TensorFlow Addons Callbacks: TimeStopping

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Este notebook demonstrará como usar o retorno de chamada do TimeStopping nos complementos do TensorFlow.

Configurar

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 e normalizar dados

# 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

Construir um modelo simples MNIST CNN

# 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 de TimeStopping Simples

# 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>