python-kerasHow do I use dropout in Python Keras?

Dropout is a regularization technique used to reduce overfitting in neural networks. To use dropout in Python Keras, add a Dropout layer to the model after each layer that you want to regularize. For example:

from keras.layers import Dropout

model = Sequential()
model.add(Dense(64, activation='relu', input_dim=64))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))

The Dropout layer takes a rate parameter which is a float between 0 and 1, representing the fraction of the input units to drop. This example drops 50% of the input units for each of the two Dense layers.

The following list explains the different parts of the code:

• from keras.layers import Dropout: Imports the Dropout layer from the Keras library.
• model = Sequential(): Creates a Sequential model object.
• model.add(Dense(64, activation='relu', input_dim=64)): Adds a Dense layer with 64 units, ReLU activation, and 64 input dimensions.
• model.add(Dropout(0.5)): Adds a Dropout layer with a rate of 0.5.
• model.add(Dense(64, activation='relu')): Adds a Dense layer with 64 units and ReLU activation.
• model.add(Dropout(0.5)): Adds a Dropout layer with a rate of 0.5.
• model.add(Dense(10, activation='softmax')): Adds a Dense layer with 10 units and softmax activation.

For more information on using dropout in Python Keras, check out the Keras documentation and the Keras blog post on dropout.

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