tf.contrib.training.weighted_resample

Performs an approximate weighted resampling of inputs.

tf.contrib.training.weighted_resample(
    inputs, weights, overall_rate, scope=None, mean_decay=0.999, seed=None
)

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 overall_rate.

Args
`inputs`	A list of tensors whose first dimension is `batch_size`.
`weights`	A `[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.

Returns
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.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2020-10-01 UTC.

tf.contrib.training.weighted_resample

Args

Returns