How to implement dropout in a Keras model?

To implement dropout in a Keras model, you can use the Dropout layer from Keras. This layer randomly sets a fraction of input units to 0 during training, which helps prevent overfitting.

Here’s an example of how to use the Dropout layer:

from tensorflow import keras
from tensorflow.keras import layers

model = keras.Sequential([
  layers.Dense(64, activation='relu'),
  layers.Dropout(0.5),
  layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

In this example, we’ve added a Dropout layer between the first hidden layer and the output layer. The dropout rate is set to 0.5, which means that half of the input units will be randomly set to 0 during each training batch.

Note that the dropout layer only applies when training is set to True. When using model.fit(), training will automatically be set to True during training and False during evaluation.

You can experiment with different dropout rates to find the best value for your model and dataset, but generally values between 0.2 and 0.5 are a good starting point.