tf.keras.callbacks.EarlyStopping

TensorFlow 1 version

View source on GitHub

Callback for creating simple, custom callbacks on-the-fly.

Inherits From: Callback

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.callbacks.LambdaCallback

tf.keras.callbacks.LambdaCallback(
    on_epoch_begin=None, on_epoch_end=None, on_batch_begin=None, on_batch_end=None,
    on_train_begin=None, on_train_end=None, **kwargs
)

Used in the notebooks

Used in the tutorials
TensorFlow Addons Optimizers: ConditionalGradient Displaying image data in TensorBoard

This callback is constructed with anonymous functions that will be called at the appropriate time. Note that the callbacks expects positional arguments, as:

on_epoch_begin and on_epoch_end expect two positional arguments: epoch, logs
on_batch_begin and on_batch_end expect two positional arguments: batch, logs
on_train_begin and on_train_end expect one positional argument: logs

Arguments
`on_epoch_begin`	called at the beginning of every epoch.
`on_epoch_end`	called at the end of every epoch.
`on_batch_begin`	called at the beginning of every batch.
`on_batch_end`	called at the end of every batch.
`on_train_begin`	called at the beginning of model training.
`on_train_end`	called at the end of model training.

Example:

# Print the batch number at the beginning of every batch.
batch_print_callback = LambdaCallback(
    on_batch_begin=lambda batch,logs: print(batch))

# Stream the epoch loss to a file in JSON format. The file content
# is not well-formed JSON but rather has a JSON object per line.
import json
json_log = open('loss_log.json', mode='wt', buffering=1)
json_logging_callback = LambdaCallback(
    on_epoch_end=lambda epoch, logs: json_log.write(
        json.dumps({'epoch': epoch, 'loss': logs['loss']}) + '\n'),
    on_train_end=lambda logs: json_log.close()
)

# Terminate some processes after having finished model training.
processes = ...
cleanup_callback = LambdaCallback(
    on_train_end=lambda logs: [
        p.terminate() for p in processes if p.is_alive()])

model.fit(...,
          callbacks=[batch_print_callback,
                     json_logging_callback,
                     cleanup_callback])

Methods

`set_model`

View source

set_model(
    model
)

`set_params`

View source

set_params(
    params
)

TensorFlow

tf

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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

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tf.losses

tf.math

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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.keras / callbacks / callbacks.EarlyStopping

View aliases

Used in the notebooks

Arguments

Example:

Methods

set_model

set_params

`set_model`

`set_params`