tf.contrib.training.weighted_resample()

tf.contrib.training.weighted_resample(inputs, weights, overall_rate, scope=None, mean_decay=0.999, warmup=10, seed=None)

Performs an approximate weighted resampling of inputs.

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.
• warmup: Until the resulting tensor has been evaluated warmup times, the resampling menthod uses the true mean over all calls as its weight estimate, rather than a decayed mean.
• 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.