Dropout consists in randomly setting a fraction rate of input units to 0
at each update during training time, which helps prevent overfitting.
The units that are kept are scaled by 1 / (1 - rate), so that their
sum is unchanged at training time and inference time.
Arguments
inputs
Tensor input.
rate
The dropout rate, between 0 and 1. E.g. "rate=0.1" would drop out
10% of input units.
noise_shape
1D tensor of type int32 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].
Either a Python boolean, or a TensorFlow boolean scalar tensor
(e.g. a placeholder). Whether to return the output in training mode
(apply dropout) or in inference mode (return the input untouched).
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2020-10-01 UTC."],[],[]]