Keras 콜백

Keras로 시작하는 딥 러닝

Miguel Esteban

Data Scientist & Founder

콜백이란?

Keras의 콜백

놓치고 있던 콜백

# Training a model and saving its history
history = model.fit(X_train, y_train,
                    epochs=100,
                    metrics=['accuracy'])

print(history.history['loss'])

[0.6753975939750672, ..., 0.3155936544282096]

print(history.history['accuracy'])

[0.7030952412741525, ..., 0.8604761900220599]

놓치고 있던 콜백

# Training a model and saving its history
history = model.fit(X_train, y_train,
                    epochs=100,
                    validation_data=(X_test, y_test),
                    metrics=['accuracy'])        

print(history.history['val_loss'])

[0.7753975939750672, ..., 0.4155936544282096]

print(history.history['val_accuracy'])

[0.6030952412741525, ..., 0.7604761900220599]

히스토리 플롯

# Plot train vs test accuracy per epoch
plt.figure()

# Use the history metrics
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])

# Make it pretty
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'])
plt.show()

히스토리 플롯

조기 종료(Early Stopping)

# Import early stopping from keras callbacks
from tensorflow.keras.callbacks import EarlyStopping


# Instantiate an early stopping callback
early_stopping = EarlyStopping(monitor='val_loss', patience=5)


# Train your model with the callback
model.fit(X_train, y_train, epochs=100,
          validation_data=(X_test, y_test),
          callbacks = [early_stopping])

모델 체크포인트

# Import model checkpoint from keras callbacks
from keras.callbacks import ModelCheckpoint


# Instantiate a model checkpoint callback
model_save = ModelCheckpoint('best_model.hdf5',
                              save_best_only=True)


# Train your model with the callback
model.fit(X_train, y_train, epochs=100,
          validation_data=(X_test, y_test),
          callbacks = [model_save])

연습해 봅시다!

Keras로 시작하는 딥 러닝