Given inputs tensors, stochastically resamples each at a given rate.
tf.contrib.training.resample_at_rate(
inputs, rates, scope=None, seed=None, back_prop=False
)
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] containing 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.
|
View source on GitHub