o 2h @s@ddlmZddlmZddlmZedGdddeZdS))backend) keras_export)Layerzkeras.layers.Dropoutcs@eZdZdZd fdd Zd ddZdd Zfd d ZZS)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). Nc sptjdi|d|krdksntd|||_||_||_|dkr/tj||_ d|_ | dS)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)selfr r r kwargs __class__rb/var/www/html/chatgem/venv/lib/python3.10/site-packages/keras/src/layers/regularization/dropout.pyr )s zDropout.__init__FcCs,|r|jdkrtjj||j|j|jdS|S)Nr)r r )r rrdropoutr r)rinputstrainingrrrcall:sz Dropout.callcCs|S)Nr)r input_shaperrrcompute_output_shapeDszDropout.compute_output_shapecs(t}|j|j|jd}i||S)N)r r r )r get_configr r r )r base_configconfigrrrrGs  zDropout.get_config)NN)F) __name__ __module__ __qualname____doc__r rrr __classcell__rrrrrs !  rN) keras.srcrkeras.src.api_exportrkeras.src.layers.layerrrrrrrs