tf.data.experimental.Reducer

Samples elements at random from the datasets in datasets.

tf.data.experimental.sample_from_datasets(
    datasets, weights=None, seed=None
)

Used in the notebooks

Used in the guide	Used in the tutorials
tf.data: Build TensorFlow input pipelines	Classification on imbalanced data

Args
`datasets`	A list of `tf.data.Dataset` objects with compatible structure.
`weights`	(Optional.) A list of `len(datasets)` floating-point values where `weights[i]` represents the probability with which an element should be sampled from `datasets[i]`, or a `tf.data.Dataset` object where each element is such a list. Defaults to a uniform distribution across `datasets`.
`seed`	(Optional.) A `tf.int64` scalar `tf.Tensor`, representing the random seed that will be used to create the distribution. See `tf.random.set_seed` for behavior.

Returns
A dataset that interleaves elements from `datasets` at random, according to `weights` if provided, otherwise with uniform probability.

Raises
`TypeError`	If the `datasets` or `weights` arguments have the wrong type.
`ValueError`	If the `weights` argument is specified and does not match the length of the `datasets` element.

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.data / experimental / experimental.Reducer

Used in the notebooks

Args

Returns

Raises