6bib jSrSSKJs Jr SSKJr SSKJr SSK J r \ "S5"SS\55r g) z$Contains the SpatialDropout1D layer.N) InputSpec)Dropout) keras_exportzkeras.layers.SpatialDropout1Dc2^\rSrSrSrU4SjrSrSrU=r$)SpatialDropout1DaSpatial 1D version of Dropout. This version performs the same function as Dropout, however, it drops entire 1D feature maps instead of individual elements. If adjacent frames within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in an effective learning rate decrease. In this case, SpatialDropout1D will help promote independence between feature maps and should be used instead. Args: rate: Float between 0 and 1. Fraction of the input units to drop. Call arguments: inputs: A 3D tensor. training: Python boolean indicating whether the layer should behave in training mode (adding dropout) or in inference mode (doing nothing). Input shape: 3D tensor with shape: `(samples, timesteps, channels)` Output shape: Same as input. References: - [Efficient Object Localization Using Convolutional Networks](https://arxiv.org/abs/1411.4280) c D>[TU]"U40UD6 [SS9Ulg)N)ndim)super__init__r input_spec)selfratekwargs __class__s n/home/james-whalen/.local/lib/python3.13/site-packages/tf_keras/src/layers/regularization/spatial_dropout1d.pyr SpatialDropout1D.__init__4s! ((#+cH[R"U5nUSSUS4nU$)Nr)tfshape)rinputs input_shape noise_shapes r_get_noise_shape!SpatialDropout1D._get_noise_shape8s*hhv& "1~q+a.9 r)r) __name__ __module__ __qualname____firstlineno____doc__r r__static_attributes__ __classcell__)rs@rrrs.,rr) r$tensorflow.compat.v2compatv2rtf_keras.src.engine.input_specr*tf_keras.src.layers.regularization.dropoutr tensorflow.python.util.tf_exportrrrrr.s>+"!4>:-.w/r