tf.contrib.training.weighted_resample( inputs, weights, overall_rate, scope=None, mean_decay=0.999, seed=None )
See the guide: Training (contrib) > Online data resampling
Performs an approximate weighted resampling of
This method chooses elements from
inputs where each item's rate of
selection is proportional to its value in
weights, and the average
rate of selection across all inputs (and many invocations!) is
inputs: A list of tensors whose first dimension is
[batch_size]-shaped tensor with each batch member's weight.
overall_rate: Desired overall rate of resampling.
scope: Scope to use for the op.
mean_decay: How quickly to decay the running estimate of the mean weight.
seed: Random seed.
A list of tensors exactly like
inputs, but with an unknown (and
possibly zero) first dimension.
A tensor containing the effective resampling rate used for each output.