 Ver en TensorFlow.org |  Ejecutar en Google Colab |  Ver fuente en GitHub |  Descargar cuaderno |
Visión general
Este cuaderno demostrará cómo usar la devolución de llamada TimeStopping en los complementos de TensorFlow.
Configuración
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
Importar y normalizar datos
# 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
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz 11493376/11490434 [==============================] - 0s 0us/step
Cree un modelo CNN de MNIST simple
# 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'])
Uso simple de interrupción del tiempo
# 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 [==============================] - 3s 3ms/step - loss: 0.5649 - accuracy: 0.8378 - val_loss: 0.1624 - val_accuracy: 0.9548 Epoch 2/100 938/938 [==============================] - 2s 2ms/step - loss: 0.1684 - accuracy: 0.9514 - val_loss: 0.1160 - val_accuracy: 0.9653 Timed stopping at epoch 2 after training for 0:00:05 <tensorflow.python.keras.callbacks.History at 0x7f3b947672b0>