tf.contrib.training.resample_at_rate

tf.contrib.training.resample_at_rate(
    inputs,
    rates,
    scope=None,
    seed=None,
    back_prop=False
)

Defined in tensorflow/contrib/training/python/training/resample.py.

See the guide: Training (contrib) > Online data resampling

Given inputs tensors, stochastically resamples each at a given rate.

For example, if the inputs are [[a1, a2], [b1, b2]] and the rates tensor contains [3, 1], then the return value may look like [[a1, a2, a1, a1], [b1, b2, b1, b1]]. However, many other outputs are possible, since this is stochastic -- averaged over many repeated calls, each set of inputs should appear in the output rate times the number of invocations.

Args:

• inputs: A list of tensors, each of which has a shape of [batch_size, ...]
• rates: A tensor of shape [batch_size] contiaining the resampling rates for each input.
• scope: Scope for the op.
• seed: Random seed to use.
• back_prop: Whether to allow back-propagation through this op.

Returns:

Selections from the input tensors.