Learn By Example | How to add a dropout layer to a Deep Learning Model in Keras?
Deep learning models are complex algorithms that can be used to solve a variety of tasks, such as image recognition, natural language processing, and more. However, these models can sometimes “memorize” the training data too well, which results in poor performance when presented with new data. This is called overfitting.
One way to combat overfitting is by using a technique called dropout. Dropout works by randomly ignoring a certain percentage of the neurons in the model during training. This forces the model to rely on other neurons to make predictions, which helps to prevent overfitting.
To add dropout to a deep learning model in Keras, you will need to use the “Dropout” function and specify the percentage of neurons to ignore. The percentage should be set based on the dataset and the architecture of the model. It is important to note that dropout should only be used during the training phase and not during the testing phase, so the dropout rate should be set to 0 during testing or use model.evaluate() or model.predict()
In summary, adding dropout to a deep learning model in Keras can help to prevent overfitting by randomly ignoring a certain percentage of the neurons during training. This forces the model to rely on other neurons to make predictions, which can lead to better performance on new, unseen data.
In this Applied Machine Learning & Data Science Recipe, the reader will find the practical use of applied machine learning and data science in Python & R programming: Learn By Example | How to setup a Deep Learning Model in Keras?
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