2Vh TddlmZddlmZddlmZedGddeZy))backend) keras_export)Layerzkeras.layers.Dropoutc<eZdZdZdfd ZddZdZfdZxZS)DropoutaBApplies dropout to the input. The `Dropout` layer randomly sets input units to 0 with a frequency of `rate` at each step during training time, which helps prevent overfitting. Inputs not set to 0 are scaled up by `1 / (1 - rate)` such that the sum over all inputs is unchanged. Note that the `Dropout` layer only applies when `training` is set to `True` in `call()`, such that no values are dropped during inference. When using `model.fit`, `training` will be appropriately set to `True` automatically. In other contexts, you can set the argument explicitly to `True` when calling the layer. (This is in contrast to setting `trainable=False` for a `Dropout` layer. `trainable` does not affect the layer's behavior, as `Dropout` does not have any variables/weights that can be frozen during training.) Args: 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. Call arguments: 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). c t|di|d|cxkrdksntd|||_||_||_|dkDr$t jj||_ d|_ |jy)NrzcInvalid value received for argument `rate`. Expected a float value between 0 and 1. Received: rate=T) super__init__ ValueErrorrateseed noise_shaperrandom SeedGeneratorseed_generatorsupports_masking_build_at_init)selfrrrkwargs __class__s W/home/dcms/DCMS/lib/python3.12/site-packages/keras/src/layers/regularization/dropout.pyr zDropout.__init__)s "6"D~A~""&)    & !8")..">">t"DD  $ c|rP|jdkDrAtjj||j|j|j S|S)Nr)rr)rrrdropoutrr)rinputstrainings rcallz Dropout.call:sN  A >>))  ,,(( *   rc|S)Nr )r input_shapes rcompute_output_shapezDropout.compute_output_shapeDsrctt|}|j|j|jd}i||S)N)rrr)r get_configrrr)r base_configconfigrs rr$zDropout.get_configGs@g(* IIII++  )+(((r)NN)F) __name__ __module__ __qualname____doc__r rr"r$ __classcell__)rs@rrrs"B"))rrN) keras.srcrkeras.src.api_exportrkeras.src.layers.layerrrr rrr/s2-($%G)eG)&G)r