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introduction
Un rappel est un outil puissant pour personnaliser le comportement d'un modèle Keras pendant l'entraînement, l'évaluation ou l'inférence. Les exemples incluent tf.keras.callbacks.TensorBoard
pour visualiser la progression et les résultats de l'entraînement avec TensorBoard, ou tf.keras.callbacks.ModelCheckpoint
pour enregistrer périodiquement votre modèle pendant l'entraînement.
Dans ce guide, vous apprendrez ce qu'est un rappel Keras, ce qu'il peut faire et comment vous pouvez créer le vôtre. Nous fournissons quelques démos d'applications de rappel simples pour vous aider à démarrer.
Installer
import tensorflow as tf
from tensorflow import keras
Présentation des rappels Keras
Tous les callbacks sous- keras.callbacks.Callback
classe keras.callbacks.Callback
et remplacent un ensemble de méthodes appelées à différentes étapes d'entraînement, de test et de prévision. Les rappels sont utiles pour obtenir une vue sur les états internes et les statistiques du modèle pendant l'entraînement.
Vous pouvez transmettre une liste de rappels (en tant que callbacks
argument de mot-clé) aux méthodes de modèle suivantes:
Un aperçu des méthodes de rappel
Méthodes globales
on_(train|test|predict)_begin(self, logs=None)
Appelé au début de l' fit
/ evaluate
/ predict
.
on_(train|test|predict)_end(self, logs=None)
Appelé à la fin de l' fit
/ evaluate
/ predict
.
Méthodes au niveau du lot pour la formation / le test / la prévision
on_(train|test|predict)_batch_begin(self, batch, logs=None)
Appelé juste avant de traiter un lot pendant la formation / les tests / les prévisions.
on_(train|test|predict)_batch_end(self, batch, logs=None)
Appelé à la fin de la formation / du test / de la prédiction d'un lot. Dans cette méthode, logs
est un dict contenant les résultats des métriques.
Méthodes au niveau de l'époque (formation uniquement)
on_epoch_begin(self, epoch, logs=None)
Appelé au début d'une époque pendant l'entraînement.
on_epoch_end(self, epoch, logs=None)
Appelé à la fin d'une époque pendant l'entraînement.
Un exemple basique
Prenons un exemple concret. Pour commencer, importons tensorflow et définissons un modèle Keras séquentiel simple:
# Define the Keras model to add callbacks to
def get_model():
model = keras.Sequential()
model.add(keras.layers.Dense(1, input_dim=784))
model.compile(
optimizer=keras.optimizers.RMSprop(learning_rate=0.1),
loss="mean_squared_error",
metrics=["mean_absolute_error"],
)
return model
Ensuite, chargez les données MNIST pour l'entraînement et les tests à partir de l'API des ensembles de données Keras:
# Load example MNIST data and pre-process it
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train = x_train.reshape(-1, 784).astype("float32") / 255.0
x_test = x_test.reshape(-1, 784).astype("float32") / 255.0
# Limit the data to 1000 samples
x_train = x_train[:1000]
y_train = y_train[:1000]
x_test = x_test[:1000]
y_test = y_test[:1000]
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz 11493376/11490434 [==============================] - 0s 0us/step
Maintenant, définissez un simple rappel personnalisé qui enregistre:
- Quand
fit
/evaluate
/predict
début et la fin - Quand chaque époque commence et se termine
- Quand chaque lot de formation commence et se termine
- Quand chaque lot d'évaluation (test) commence et se termine
- Quand chaque lot d'inférence (prédiction) commence et se termine
class CustomCallback(keras.callbacks.Callback):
def on_train_begin(self, logs=None):
keys = list(logs.keys())
print("Starting training; got log keys: {}".format(keys))
def on_train_end(self, logs=None):
keys = list(logs.keys())
print("Stop training; got log keys: {}".format(keys))
def on_epoch_begin(self, epoch, logs=None):
keys = list(logs.keys())
print("Start epoch {} of training; got log keys: {}".format(epoch, keys))
def on_epoch_end(self, epoch, logs=None):
keys = list(logs.keys())
print("End epoch {} of training; got log keys: {}".format(epoch, keys))
def on_test_begin(self, logs=None):
keys = list(logs.keys())
print("Start testing; got log keys: {}".format(keys))
def on_test_end(self, logs=None):
keys = list(logs.keys())
print("Stop testing; got log keys: {}".format(keys))
def on_predict_begin(self, logs=None):
keys = list(logs.keys())
print("Start predicting; got log keys: {}".format(keys))
def on_predict_end(self, logs=None):
keys = list(logs.keys())
print("Stop predicting; got log keys: {}".format(keys))
def on_train_batch_begin(self, batch, logs=None):
keys = list(logs.keys())
print("...Training: start of batch {}; got log keys: {}".format(batch, keys))
def on_train_batch_end(self, batch, logs=None):
keys = list(logs.keys())
print("...Training: end of batch {}; got log keys: {}".format(batch, keys))
def on_test_batch_begin(self, batch, logs=None):
keys = list(logs.keys())
print("...Evaluating: start of batch {}; got log keys: {}".format(batch, keys))
def on_test_batch_end(self, batch, logs=None):
keys = list(logs.keys())
print("...Evaluating: end of batch {}; got log keys: {}".format(batch, keys))
def on_predict_batch_begin(self, batch, logs=None):
keys = list(logs.keys())
print("...Predicting: start of batch {}; got log keys: {}".format(batch, keys))
def on_predict_batch_end(self, batch, logs=None):
keys = list(logs.keys())
print("...Predicting: end of batch {}; got log keys: {}".format(batch, keys))
Essayons-le:
model = get_model()
model.fit(
x_train,
y_train,
batch_size=128,
epochs=1,
verbose=0,
validation_split=0.5,
callbacks=[CustomCallback()],
)
res = model.evaluate(
x_test, y_test, batch_size=128, verbose=0, callbacks=[CustomCallback()]
)
res = model.predict(x_test, batch_size=128, callbacks=[CustomCallback()])
Starting training; got log keys: [] Start epoch 0 of training; got log keys: [] ...Training: start of batch 0; got log keys: [] ...Training: end of batch 0; got log keys: ['loss', 'mean_absolute_error'] ...Training: start of batch 1; got log keys: [] ...Training: end of batch 1; got log keys: ['loss', 'mean_absolute_error'] ...Training: start of batch 2; got log keys: [] ...Training: end of batch 2; got log keys: ['loss', 'mean_absolute_error'] ...Training: start of batch 3; got log keys: [] ...Training: end of batch 3; got log keys: ['loss', 'mean_absolute_error'] Start testing; got log keys: [] ...Evaluating: start of batch 0; got log keys: [] ...Evaluating: end of batch 0; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 1; got log keys: [] ...Evaluating: end of batch 1; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 2; got log keys: [] ...Evaluating: end of batch 2; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 3; got log keys: [] ...Evaluating: end of batch 3; got log keys: ['loss', 'mean_absolute_error'] Stop testing; got log keys: ['loss', 'mean_absolute_error'] End epoch 0 of training; got log keys: ['loss', 'mean_absolute_error', 'val_loss', 'val_mean_absolute_error'] Stop training; got log keys: ['loss', 'mean_absolute_error', 'val_loss', 'val_mean_absolute_error'] Start testing; got log keys: [] ...Evaluating: start of batch 0; got log keys: [] ...Evaluating: end of batch 0; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 1; got log keys: [] ...Evaluating: end of batch 1; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 2; got log keys: [] ...Evaluating: end of batch 2; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 3; got log keys: [] ...Evaluating: end of batch 3; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 4; got log keys: [] ...Evaluating: end of batch 4; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 5; got log keys: [] ...Evaluating: end of batch 5; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 6; got log keys: [] ...Evaluating: end of batch 6; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 7; got log keys: [] ...Evaluating: end of batch 7; got log keys: ['loss', 'mean_absolute_error'] Stop testing; got log keys: ['loss', 'mean_absolute_error'] Start predicting; got log keys: [] ...Predicting: start of batch 0; got log keys: [] ...Predicting: end of batch 0; got log keys: ['outputs'] ...Predicting: start of batch 1; got log keys: [] ...Predicting: end of batch 1; got log keys: ['outputs'] ...Predicting: start of batch 2; got log keys: [] ...Predicting: end of batch 2; got log keys: ['outputs'] ...Predicting: start of batch 3; got log keys: [] ...Predicting: end of batch 3; got log keys: ['outputs'] ...Predicting: start of batch 4; got log keys: [] ...Predicting: end of batch 4; got log keys: ['outputs'] ...Predicting: start of batch 5; got log keys: [] ...Predicting: end of batch 5; got log keys: ['outputs'] ...Predicting: start of batch 6; got log keys: [] ...Predicting: end of batch 6; got log keys: ['outputs'] ...Predicting: start of batch 7; got log keys: [] ...Predicting: end of batch 7; got log keys: ['outputs'] Stop predicting; got log keys: []
Utilisation des logs
dict
Le logs
dict contient la valeur de la perte et toutes les métriques à la fin d'un lot ou d'une époque. L'exemple comprend la perte et l'erreur absolue moyenne.
class LossAndErrorPrintingCallback(keras.callbacks.Callback):
def on_train_batch_end(self, batch, logs=None):
print("For batch {}, loss is {:7.2f}.".format(batch, logs["loss"]))
def on_test_batch_end(self, batch, logs=None):
print("For batch {}, loss is {:7.2f}.".format(batch, logs["loss"]))
def on_epoch_end(self, epoch, logs=None):
print(
"The average loss for epoch {} is {:7.2f} "
"and mean absolute error is {:7.2f}.".format(
epoch, logs["loss"], logs["mean_absolute_error"]
)
)
model = get_model()
model.fit(
x_train,
y_train,
batch_size=128,
epochs=2,
verbose=0,
callbacks=[LossAndErrorPrintingCallback()],
)
res = model.evaluate(
x_test,
y_test,
batch_size=128,
verbose=0,
callbacks=[LossAndErrorPrintingCallback()],
)
For batch 0, loss is 27.09. For batch 1, loss is 455.54. For batch 2, loss is 310.84. For batch 3, loss is 235.38. For batch 4, loss is 189.59. For batch 5, loss is 159.45. For batch 6, loss is 137.62. For batch 7, loss is 123.95. The average loss for epoch 0 is 123.95 and mean absolute error is 6.04. For batch 0, loss is 4.68. For batch 1, loss is 4.44. For batch 2, loss is 4.25. For batch 3, loss is 4.19. For batch 4, loss is 4.10. For batch 5, loss is 4.15. For batch 6, loss is 4.41. For batch 7, loss is 4.44. The average loss for epoch 1 is 4.44 and mean absolute error is 1.70. For batch 0, loss is 4.60. For batch 1, loss is 4.22. For batch 2, loss is 4.30. For batch 3, loss is 4.23. For batch 4, loss is 4.37. For batch 5, loss is 4.35. For batch 6, loss is 4.34. For batch 7, loss is 4.28.
Utilisation de l'attribut self.model
En plus de recevoir des informations de journal lorsque l'une de leurs méthodes est appelée, les callbacks ont accès au modèle associé à la ronde actuelle de formation / évaluation / inférence: self.model
.
Voici quelques-unes des choses que vous pouvez faire avec self.model
dans un rappel:
- Définissez
self.model.stop_training = True
pour interrompre immédiatement l'entraînement. - Mutation des hyperparamètres de l'optimiseur (disponibles sous le nom de
self.model.optimizer
), tels queself.model.optimizer.learning_rate
. - Enregistrez le modèle à intervalles réguliers.
- Enregistrez la sortie de
model.predict()
sur quelques échantillons de test à la fin de chaque époque, à utiliser comme contrôle de cohérence pendant la formation. - Extrayez des visualisations des caractéristiques intermédiaires à la fin de chaque époque, pour surveiller ce que le modèle apprend au fil du temps.
- etc.
Voyons cela en action dans quelques exemples.
Exemples d'applications de rappel Keras
Arrêt précoce avec une perte minimale
Ce premier exemple montre la création d'un Callback
qui arrête l'entraînement lorsque le minimum de perte a été atteint, en définissant l'attribut self.model.stop_training
(booléen). En option, vous pouvez fournir un argument patience
pour spécifier combien d'époques nous devons attendre avant de s'arrêter après avoir atteint un minimum local.
tf.keras.callbacks.EarlyStopping
fournit une implémentation plus complète et plus générale.
import numpy as np
class EarlyStoppingAtMinLoss(keras.callbacks.Callback):
"""Stop training when the loss is at its min, i.e. the loss stops decreasing.
Arguments:
patience: Number of epochs to wait after min has been hit. After this
number of no improvement, training stops.
"""
def __init__(self, patience=0):
super(EarlyStoppingAtMinLoss, self).__init__()
self.patience = patience
# best_weights to store the weights at which the minimum loss occurs.
self.best_weights = None
def on_train_begin(self, logs=None):
# The number of epoch it has waited when loss is no longer minimum.
self.wait = 0
# The epoch the training stops at.
self.stopped_epoch = 0
# Initialize the best as infinity.
self.best = np.Inf
def on_epoch_end(self, epoch, logs=None):
current = logs.get("loss")
if np.less(current, self.best):
self.best = current
self.wait = 0
# Record the best weights if current results is better (less).
self.best_weights = self.model.get_weights()
else:
self.wait += 1
if self.wait >= self.patience:
self.stopped_epoch = epoch
self.model.stop_training = True
print("Restoring model weights from the end of the best epoch.")
self.model.set_weights(self.best_weights)
def on_train_end(self, logs=None):
if self.stopped_epoch > 0:
print("Epoch %05d: early stopping" % (self.stopped_epoch + 1))
model = get_model()
model.fit(
x_train,
y_train,
batch_size=64,
steps_per_epoch=5,
epochs=30,
verbose=0,
callbacks=[LossAndErrorPrintingCallback(), EarlyStoppingAtMinLoss()],
)
For batch 0, loss is 36.12. For batch 1, loss is 473.15. For batch 2, loss is 324.54. For batch 3, loss is 245.95. For batch 4, loss is 198.35. The average loss for epoch 0 is 198.35 and mean absolute error is 8.54. For batch 0, loss is 8.53. For batch 1, loss is 7.74. For batch 2, loss is 6.75. For batch 3, loss is 7.01. For batch 4, loss is 7.12. The average loss for epoch 1 is 7.12 and mean absolute error is 2.20. For batch 0, loss is 6.39. For batch 1, loss is 6.75. For batch 2, loss is 6.46. For batch 3, loss is 6.55. For batch 4, loss is 7.21. The average loss for epoch 2 is 7.21 and mean absolute error is 2.20. Restoring model weights from the end of the best epoch. Epoch 00003: early stopping <tensorflow.python.keras.callbacks.History at 0x7f39a680ffd0>
Planification du taux d'apprentissage
Dans cet exemple, nous montrons comment un rappel personnalisé peut être utilisé pour modifier dynamiquement le taux d'apprentissage de l'optimiseur au cours de la formation.
Voir callbacks.LearningRateScheduler
pour une implémentation plus générale.
class CustomLearningRateScheduler(keras.callbacks.Callback):
"""Learning rate scheduler which sets the learning rate according to schedule.
Arguments:
schedule: a function that takes an epoch index
(integer, indexed from 0) and current learning rate
as inputs and returns a new learning rate as output (float).
"""
def __init__(self, schedule):
super(CustomLearningRateScheduler, self).__init__()
self.schedule = schedule
def on_epoch_begin(self, epoch, logs=None):
if not hasattr(self.model.optimizer, "lr"):
raise ValueError('Optimizer must have a "lr" attribute.')
# Get the current learning rate from model's optimizer.
lr = float(tf.keras.backend.get_value(self.model.optimizer.learning_rate))
# Call schedule function to get the scheduled learning rate.
scheduled_lr = self.schedule(epoch, lr)
# Set the value back to the optimizer before this epoch starts
tf.keras.backend.set_value(self.model.optimizer.lr, scheduled_lr)
print("\nEpoch %05d: Learning rate is %6.4f." % (epoch, scheduled_lr))
LR_SCHEDULE = [
# (epoch to start, learning rate) tuples
(3, 0.05),
(6, 0.01),
(9, 0.005),
(12, 0.001),
]
def lr_schedule(epoch, lr):
"""Helper function to retrieve the scheduled learning rate based on epoch."""
if epoch < LR_SCHEDULE[0][0] or epoch > LR_SCHEDULE[-1][0]:
return lr
for i in range(len(LR_SCHEDULE)):
if epoch == LR_SCHEDULE[i][0]:
return LR_SCHEDULE[i][1]
return lr
model = get_model()
model.fit(
x_train,
y_train,
batch_size=64,
steps_per_epoch=5,
epochs=15,
verbose=0,
callbacks=[
LossAndErrorPrintingCallback(),
CustomLearningRateScheduler(lr_schedule),
],
)
Epoch 00000: Learning rate is 0.1000. For batch 0, loss is 28.49. For batch 1, loss is 432.45. For batch 2, loss is 298.60. For batch 3, loss is 227.34. For batch 4, loss is 183.34. The average loss for epoch 0 is 183.34 and mean absolute error is 8.37. Epoch 00001: Learning rate is 0.1000. For batch 0, loss is 5.96. For batch 1, loss is 6.24. For batch 2, loss is 5.68. For batch 3, loss is 5.64. For batch 4, loss is 5.41. The average loss for epoch 1 is 5.41 and mean absolute error is 1.89. Epoch 00002: Learning rate is 0.1000. For batch 0, loss is 4.84. For batch 1, loss is 4.66. For batch 2, loss is 5.96. For batch 3, loss is 7.54. For batch 4, loss is 8.48. The average loss for epoch 2 is 8.48 and mean absolute error is 2.29. Epoch 00003: Learning rate is 0.0500. For batch 0, loss is 11.10. For batch 1, loss is 6.77. For batch 2, loss is 5.99. For batch 3, loss is 5.07. For batch 4, loss is 5.03. The average loss for epoch 3 is 5.03 and mean absolute error is 1.76. Epoch 00004: Learning rate is 0.0500. For batch 0, loss is 4.72. For batch 1, loss is 4.30. For batch 2, loss is 4.20. For batch 3, loss is 4.29. For batch 4, loss is 4.30. The average loss for epoch 4 is 4.30 and mean absolute error is 1.66. Epoch 00005: Learning rate is 0.0500. For batch 0, loss is 5.52. For batch 1, loss is 5.15. For batch 2, loss is 4.51. For batch 3, loss is 4.40. For batch 4, loss is 4.80. The average loss for epoch 5 is 4.80 and mean absolute error is 1.77. Epoch 00006: Learning rate is 0.0100. For batch 0, loss is 7.07. For batch 1, loss is 6.72. For batch 2, loss is 5.62. For batch 3, loss is 4.79. For batch 4, loss is 4.68. The average loss for epoch 6 is 4.68 and mean absolute error is 1.69. Epoch 00007: Learning rate is 0.0100. For batch 0, loss is 2.61. For batch 1, loss is 2.50. For batch 2, loss is 2.76. For batch 3, loss is 2.96. For batch 4, loss is 3.14. The average loss for epoch 7 is 3.14 and mean absolute error is 1.38. Epoch 00008: Learning rate is 0.0100. For batch 0, loss is 4.12. For batch 1, loss is 3.91. For batch 2, loss is 3.37. For batch 3, loss is 3.30. For batch 4, loss is 3.08. The average loss for epoch 8 is 3.08 and mean absolute error is 1.37. Epoch 00009: Learning rate is 0.0050. For batch 0, loss is 5.81. For batch 1, loss is 5.12. For batch 2, loss is 4.53. For batch 3, loss is 4.08. For batch 4, loss is 3.95. The average loss for epoch 9 is 3.95 and mean absolute error is 1.56. Epoch 00010: Learning rate is 0.0050. For batch 0, loss is 2.73. For batch 1, loss is 2.83. For batch 2, loss is 2.75. For batch 3, loss is 3.07. For batch 4, loss is 2.93. The average loss for epoch 10 is 2.93 and mean absolute error is 1.35. Epoch 00011: Learning rate is 0.0050. For batch 0, loss is 3.33. For batch 1, loss is 3.60. For batch 2, loss is 3.77. For batch 3, loss is 3.51. For batch 4, loss is 3.43. The average loss for epoch 11 is 3.43 and mean absolute error is 1.40. Epoch 00012: Learning rate is 0.0010. For batch 0, loss is 4.29. For batch 1, loss is 3.72. For batch 2, loss is 3.78. For batch 3, loss is 3.61. For batch 4, loss is 3.47. The average loss for epoch 12 is 3.47 and mean absolute error is 1.46. Epoch 00013: Learning rate is 0.0010. For batch 0, loss is 3.01. For batch 1, loss is 3.10. For batch 2, loss is 3.20. For batch 3, loss is 3.00. For batch 4, loss is 3.16. The average loss for epoch 13 is 3.16 and mean absolute error is 1.36. Epoch 00014: Learning rate is 0.0010. For batch 0, loss is 5.22. For batch 1, loss is 3.80. For batch 2, loss is 3.61. For batch 3, loss is 3.45. For batch 4, loss is 3.43. The average loss for epoch 14 is 3.43 and mean absolute error is 1.43. <tensorflow.python.keras.callbacks.History at 0x7f39a6875400>
Callbacks Keras intégrés
Assurez-vous de vérifier les rappels Keras existants en lisant la documentation de l' API . Les applications incluent la journalisation au format CSV, l'enregistrement du modèle, la visualisation des métriques dans TensorBoard et bien plus encore!