tf.compat.v1.train.batch

Checks whether a V1 or V2 checkpoint exists with the specified prefix. (deprecated)

tf.compat.v1.train.checkpoint_exists(
    checkpoint_prefix
)

This is the recommended way to check if a checkpoint exists, since it takes into account the naming difference between V1 and V2 formats.

Args
`checkpoint_prefix`	the prefix of a V1 or V2 checkpoint, with V2 taking priority. Typically the result of `Saver.save()` or that of `tf.train.latest_checkpoint()`, regardless of sharded/non-sharded or V1/V2.

Returns
A bool, true if a checkpoint referred to by `checkpoint_prefix` exists.

TensorFlow

tf.compat / v1 / v1.train.batch_join

Args

Returns

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.compat / v1 / v1.train.batch_join

Args

Returns