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独自のコールバックを書く

TensorFlow.orgで見る Google Colabで実行 GitHubでソースを表示 ノートブックをダウンロード

前書き

コールバックは、トレーニング、評価、または推論中の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。例には、 tf.keras.callbacks.TensorBoardトレーニングの進行状況と結果を視覚化するtf.keras.callbacks.TensorBoard、またはトレーニング中にモデルを定期的に保存するtf.keras.callbacks.ModelCheckpointれます。

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、および独自のコールバックを構築する方法について学びます。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ご用意しております。

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メソッドのセット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のビューを取得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はメトリクスの結果を含む辞書です。

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)

トレーニング中にエポックの初めに呼び出されます。

on_epoch_end(self, epoch, logs=None)

トレーニング中に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。はじめに、テンソルフローをインポートして、単純なSequential Kerasモデルを定義します。

 # 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、トレーニングおよびテスト用のMNISTデータをKerasデータセットAPIからロードします。

 # 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、ログを記録する簡単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
  • 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が開始および終了するとき
  • 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))

 

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: []
End epoch 0 of training; got log keys: ['loss', 'mean_absolute_error', 'val_loss', 'val_mean_absolute_error']
Stop training; got log keys: []
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: []
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辞書の使用

logs辞書には、損失値と、バッチまたはエポックの最後のすべてのメトリックが含まれています。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   30.07.
For batch 1, loss is  413.20.
For batch 2, loss is  284.62.
For batch 3, loss is  215.74.
For batch 4, loss is  174.43.
For batch 5, loss is  146.54.
For batch 6, loss is  126.53.
For batch 7, loss is  114.00.
The average loss for epoch 0 is  114.00 and mean absolute error is    5.88.
For batch 0, loss is    5.26.
For batch 1, loss is    5.16.
For batch 2, loss is    4.90.
For batch 3, loss is    4.91.
For batch 4, loss is    4.82.
For batch 5, loss is    4.64.
For batch 6, loss is    4.51.
For batch 7, loss is    4.52.
The average loss for epoch 1 is    4.52 and mean absolute error is    1.71.
For batch 0, loss is    7.47.
For batch 1, loss is    7.51.
For batch 2, loss is    7.33.
For batch 3, loss is    7.34.
For batch 4, loss is    7.35.
For batch 5, loss is    7.48.
For batch 6, loss is    7.44.
For batch 7, loss is    7.36.

self.model属性の使用

コールバックは、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だけでなく、現在のトレーニング/評価/推論のラウンドに関連付けられているモデルself.modelもアクセスできます。

コールバックでself.modelを使用してできることのいくつかをself.modelます。

  • self.model.stop_training = Trueに設定すると、トレーニングが即座に中断されます。
  • self.model.optimizer.learning_rateなど、オプティマイザーのself.model.optimizerself.model.optimizerとして利用可能)をself.model.optimizer.learning_rateます。
  • 期間ごとにモデルを保存します。
  • 各エポックの終わりにいくつかのテストサンプルでmodel.predict()出力を記録し、トレーニング中の健全性チェックとして使用します。
  • 各エポックの終わりに中間機能の視覚化を抽出し、モデルが学習しているものを経時的に監視します。

これをいくつかの例で見てみましょう。

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、損失の最小値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、ローカルの最小値に達した後、停止するまでに待機する必要があるエポックの数を指定する引数の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   27.80.
For batch 1, loss is  392.24.
For batch 2, loss is  269.56.
For batch 3, loss is  205.69.
For batch 4, loss is  166.82.
The average loss for epoch 0 is  166.82 and mean absolute error is    7.96.
For batch 0, loss is    7.27.
For batch 1, loss is    6.18.
For batch 2, loss is    6.08.
For batch 3, loss is    5.83.
For batch 4, loss is    5.61.
The average loss for epoch 1 is    5.61 and mean absolute error is    1.91.
For batch 0, loss is    5.03.
For batch 1, loss is    6.90.
For batch 2, loss is    7.60.
For batch 3, loss is    7.69.
For batch 4, loss is    8.49.
The average loss for epoch 2 is    8.49 and mean absolute error is    2.41.
Restoring model weights from the end of the best epoch.
Epoch 00003: early stopping

<tensorflow.python.keras.callbacks.History at 0x7fa32a9822e8>

学習率スケジューリング

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   36.98.
For batch 1, loss is  505.58.
For batch 2, loss is  344.37.
For batch 3, loss is  260.42.
For batch 4, loss is  210.08.
The average loss for epoch 0 is  210.08 and mean absolute error is    8.70.

Epoch 00001: Learning rate is 0.1000.
For batch 0, loss is    6.16.
For batch 1, loss is    5.91.
For batch 2, loss is    5.25.
For batch 3, loss is    5.30.
For batch 4, loss is    5.15.
The average loss for epoch 1 is    5.15 and mean absolute error is    1.84.

Epoch 00002: Learning rate is 0.1000.
For batch 0, loss is    8.28.
For batch 1, loss is    7.63.
For batch 2, loss is    6.94.
For batch 3, loss is    6.69.
For batch 4, loss is    6.24.
The average loss for epoch 2 is    6.24 and mean absolute error is    1.98.

Epoch 00003: Learning rate is 0.0500.
For batch 0, loss is    6.48.
For batch 1, loss is    5.40.
For batch 2, loss is    4.70.
For batch 3, loss is    4.45.
For batch 4, loss is    4.08.
The average loss for epoch 3 is    4.08 and mean absolute error is    1.64.

Epoch 00004: Learning rate is 0.0500.
For batch 0, loss is    3.30.
For batch 1, loss is    4.07.
For batch 2, loss is    4.14.
For batch 3, loss is    4.02.
For batch 4, loss is    4.07.
The average loss for epoch 4 is    4.07 and mean absolute error is    1.62.

Epoch 00005: Learning rate is 0.0500.
For batch 0, loss is    4.81.
For batch 1, loss is    5.43.
For batch 2, loss is    4.83.
For batch 3, loss is    4.90.
For batch 4, loss is    4.58.
The average loss for epoch 5 is    4.58 and mean absolute error is    1.73.

Epoch 00006: Learning rate is 0.0100.
For batch 0, loss is    2.87.
For batch 1, loss is    3.13.
For batch 2, loss is    2.93.
For batch 3, loss is    3.02.
For batch 4, loss is    3.08.
The average loss for epoch 6 is    3.08 and mean absolute error is    1.41.

Epoch 00007: Learning rate is 0.0100.
For batch 0, loss is    3.33.
For batch 1, loss is    4.50.
For batch 2, loss is    4.32.
For batch 3, loss is    4.17.
For batch 4, loss is    3.97.
The average loss for epoch 7 is    3.97 and mean absolute error is    1.57.

Epoch 00008: Learning rate is 0.0100.
For batch 0, loss is    3.20.
For batch 1, loss is    3.54.
For batch 2, loss is    3.16.
For batch 3, loss is    3.29.
For batch 4, loss is    3.54.
The average loss for epoch 8 is    3.54 and mean absolute error is    1.47.

Epoch 00009: Learning rate is 0.0050.
For batch 0, loss is    3.39.
For batch 1, loss is    3.00.
For batch 2, loss is    3.12.
For batch 3, loss is    3.36.
For batch 4, loss is    3.20.
The average loss for epoch 9 is    3.20 and mean absolute error is    1.43.

Epoch 00010: Learning rate is 0.0050.
For batch 0, loss is    3.72.
For batch 1, loss is    3.55.
For batch 2, loss is    3.21.
For batch 3, loss is    2.98.
For batch 4, loss is    3.02.
The average loss for epoch 10 is    3.02 and mean absolute error is    1.36.

Epoch 00011: Learning rate is 0.0050.
For batch 0, loss is    2.88.
For batch 1, loss is    2.88.
For batch 2, loss is    2.89.
For batch 3, loss is    2.95.
For batch 4, loss is    3.50.
The average loss for epoch 11 is    3.50 and mean absolute error is    1.44.

Epoch 00012: Learning rate is 0.0010.
For batch 0, loss is    3.26.
For batch 1, loss is    3.15.
For batch 2, loss is    3.59.
For batch 3, loss is    3.46.
For batch 4, loss is    3.44.
The average loss for epoch 12 is    3.44 and mean absolute error is    1.44.

Epoch 00013: Learning rate is 0.0010.
For batch 0, loss is    3.09.
For batch 1, loss is    3.34.
For batch 2, loss is    3.34.
For batch 3, loss is    3.31.
For batch 4, loss is    3.13.
The average loss for epoch 13 is    3.13 and mean absolute error is    1.43.

Epoch 00014: Learning rate is 0.0010.
For batch 0, loss is    3.69.
For batch 1, loss is    3.23.
For batch 2, loss is    3.09.
For batch 3, loss is    3.39.
For batch 4, loss is    3.19.
The average loss for epoch 14 is    3.19 and mean absolute error is    1.37.

<tensorflow.python.keras.callbacks.History at 0x7fa31c4a5c18>

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へのロギング、モデルの保存、TensorBoardでの指標の視覚化などが含まれます。