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Applies Dropout to the input.
Used in the guide:
Used in the tutorials:
- Basic classification: Predict an image of clothing
- Classification on imbalanced data
- Create an Estimator from a Keras model
- Deep Convolutional Generative Adversarial Network
- Explore overfit and underfit
- Image classification
- TensorFlow 2.0 quickstart for beginners
- Transformer model for language understanding
Dropout consists in randomly setting
rate of input units to 0 at each update during training time,
which helps prevent overfitting.
rate: Float between 0 and 1. Fraction of the input units to drop.
noise_shape: 1D integer tensor representing the shape of the binary dropout mask that will be multiplied with the input. For instance, if your inputs have shape
(batch_size, timesteps, features)and you want the dropout mask to be the same for all timesteps, you can use
noise_shape=(batch_size, 1, features).
seed: A Python integer to use as random seed.
inputs: Input tensor (of any rank).
training: Python boolean indicating whether the layer should behave in training mode (adding dropout) or in inference mode (doing nothing).
__init__( rate, noise_shape=None, seed=None, **kwargs )