates, ensuring the journey towards performance peaks is both efficient and effective

In order to prevent overfitting, you can employ several techniques: - Dropout: By randomly deactivating connections between neurons in the network, you can make it more difficult for the model to excessively memorize training samples. - Regularization: Techniques such as L1 or L2 regularization can adjust the weights of the neural network, thereby enhancing its performance. - Early Stopping: Halting the training process when your model ceases to show improvement. - Data Augmentation: Generating new training data by modifying the original input data. This technique is applicable to both structured and unstructured data. These are some techniques you can utilize to minimize overfitting and maximize predictive performance.

The incorporation of regularization and optimization techniques is crucial for enhancing training and testing performance. At my former experience at Bloomberg AI, implementing techniques like dropout and weight decay played a pivotal role in improving the robustness of models for self-service ML model remediation. This step ensures the network doesn't overfit or underfit the data, contributing to better generalization.

Regularization methods, such as dropout and weight decay, serve to mitigate overfitting by introducing constraints or penalties to the network. Dropout randomly drops out nodes during training, while weight decay shrinks weights towards zero. Optimization techniques, like gradient descent and Adam, play a crucial role in finding optimal parameter values that minimize the loss function. Gradient descent updates weights based on the loss gradient, while Adam adapts the learning rate and momentum based on the historical gradients. This step underscores the significance of fine-tuning and enhancing the robustness of the ANN for better generalization.

I barely see methods like cross validation being used in deep learning. It’s already expensive enough to train a single model. Training multiple models is often luxury. Outside of kaggle competitions I rarely see that. What’s from my point of view more important and often neglected is having a good Validation and test set. It’s really crucial to make sure you test your model well. Use the validation set too tweak the model architecture and hyper parameters. Then use the test set for a final check.

A notable evaluation technique for ANNs is k-fold cross-validation. In this technique, instead of statically dividing your input data into two or three subsamples, you iteratively and dynamically utilize the data. For each of the x iterations, you divide your data into k-folds—segments of k number—and utilize some for training and some for testing. This approach ensures dynamic utilization of different data segments for both training and testing, preventing the repetitive use of the same data for training or validation.

A big one for me is just manually checking a few results. Too often do I see people get lost in F1 scores and accuracy matrices, when the problem is right in front of them in the output of the model (or even in visualization of intermediate layers). That said, it’s recommendable to add many evals by default and keep a history with every training run, so new problems pop out quickly. I disagree with this division into ?steps. Your architecture is not set in stone, and it SHOULD be changed even after evals were selected. All these are important aspects but they should be looked at holistically.

You can tune the hyperparameters of an ANN model. Yes you heard me right. Try different number of hidden layers, try varying number of hidden nodes, try regularisation, and everything suggested in the article. To find a starting point, i usually reference literature. For example, if image recognition usually works best with a certain ANN structure. Start with that. Here is a snippet of code for tuning a Keras model: params = {'activation':['relu'], 'conv_layers': [0,1,2], 'dense_layers': [1,2,3], 'batch_size':[8]} grid_search = GridSearchCV(model, param_grid = params, cv = 3)

Nowadays, OpenAI's GPT-4 / 3.5 has turned into another source for benchmarking. If you're working on text related tasks, you may want to use these APIs to test the same dataset, so that you could get a sense about the SOTA performance and where the gaps are.

The process of choosing an optimal Artificial Neural Network (ANN) architecture involves various considerations such as network depth, width, and activation functions. Reinforcement Learning (RL) offers a powerful approach to automate this selection through methods like Proximal Policy Optimization (PPO) or Deep Deterministic Policy Gradients (DDPG). RL-based Neural Architecture Search (NAS) involves representing ANN architectures as states, defining actions for architectural modifications, and training an RL agent to explore and exploit this space. By formulating a reward function based on desired metrics, RL guides the search for architectures that optimize specific objectives.

Perhaps the most important point missing in this article is the productionization consideration for the model. While it is important to aim for a model that performs best in terms of accuracy or other scoring metrics, it is very import to make sure the model's latency fits the requirements. If your model is doing online predictions, it needs to give a prediction in a few milliseconds to maintain a good user experience. My current work at Nvidia focusses exactly on this - building a toolkit to optimize your models for the best latency and accuracy trade-off!

Choosing an?appropriate?tool?for?a?task?is?similar?to?selecting?an?ANN?design. Just?like?with?a?simple?task,?sometimes?all?you?need?is?a?little,?basic?network.Use?a?larger,?more?sophisticated?architecture?for?more?complex?problems,?this?is?similar?to?utilising?specialised?equipment?for?major?construction?projects. Think?about?things?like the?quantity?of?data?you?have,?the?complexity?of?patterns?you?are?working?with,?and?the?available?computing?power. Finding?the?right?balance?is?key, it?shouldn't?be?too?complicated?to?tackle?the?task?at?hand?or?too?easy?to?make?things?unmanageable. Just?as?you?wouldn't?hang?a?picture?with?a?sledgehammer,?pick?an?ANN?architecture?that?is?appropriate?for?the?particulars?of?your?issue.

Read existing literature. Odds are others have already tried to solve the same problem or one similar enough to give you a good starting point. Having a good grasp of the history of solutions and their evolution can help you avoid costly mistakes. It will also give you a good idea about what hasn't been tried yet and might be worth investigating.

Don't be afraid to peek at what others have done! Research similar tasks and use existing architectures as a starting point. And if you get stuck, don't hesitate to ask for help from the amazing online communities out there!