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introduction
Un rappel est un outil puissant pour personnaliser le comportement d'un modèle Keras pendant la formation, l'évaluation ou l'inférence. Les exemples incluent tf.keras.callbacks.TensorBoard
de visualiser les progrès de la formation et les résultats avec TensorBoard, ou tf.keras.callbacks.ModelCheckpoint
pour périodiquement enregistrez votre modèle pendant la formation.
Dans ce guide, vous apprendrez ce qu'est un rappel Keras, ce qu'il peut faire et comment vous pouvez créer le vôtre. Nous proposons 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 - classe la keras.callbacks.Callback
classe et passer outre un ensemble de méthodes appelées à différents stades de la formation, les tests et prédisant. Les rappels sont utiles pour obtenir une vue sur les états internes et les statistiques du modèle pendant l'entraînement.
Vous pouvez passer une liste des callbacks (comme argument mot - clé callbacks
) aux méthodes de modèle suivants:
Un aperçu des méthodes de rappel
Méthodes globales
on_(train|test|predict)_begin(self, logs=None)
Appelé au début de fit
/ evaluate
/ predict
.
on_(train|test|predict)_end(self, logs=None)
Appelé à la fin de fit
/ evaluate
/ predict
.
Méthodes par lots pour l'entraînement/les tests/la prédiction
on_(train|test|predict)_batch_begin(self, batch, logs=None)
Appelé juste avant de traiter un lot pendant la formation/les tests/la prédiction.
on_(train|test|predict)_batch_end(self, batch, logs=None)
Appelé en fin de formation/test/prédiction d'un lot. Au sein de cette méthode, les 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]
Maintenant, définissez un rappel personnalisé simple qui enregistre :
- Quand
fit
/evaluate
/predict
début et 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 :
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
Les logs
dict contient la valeur de la perte, et toutes les mesures à la fin d'un lot ou époque. L'exemple inclut la perte et l'erreur absolue moyenne.
class LossAndErrorPrintingCallback(keras.callbacks.Callback):
def on_train_batch_end(self, batch, logs=None):
print(
"Up to batch {}, the average loss is {:7.2f}.".format(batch, logs["loss"])
)
def on_test_batch_end(self, batch, logs=None):
print(
"Up to batch {}, the average 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()],
)
Up to batch 0, the average loss is 30.79. Up to batch 1, the average loss is 459.11. Up to batch 2, the average loss is 314.68. Up to batch 3, the average loss is 237.97. Up to batch 4, the average loss is 191.76. Up to batch 5, the average loss is 160.95. Up to batch 6, the average loss is 138.74. Up to batch 7, the average loss is 124.85. The average loss for epoch 0 is 124.85 and mean absolute error is 6.00. Up to batch 0, the average loss is 5.13. Up to batch 1, the average loss is 4.66. Up to batch 2, the average loss is 4.71. Up to batch 3, the average loss is 4.66. Up to batch 4, the average loss is 4.69. Up to batch 5, the average loss is 4.56. Up to batch 6, the average loss is 4.77. Up to batch 7, the average loss is 4.77. The average loss for epoch 1 is 4.77 and mean absolute error is 1.75. Up to batch 0, the average loss is 5.73. Up to batch 1, the average loss is 5.04. Up to batch 2, the average loss is 5.10. Up to batch 3, the average loss is 5.14. Up to batch 4, the average loss is 5.37. Up to batch 5, the average loss is 5.24. Up to batch 6, the average loss is 5.22. Up to batch 7, the average loss is 5.16.
Utilisation de self.model
attribut
En plus de recevoir les informations du journal lorsque l' un de leurs méthodes est appelée, les callbacks ont accès au modèle associé au cycle actuel de formation / évaluation / inférence: self.model
.
Voici de quelques - unes des choses que vous pouvez faire avec self.model
dans un rappel:
- Set
self.model.stop_training = True
à la formation immédiatement interruption. - Hyperparamètres mutent de l'optimiseur (disponible en tant
self.model.optimizer
), tels queself.model.optimizer.learning_rate
. - Enregistrez le modèle à intervalles réguliers.
- Enregistrez la sortie de
model.predict()
sur quelques échantillons d'essai à la fin de chaque époque, à utiliser comme une vérification de la santé mentale au cours de la formation. - Extrayez les 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 à perte minimale
Ce premier exemple montre la création d'un Callback
qui empêche la formation lorsque le minimum de perte a été atteint, en définissant l'attribut self.model.stop_training
(Boolean). En option, vous pouvez fournir un argument de la patience
de préciser combien nous devrions attendre les époques avant d' arrêter après avoir atteint un minimum local.
tf.keras.callbacks.EarlyStopping
fournit une implémentation plus complète et 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()],
)
Up to batch 0, the average loss is 34.62. Up to batch 1, the average loss is 405.62. Up to batch 2, the average loss is 282.27. Up to batch 3, the average loss is 215.95. Up to batch 4, the average loss is 175.32. The average loss for epoch 0 is 175.32 and mean absolute error is 8.59. Up to batch 0, the average loss is 8.86. Up to batch 1, the average loss is 7.31. Up to batch 2, the average loss is 6.51. Up to batch 3, the average loss is 6.71. Up to batch 4, the average loss is 6.24. The average loss for epoch 1 is 6.24 and mean absolute error is 2.06. Up to batch 0, the average loss is 4.83. Up to batch 1, the average loss is 5.05. Up to batch 2, the average loss is 4.71. Up to batch 3, the average loss is 4.41. Up to batch 4, the average loss is 4.48. The average loss for epoch 2 is 4.48 and mean absolute error is 1.68. Up to batch 0, the average loss is 5.84. Up to batch 1, the average loss is 5.73. Up to batch 2, the average loss is 7.24. Up to batch 3, the average loss is 10.34. Up to batch 4, the average loss is 15.53. The average loss for epoch 3 is 15.53 and mean absolute error is 3.20. Restoring model weights from the end of the best epoch. Epoch 00004: early stopping <keras.callbacks.History at 0x7fd0843bf510>
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 mise en oeuvre 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. Up to batch 0, the average loss is 26.55. Up to batch 1, the average loss is 435.15. Up to batch 2, the average loss is 298.00. Up to batch 3, the average loss is 225.91. Up to batch 4, the average loss is 182.66. The average loss for epoch 0 is 182.66 and mean absolute error is 8.16. Epoch 00001: Learning rate is 0.1000. Up to batch 0, the average loss is 7.30. Up to batch 1, the average loss is 6.22. Up to batch 2, the average loss is 6.76. Up to batch 3, the average loss is 6.37. Up to batch 4, the average loss is 5.98. The average loss for epoch 1 is 5.98 and mean absolute error is 2.01. Epoch 00002: Learning rate is 0.1000. Up to batch 0, the average loss is 4.23. Up to batch 1, the average loss is 4.56. Up to batch 2, the average loss is 4.81. Up to batch 3, the average loss is 4.63. Up to batch 4, the average loss is 4.67. The average loss for epoch 2 is 4.67 and mean absolute error is 1.73. Epoch 00003: Learning rate is 0.0500. Up to batch 0, the average loss is 6.24. Up to batch 1, the average loss is 5.62. Up to batch 2, the average loss is 5.48. Up to batch 3, the average loss is 5.09. Up to batch 4, the average loss is 4.68. The average loss for epoch 3 is 4.68 and mean absolute error is 1.77. Epoch 00004: Learning rate is 0.0500. Up to batch 0, the average loss is 3.38. Up to batch 1, the average loss is 3.83. Up to batch 2, the average loss is 3.53. Up to batch 3, the average loss is 3.64. Up to batch 4, the average loss is 3.76. The average loss for epoch 4 is 3.76 and mean absolute error is 1.54. Epoch 00005: Learning rate is 0.0500. Up to batch 0, the average loss is 3.62. Up to batch 1, the average loss is 3.79. Up to batch 2, the average loss is 3.75. Up to batch 3, the average loss is 3.83. Up to batch 4, the average loss is 4.37. The average loss for epoch 5 is 4.37 and mean absolute error is 1.65. Epoch 00006: Learning rate is 0.0100. Up to batch 0, the average loss is 6.73. Up to batch 1, the average loss is 6.13. Up to batch 2, the average loss is 5.11. Up to batch 3, the average loss is 4.57. Up to batch 4, the average loss is 4.21. The average loss for epoch 6 is 4.21 and mean absolute error is 1.61. Epoch 00007: Learning rate is 0.0100. Up to batch 0, the average loss is 3.37. Up to batch 1, the average loss is 3.83. Up to batch 2, the average loss is 3.80. Up to batch 3, the average loss is 3.50. Up to batch 4, the average loss is 3.31. The average loss for epoch 7 is 3.31 and mean absolute error is 1.42. Epoch 00008: Learning rate is 0.0100. Up to batch 0, the average loss is 5.33. Up to batch 1, the average loss is 4.84. Up to batch 2, the average loss is 4.02. Up to batch 3, the average loss is 3.87. Up to batch 4, the average loss is 3.85. The average loss for epoch 8 is 3.85 and mean absolute error is 1.53. Epoch 00009: Learning rate is 0.0050. Up to batch 0, the average loss is 1.84. Up to batch 1, the average loss is 2.75. Up to batch 2, the average loss is 3.16. Up to batch 3, the average loss is 3.52. Up to batch 4, the average loss is 3.34. The average loss for epoch 9 is 3.34 and mean absolute error is 1.43. Epoch 00010: Learning rate is 0.0050. Up to batch 0, the average loss is 2.36. Up to batch 1, the average loss is 2.91. Up to batch 2, the average loss is 2.63. Up to batch 3, the average loss is 2.93. Up to batch 4, the average loss is 3.17. The average loss for epoch 10 is 3.17 and mean absolute error is 1.36. Epoch 00011: Learning rate is 0.0050. Up to batch 0, the average loss is 3.32. Up to batch 1, the average loss is 3.02. Up to batch 2, the average loss is 2.96. Up to batch 3, the average loss is 2.80. Up to batch 4, the average loss is 2.92. The average loss for epoch 11 is 2.92 and mean absolute error is 1.32. Epoch 00012: Learning rate is 0.0010. Up to batch 0, the average loss is 4.11. Up to batch 1, the average loss is 3.70. Up to batch 2, the average loss is 3.89. Up to batch 3, the average loss is 3.76. Up to batch 4, the average loss is 3.45. The average loss for epoch 12 is 3.45 and mean absolute error is 1.44. Epoch 00013: Learning rate is 0.0010. Up to batch 0, the average loss is 3.38. Up to batch 1, the average loss is 3.34. Up to batch 2, the average loss is 3.26. Up to batch 3, the average loss is 3.56. Up to batch 4, the average loss is 3.62. The average loss for epoch 13 is 3.62 and mean absolute error is 1.44. Epoch 00014: Learning rate is 0.0010. Up to batch 0, the average loss is 2.48. Up to batch 1, the average loss is 2.38. Up to batch 2, the average loss is 2.76. Up to batch 3, the average loss is 2.63. Up to batch 4, the average loss is 2.66. The average loss for epoch 14 is 2.66 and mean absolute error is 1.29. <keras.callbacks.History at 0x7fd08446c290>
Rappels Keras intégrés
Assurez - vous de vérifier les callbacks KERAS existants en lisant les API docs . Les applications incluent la journalisation au format CSV, l'enregistrement du modèle, la visualisation des métriques dans TensorBoard et bien plus encore !