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개요
이 노트북은 TensorFlow 애드온에서 TimeStopping 콜백을 사용하는 방법을 보여줍니다.
설정
pip install -q -U tensorflow-addons
import tensorflow_addons as tfa
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
데이터 가져오기 및 정규화
# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# normalize data
x_train, x_test = x_train / 255.0, x_test / 255.0
간단한 MNIST CNN 모델 빌드하기
# build the model using the Sequential API
model = Sequential()
model.add(Flatten(input_shape=(28, 28)))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam',
loss = 'sparse_categorical_crossentropy',
metrics=['accuracy'])
간단한 TimeStopping 사용법
# initialize TimeStopping callback
time_stopping_callback = tfa.callbacks.TimeStopping(seconds=5, verbose=1)
# train the model with tqdm_callback
# make sure to set verbose = 0 to disable
# the default progress bar.
model.fit(x_train, y_train,
batch_size=64,
epochs=100,
callbacks=[time_stopping_callback],
validation_data=(x_test, y_test))
Epoch 1/100 938/938 [==============================] - 2s 2ms/step - loss: 0.3393 - accuracy: 0.9019 - val_loss: 0.1657 - val_accuracy: 0.9516 Epoch 2/100 938/938 [==============================] - 2s 2ms/step - loss: 0.1658 - accuracy: 0.9514 - val_loss: 0.1171 - val_accuracy: 0.9670 Epoch 3/100 938/938 [==============================] - 2s 2ms/step - loss: 0.1220 - accuracy: 0.9636 - val_loss: 0.0951 - val_accuracy: 0.9723 Timed stopping at epoch 3 after training for 0:00:05 <tensorflow.python.keras.callbacks.History at 0x7ff8b4a0a7b8>