Neural network: Architecture search
Determining the optimal number of hidden layers and neurons in a neural network
- is a process called architecture search,
and there are several approaches to finding the optimal architecture
for a given problem.
Here are some common methods
Rule of thumb: There are some general guidelines
for the number of hidden layers and neurons,
such as starting with a single hidden layer containing
the average of the number of input and output neurons,
or using a number of hidden neurons that is
between the number of input and output neurons.
However, these guidelines are only starting points,
and the optimal number of hidden layers and neurons
will depend on the specific problem and dataset.
Grid search: This method involves exhaustively
searching over a specified range of possible architectures,
such as using one to four hidden layers and 10 to 100 neurons per layer.
The model is trained and evaluated using cross-validation
for each combination of architecture, and the best architecture
is selected based on some performance metric, such as accuracy.
Random Search: This method is similar to grid search,
but instead of exhaustively searching over a specified range,
the architecture is randomly sampled from the possible architectures.
The best architecture is selected based on
the performance of the model on the validation data.
Bayesian optimization:
This method uses a probabilistic model to predict the performance of different architectures,
based on the performance of the models that have been trained so far.
The algorithm selects the next architecture to evaluate based on
the predicted performance and the uncertainty in the prediction,
and updates the model after each evaluation.
Ultimately, the optimal number of hidden layers and neurons will
depend on the specific problem and dataset,
and it is common to try several different architectures and
select the best one based on the results of the model evaluation.
It is also important to keep in mind that overfitting can occur
if the model is too complex, and
underfitting can occur if the model is too simple,
so finding the right balance between complexity and simplicity is key.