The `Dropout` layer in Python's Keras library is a regularization technique used to prevent overfitting in neural networks. It randomly sets a fraction of input units to 0 at each update during training time, which helps to reduce the sensitivity of the model to specific patterns in the training data. As a result, Dropout can improve the generalization and performance of the model by introducing a form of stochastic regularization. It is often used in conjunction with other layers such as Dense or Convolutional layers to improve the overall performance and robustness of the neural network.
Python Dropout - 30 examples found. These are the top rated real world Python examples of keras.layers.core.Dropout extracted from open source projects. You can rate examples to help us improve the quality of examples.