How can I optimize hyperparameters in TensorFlow Keras?
An example of an AI chatbot conversation about optimizing hyperparameters in TensorFlow Keras.
How can I optimize hyperparameters in TensorFlow Keras?
You can optimize hyperparameters in TensorFlow Keras using techniques like Grid Search, Random Search, or Bayesian Optimization.
What is Grid Search and how do I implement it?
Grid Search is a method to systematically work through multiple combinations of hyperparameters. You can use GridSearchCV from sklearn to implement it.
Can you give me an example of using Grid Search with Keras?
Sure! You can define a model function, then use GridSearchCV to search through a parameter grid for the best model.
What are some common hyperparameters I should tune?
Common hyperparameters include learning rate, batch size, number of epochs, and the architecture of the model itself.
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