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独自のコールバックを書く

TensorFlow.orgで表示 GoogleColab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。

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。