TensorFlow Addons Callbacks: TimeStopping

مشاهده در TensorFlow.org

در Google Colab اجرا شود

مشاهده منبع در GitHub

دانلود دفترچه یادداشت

بررسی اجمالی

این نوت بوک نحوه استفاده از TimeStopping Callback در افزونه های TensorFlow را نشان می دهد.

برپایی

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

وارد کردن و عادی سازی داده ها

# 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

ساخت مدل ساده 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'])

استفاده از زمان توقف ساده

# 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>