From the course: Deep Learning with Python: Optimizing Deep Learning Models
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Defining a tunable deep learning model in Keras - Python Tutorial
From the course: Deep Learning with Python: Optimizing Deep Learning Models
Defining a tunable deep learning model in Keras
- [Instructor] In this video, you'll learn how to define a tuneable deep learning model in preparation for hyperparameter tuning. I will run the code in the 04_04e file. You can follow along by completing the empty code cells in the 04_04b file. Note that this video is the first in a two video sequence that teaches you how to tune the hyperparameters of a deep learning model. Make sure to run the previously written code to import and pre-process the data, as well as to build and train the baseline model. I've already done so. So the previously written code has been run above to create a baseline model. Now we're going to define a tuneable model architecture. Before we search for the optimal hyperparameters for a model, we need to define a function that specifies the architectural blueprint of the model. The blueprint will incorporate hyperparameters for the number of units per layer, dropout rates, and the optimizer learning rate. Keras Tuner will invoke this function later on…
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