tf.contrib.training.resample_at_rate()

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

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.

Uses Knuth's method to generate samples from the poisson distribution (but instead of just incrementing a count, actually emits the input); this is described at https://en.wikipedia.org/wiki/Poisson_distribution in the section on generating Poisson-distributed random variables.

Note that this method is not appropriate for large rate values: with float16 it will stop performing correctly for rates above 9.17; float32, 87; and float64, 708. (These are the base-e versions of the minimum representable exponent for each type.)

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.