0hdZddlZddlmcmZddlmZddlm Z ddl m Z ddl m Z e dGdd e jZy) zContains the Dropout layer.N)backend) base_layer)control_flow_util) keras_exportzkeras.layers.DropoutcBeZdZdZdfd ZdZddZdZfdZxZ S) DropoutaApplies 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 such that no values are dropped during inference. When using `model.fit`, `training` will be appropriately set to True automatically, and in other contexts, you can set the kwarg 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.) >>> tf.random.set_seed(0) >>> layer = tf.keras.layers.Dropout(.2, input_shape=(2,)) >>> data = np.arange(10).reshape(5, 2).astype(np.float32) >>> print(data) [[0. 1.] [2. 3.] [4. 5.] [6. 7.] [8. 9.]] >>> outputs = layer(data, training=True) >>> print(outputs) tf.Tensor( [[ 0. 1.25] [ 2.5 3.75] [ 5. 6.25] [ 7.5 8.75] [10. 0. ]], shape=(5, 2), dtype=float32) 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|dd|i|t|ttfrd|cxkrdksnt d|d||_||_||_d|_ y)NseedrzInvalid value z7 received for `rate`, expected a value between 0 and 1.T) super__init__ isinstanceintfloat ValueErrorrate noise_shaper supports_masking)selfrrr kwargs __class__s d/var/www/html/engine/venv/lib/python3.12/site-packages/tf_keras/src/layers/regularization/dropout.pyrzDropout.__init__Qsn -d-f- dS%L )!t.q. '<<  & $c|jytj|}g}t|jD]\}}|j |||n|tj |SN)rtfshape enumerateappendconvert_to_tensor)rinputsconcrete_inputs_shaperivalues r_get_noise_shapezDropout._get_noise_shape]su    # " 0 !$"2"23 HAu   ,1M%a(u  ##K00rctjtjr$jdk(rt j S|t j}fd}tj||fd}|S)NrcrjjjjS)N)r)_random_generatordropoutrr&)r"rsrdropped_inputsz$Dropout.call..dropped_inputsss7))11 t/D/DV/L2 rc.tjSr)ridentity)r"srzDropout.call..ysbkk&.Ar) rrnumbersRealrr-rlearning_phaser smart_cond)rr"trainingr+outputs`` rcallz Dropout.calllsh dii .499>;;v& &  --/H  #-- n&A  rc|Srr )r input_shapes rcompute_output_shapezDropout.compute_output_shape}src|j|j|jd}t|}t t |jt |jzS)N)rrr )rrr r get_configdictlistitems)rconfig base_configrs rr:zDropout.get_configsYII++II  g(* D**,-V\\^0DDEEr)NNr) __name__ __module__ __qualname____doc__rr&r5r8r: __classcell__)rs@rrrs)0d % 1"FFrr)rCr/tensorflow.compat.v2compatv2r tf_keras.srcrtf_keras.src.enginertf_keras.src.utilsr tensorflow.python.util.tf_exportrBaseRandomLayerrr rrrMsN"!! *0:$%iFj((iF&iFr