tf.nn.experimental.general_dropout

Computes dropout: randomly sets elements to zero to prevent overfitting.

tf.nn.experimental.general_dropout(
    x, rate, uniform_sampler, noise_shape=None, name=None
)

Please see tf.nn.experimental.stateless_dropout for an overview of dropout.

Unlike tf.nn.experimental.stateless_dropout, here you can supply a custom sampler function uniform_sampler that (given a shape and a dtype) generates a random, Uniform[0, 1)-distributed tensor (of that shape and dtype). uniform_sampler can be e.g. tf.random.stateless_random_uniform or tf.random.Generator.uniform.

For example, if you are using tf.random.Generator to generate random numbers, you can use this code to do dropouts:

g = tf.random.Generator.from_seed(7)
sampler = g.uniform
x = tf.constant([1.1, 2.2, 3.3, 4.4, 5.5])
rate = 0.5
tf.nn.experimental.general_dropout(x, rate, sampler)
<tf.Tensor: shape=(5,), ..., numpy=array([ 0. ,  4.4,  6.6,  8.8, 11. ], ...)>
tf.nn.experimental.general_dropout(x, rate, sampler)
<tf.Tensor: shape=(5,), ..., numpy=array([2.2, 0. , 0. , 8.8, 0. ], ...)>

It has better performance than using tf.nn.experimental.stateless_dropout and tf.random.Generator.make_seeds:

g = tf.random.Generator.from_seed(7)
x = tf.constant([1.1, 2.2, 3.3, 4.4, 5.5])
rate = 0.5
tf.nn.experimental.stateless_dropout(x, rate, g.make_seeds(1)[:, 0])
<tf.Tensor: shape=(5,), ..., numpy=array([ 2.2,  4.4,  6.6,  0. , 11. ], ...)>
tf.nn.experimental.stateless_dropout(x, rate, g.make_seeds(1)[:, 0])
<tf.Tensor: shape=(5,), ..., numpy=array([2.2, 0. , 6.6, 8.8, 0. ], ...>

because generating and consuming seeds cost extra computation. tf.nn.experimental.general_dropout can let you avoid them.

x A floating point tensor.
rate A scalar Tensor with the same type as x. The probability that each element is dropped. For example, setting rate=0.1 would drop 10% of input elements.
uniform_sampler a callable of signature (shape, dtype) -> Tensor[shape, dtype], used to generate a tensor of uniformly-distributed random numbers in the range [0, 1), of the given shape and dtype.
noise_shape A 1-D integer Tensor, representing the shape for randomly generated keep/drop flags.
name A name for this operation.

A Tensor of the same shape and dtype of x.

ValueError If rate is not in [0, 1) or if x is not a floating point tensor. rate=1 is disallowed, because the output would be all zeros, which is likely not what was intended.