Contenido del Curso
Introduction to Neural Networks
Introduction to Neural Networks
Automatic Hyperparameter Tuning
Tarea
Rather than manually selecting specific values for our model's hyperparameters, employing Random Search (RandomizedSearchCV
) can be a more efficient strategy to discover the optimal settings. The concept is somewhat akin to GridSearchCV, yet it comes with a significant difference.
In the realm of neural networks, exhaustively searching through every possible combination of parameters, as GridSearchCV does, can be prohibitively time-consuming.
This is where Random Search shines. Instead of evaluating all parameter combinations, it samples a random subset of them, which often leads to faster and surprisingly effective results.
Here is an example of Random Search usage:
Your task is:
- Generate values for two hidden layers with number of neurons in range from
20
to30
with step2
. - Set the values for the learning rate to choose from. As we saw in the previous chapter, the model performs well with a learning rate of around
0.01
. So we can reduce the search area to the values0.02
,0.01
, and0.005
. - Generate
10
random values for epochs in range from10
to50
. - Apply random search for 4 models (iterations).
- Evaluate the model.
¿Todo estuvo claro?
Contenido del Curso
Introduction to Neural Networks
Introduction to Neural Networks
Automatic Hyperparameter Tuning
Tarea
Rather than manually selecting specific values for our model's hyperparameters, employing Random Search (RandomizedSearchCV
) can be a more efficient strategy to discover the optimal settings. The concept is somewhat akin to GridSearchCV, yet it comes with a significant difference.
In the realm of neural networks, exhaustively searching through every possible combination of parameters, as GridSearchCV does, can be prohibitively time-consuming.
This is where Random Search shines. Instead of evaluating all parameter combinations, it samples a random subset of them, which often leads to faster and surprisingly effective results.
Here is an example of Random Search usage:
Your task is:
- Generate values for two hidden layers with number of neurons in range from
20
to30
with step2
. - Set the values for the learning rate to choose from. As we saw in the previous chapter, the model performs well with a learning rate of around
0.01
. So we can reduce the search area to the values0.02
,0.01
, and0.005
. - Generate
10
random values for epochs in range from10
to50
. - Apply random search for 4 models (iterations).
- Evaluate the model.
¿Todo estuvo claro?