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前書き
コールバックは、トレーニング、評価、または推論中にKerasモデルの動作をカスタマイズするための強力なツールです。例としては、 tf.keras.callbacks.TensorBoard
を使用してトレーニングの進行状況と結果を視覚化するtf.keras.callbacks.TensorBoardや、トレーニング中にモデルを定期的に保存するtf.keras.callbacks.ModelCheckpoint
があります。
このガイドでは、Kerasコールバックとは何か、それができること、そして独自のコールバックを構築する方法を学びます。簡単なコールバックアプリケーションのデモをいくつか提供して、開始します。
セットアップ
import tensorflow as tf
from tensorflow import keras
Kerasコールバックの概要
すべてのコールバックは、 keras.callbacks.Callback
クラスをサブクラス化し、トレーニング、テスト、および予測のさまざまな段階で呼び出される一連のメソッドをオーバーライドします。コールバックは、トレーニング中にモデルの内部状態と統計を表示するのに役立ちます。
コールバックのリストを(キーワード引数callbacks
として)次のモデルメソッドに渡すことができます。
コールバックメソッドの概要
グローバルメソッド
on_(train|test|predict)_begin(self, logs=None)
fit
/ evaluate
/ predict
の開始時に呼び出されます。
on_(train|test|predict)_end(self, logs=None)
fit
/ evaluate
/ predict
の最後に呼び出されます。
トレーニング/テスト/予測のためのバッチレベルの方法
on_(train|test|predict)_batch_begin(self, batch, logs=None)
トレーニング/テスト/予測中にバッチを処理する直前に呼び出されます。
on_(train|test|predict)_batch_end(self, batch, logs=None)
バッチのトレーニング/テスト/予測の最後に呼び出されます。このメソッド内では、 logs
はメトリックの結果を含むdictです。
エポックレベルのメソッド(トレーニングのみ)
on_epoch_begin(self, epoch, logs=None)
トレーニング中のエポックの開始時に呼び出されます。
on_epoch_end(self, epoch, logs=None)
トレーニング中のエポックの終わりに呼び出されます。
基本的な例
具体的な例を見てみましょう。開始するには、tensorflowをインポートして、単純なSequentialKerasモデルを定義しましょう。
# 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
次に、KerasデータセットAPIからトレーニングとテスト用のMNISTデータをロードします。
# 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
次に、ログを記録する単純なカスタムコールバックを定義します。
-
fit
/evaluate
/predict
開始と終了時 - 各エポックが開始および終了するとき
- 各トレーニングバッチの開始時と終了時
- 各評価(テスト)バッチが開始および終了するとき
- 各推論(予測)バッチが開始および終了するとき
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))
それを試してみましょう:
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: []
logs
使用法
logs
dictには、損失値と、バッチまたはエポックの終了時のすべてのメトリックが含まれます。例には、損失と平均絶対誤差が含まれます。
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.
self.model
属性の使用法
メソッドの1つが呼び出されたときにログ情報を受信することに加えて、コールバックは、トレーニング/評価/推論の現在のラウンドに関連付けられたモデルにアクセスできます: self.model
。
コールバックでself.model
を使用して実行できるいくつかのことをself.model
ます。
-
self.model.stop_training = True
に設定すると、トレーニングがすぐに中断されます。 - self.model.optimizer.learning_rateなどのオプティマイザー(
self.model.optimizer
として利用可能)のself.model.optimizer.learning_rate
ます。 - 期間間隔でモデルを保存します。
- トレーニング中のサニティチェックとして使用するために、各エポックの終わりにいくつかのテストサンプルに
model.predict()
出力を記録します。 - 各エポックの終わりに中間機能の視覚化を抽出して、モデルが時間の経過とともに学習していることを監視します。
- 等
いくつかの例でこれが実際に動作するのを見てみましょう。
Kerasコールバックアプリケーションの例
最小限の損失で早期停止
この最初の例は、属性self.model.stop_training
(boolean)を設定することにより、損失が最小に達したときにトレーニングを停止するCallback
の作成を示しています。オプションで、極小値に達した後に停止する前に待機する必要があるエポックの数を指定するための引数のpatience
を提供できます。
tf.keras.callbacks.EarlyStopping
は、より完全で一般的な実装を提供します。
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>
学習率のスケジューリング
この例では、カスタムコールバックを使用して、トレーニング中にオプティマイザーの学習率を動的に変更する方法を示します。
より一般的な実装については、 callbacks.LearningRateScheduler
を参照してください。
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>
組み込みのKerasコールバック
APIドキュメントを読んで、既存のKerasコールバックを確認してください。アプリケーションには、CSVへのログ記録、モデルの保存、TensorBoardでのメトリックの視覚化などが含まれます。