回调(07)

回调是传递给模型的对象，用于在训练期间自定义该模型并扩展其行为。您可以编写自定义回调，也可以使用包含以下方法的内置 tf.keras.callbacks：

• tf.keras.callbacks.ModelCheckpoint：定期保存模型的检查点。
• tf.keras.callbacks.LearningRateScheduler：动态更改学习速率。
• tf.keras.callbacks.EarlyStopping：在验证效果不再改进时中断训练。
• tf.keras.callbacks.TensorBoard：使用 TensorBoard 监控模型的行为。

要使用 tf.keras.callbacks.Callback，请将其传递给模型的 fit 方法：

callbacks = [
  # Interrupt training if `val_loss` stops improving for over 2 epochs
  tf.keras.callbacks.EarlyStopping(patience=2, monitor='val_loss'),
  # Write TensorBoard logs to `./logs` directory
  tf.keras.callbacks.TensorBoard(log_dir='./logs')
]
model.fit(data, labels, batch_size=32, epochs=5, callbacks=callbacks,
          validation_data=(val_data, val_labels))

Train on 1000 samples, validate on 100 samples
Epoch 1/5
1000/1000 [==============================] - 0s 150us/step - loss: 11.4748 - acc: 0.1230 - val_loss: 10.9787 - val_acc: 0.1000
Epoch 2/5
1000/1000 [==============================] - 0s 78us/step - loss: 11.4730 - acc: 0.1060 - val_loss: 10.9783 - val_acc: 0.1300
Epoch 3/5
1000/1000 [==============================] - 0s 82us/step - loss: 11.4711 - acc: 0.1130 - val_loss: 10.9756 - val_acc: 0.1500
Epoch 4/5
1000/1000 [==============================] - 0s 82us/step - loss: 11.4704 - acc: 0.1050 - val_loss: 10.9772 - val_acc: 0.0900
Epoch 5/5
1000/1000 [==============================] - 0s 83us/step - loss: 11.4689 - acc: 0.1140 - val_loss: 10.9781 - val_acc: 0.1300