HenryAI/KerasBERTv1-Data · Datasets at Hugging Face

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Doing fine-tuning of the top layers when training seems to be plateauing
Sending email or instant message notifications when training ends or where a certain performance threshold is exceeded
Etc.
Callbacks can be passed as a list to your call to fit():

model = get_compiled_model()

callbacks = [
keras.callbacks.EarlyStopping(
# Stop training when `val_loss` is no longer improving
monitor="val_loss",
# "no longer improving" being defined as "no better than 1e-2 less"
min_delta=1e-2,
# "no longer improving" being further defined as "for at least 2 epochs"
patience=2,
verbose=1,
)
]
model.fit(
x_train,
y_train,
epochs=20,
batch_size=64,
callbacks=callbacks,
validation_split=0.2,
)
Epoch 1/20
625/625 [==============================] - 1s 1ms/step - loss: 0.6032 - sparse_categorical_accuracy: 0.8355 - val_loss: 0.2303 - val_sparse_categorical_accuracy: 0.9306
Epoch 2/20
625/625 [==============================] - 1s 1ms/step - loss: 0.1855 - sparse_categorical_accuracy: 0.9458 - val_loss: 0.1775 - val_sparse_categorical_accuracy: 0.9471
Epoch 3/20
625/625 [==============================] - 1s 1ms/step - loss: 0.1280 - sparse_categorical_accuracy: 0.9597 - val_loss: 0.1585 - val_sparse_categorical_accuracy: 0.9531
Epoch 4/20
625/625 [==============================] - 1s 1ms/step - loss: 0.0986 - sparse_categorical_accuracy: 0.9704 - val_loss: 0.1418 - val_sparse_categorical_accuracy: 0.9593
Epoch 5/20
625/625 [==============================] - 1s 1ms/step - loss: 0.0774 - sparse_categorical_accuracy: 0.9761 - val_loss: 0.1319 - val_sparse_categorical_accuracy: 0.9628
Epoch 6/20
625/625 [==============================] - 1s 1ms/step - loss: 0.0649 - sparse_categorical_accuracy: 0.9798 - val_loss: 0.1465 - val_sparse_categorical_accuracy: 0.9580
Epoch 00006: early stopping

<tensorflow.python.keras.callbacks.History at 0x14e899ad0>
Many built-in callbacks are available
There are many built-in callbacks already available in Keras, such as:

ModelCheckpoint: Periodically save the model.
EarlyStopping: Stop training when training is no longer improving the validation metrics.
TensorBoard: periodically write model logs that can be visualized in TensorBoard (more details in the section "Visualization").
CSVLogger: streams loss and metrics data to a CSV file.
etc.
See the callbacks documentation for the complete list.

Writing your own callback
You can create a custom callback by extending the base class keras.callbacks.Callback. A callback has access to its associated model through the class property self.model.

Make sure to read the complete guide to writing custom callbacks.

Here's a simple example saving a list of per-batch loss values during training:

class LossHistory(keras.callbacks.Callback):
def on_train_begin(self, logs):
self.per_batch_losses = []

def on_batch_end(self, batch, logs):
self.per_batch_losses.append(logs.get("loss"))
Checkpointing models
When you're training model on relatively large datasets, it's crucial to save checkpoints of your model at frequent intervals.

The easiest way to achieve this is with the ModelCheckpoint callback:

model = get_compiled_model()

callbacks = [
keras.callbacks.ModelCheckpoint(
# Path where to save the model
# The two parameters below mean that we will overwrite
# the current checkpoint if and only if
# the `val_loss` score has improved.
# The saved model name will include the current epoch.
filepath="mymodel_{epoch}",
save_best_only=True, # Only save a model if `val_loss` has improved.
monitor="val_loss",
verbose=1,
)
]
model.fit(
x_train, y_train, epochs=2, batch_size=64, callbacks=callbacks, validation_split=0.2
)
Epoch 1/2
625/625 [==============================] - 1s 1ms/step - loss: 0.6380 - sparse_categorical_accuracy: 0.8226 - val_loss: 0.2283 - val_sparse_categorical_accuracy: 0.9317
Epoch 00001: val_loss improved from inf to 0.22825, saving model to mymodel_1
INFO:tensorflow:Assets written to: mymodel_1/assets
Epoch 2/2
625/625 [==============================] - 1s 1ms/step - loss: 0.1787 - sparse_categorical_accuracy: 0.9466 - val_loss: 0.1877 - val_sparse_categorical_accuracy: 0.9440
Epoch 00002: val_loss improved from 0.22825 to 0.18768, saving model to mymodel_2
INFO:tensorflow:Assets written to: mymodel_2/assets

<tensorflow.python.keras.callbacks.History at 0x14e899b90>
The ModelCheckpoint callback can be used to implement fault-tolerance: the ability to restart training from the last saved state of the model in case training gets randomly interrupted. Here's a basic example:

import os

Doing fine-tuning of the top layers when training seems to be plateauing

Sending email or instant message notifications when training ends or where a certain performance threshold is exceeded

Etc.

Callbacks can be passed as a list to your call to fit():

model = get_compiled_model()

callbacks = [

keras.callbacks.EarlyStopping(

# Stop training when `val_loss` is no longer improving

monitor="val_loss",

# "no longer improving" being defined as "no better than 1e-2 less"

min_delta=1e-2,

# "no longer improving" being further defined as "for at least 2 epochs"

patience=2,

verbose=1,

)

]

model.fit(

x_train,

y_train,

epochs=20,

batch_size=64,

callbacks=callbacks,

validation_split=0.2,

)

Epoch 1/20

625/625 [==============================] - 1s 1ms/step - loss: 0.6032 - sparse_categorical_accuracy: 0.8355 - val_loss: 0.2303 - val_sparse_categorical_accuracy: 0.9306

Epoch 2/20

625/625 [==============================] - 1s 1ms/step - loss: 0.1855 - sparse_categorical_accuracy: 0.9458 - val_loss: 0.1775 - val_sparse_categorical_accuracy: 0.9471

Epoch 3/20

625/625 [==============================] - 1s 1ms/step - loss: 0.1280 - sparse_categorical_accuracy: 0.9597 - val_loss: 0.1585 - val_sparse_categorical_accuracy: 0.9531

Epoch 4/20

625/625 [==============================] - 1s 1ms/step - loss: 0.0986 - sparse_categorical_accuracy: 0.9704 - val_loss: 0.1418 - val_sparse_categorical_accuracy: 0.9593

Epoch 5/20

625/625 [==============================] - 1s 1ms/step - loss: 0.0774 - sparse_categorical_accuracy: 0.9761 - val_loss: 0.1319 - val_sparse_categorical_accuracy: 0.9628

Epoch 6/20

625/625 [==============================] - 1s 1ms/step - loss: 0.0649 - sparse_categorical_accuracy: 0.9798 - val_loss: 0.1465 - val_sparse_categorical_accuracy: 0.9580

Epoch 00006: early stopping

<tensorflow.python.keras.callbacks.History at 0x14e899ad0>

Many built-in callbacks are available

There are many built-in callbacks already available in Keras, such as:

ModelCheckpoint: Periodically save the model.

EarlyStopping: Stop training when training is no longer improving the validation metrics.

TensorBoard: periodically write model logs that can be visualized in TensorBoard (more details in the section "Visualization").

CSVLogger: streams loss and metrics data to a CSV file.

etc.

See the callbacks documentation for the complete list.

Writing your own callback

You can create a custom callback by extending the base class keras.callbacks.Callback. A callback has access to its associated model through the class property self.model.

Make sure to read the complete guide to writing custom callbacks.

Here's a simple example saving a list of per-batch loss values during training:

class LossHistory(keras.callbacks.Callback):

def on_train_begin(self, logs):

self.per_batch_losses = []

def on_batch_end(self, batch, logs):

self.per_batch_losses.append(logs.get("loss"))

Checkpointing models

When you're training model on relatively large datasets, it's crucial to save checkpoints of your model at frequent intervals.

The easiest way to achieve this is with the ModelCheckpoint callback:

model = get_compiled_model()

callbacks = [

keras.callbacks.ModelCheckpoint(

# Path where to save the model

# The two parameters below mean that we will overwrite

# the current checkpoint if and only if

# the `val_loss` score has improved.

# The saved model name will include the current epoch.

filepath="mymodel_{epoch}",

save_best_only=True, # Only save a model if `val_loss` has improved.

monitor="val_loss",

verbose=1,

)

]

model.fit(

x_train, y_train, epochs=2, batch_size=64, callbacks=callbacks, validation_split=0.2

)

Epoch 1/2

625/625 [==============================] - 1s 1ms/step - loss: 0.6380 - sparse_categorical_accuracy: 0.8226 - val_loss: 0.2283 - val_sparse_categorical_accuracy: 0.9317

Epoch 00001: val_loss improved from inf to 0.22825, saving model to mymodel_1

INFO:tensorflow:Assets written to: mymodel_1/assets

Epoch 2/2

625/625 [==============================] - 1s 1ms/step - loss: 0.1787 - sparse_categorical_accuracy: 0.9466 - val_loss: 0.1877 - val_sparse_categorical_accuracy: 0.9440

Epoch 00002: val_loss improved from 0.22825 to 0.18768, saving model to mymodel_2

INFO:tensorflow:Assets written to: mymodel_2/assets

<tensorflow.python.keras.callbacks.History at 0x14e899b90>

The ModelCheckpoint callback can be used to implement fault-tolerance: the ability to restart training from the last saved state of the model in case training gets randomly interrupted. Here's a basic example:

import os