Bantuan melindungi Great Barrier Reef dengan TensorFlow pada Kaggle Bergabung Tantangan

Menulis panggilan balik Anda sendiri

Lihat di TensorFlow.org Jalankan di Google Colab Lihat sumber di GitHub Unduh buku catatan

pengantar

Callback adalah alat yang ampuh untuk menyesuaikan perilaku model Keras selama pelatihan, evaluasi, atau inferensi. Contohnya termasuk tf.keras.callbacks.TensorBoard untuk memvisualisasikan kemajuan pelatihan dan hasil dengan TensorBoard, atau tf.keras.callbacks.ModelCheckpoint untuk secara berkala menyimpan model Anda selama pelatihan.

Dalam panduan ini, Anda akan mempelajari apa itu panggilan balik Keras, apa yang bisa dilakukannya, dan bagaimana Anda bisa membuatnya sendiri. Kami menyediakan beberapa demo aplikasi panggilan balik sederhana untuk membantu Anda memulai.

Mempersiapkan

import tensorflow as tf
from tensorflow import keras

Ikhtisar panggilan balik Keras

Semua callback subclass keras.callbacks.Callback kelas, dan menimpa satu set metode yang disebut pada berbagai tahap pelatihan, pengujian, dan memprediksi. Callback berguna untuk mendapatkan tampilan status internal dan statistik model selama pelatihan.

Anda dapat melewati daftar callback (sebagai argumen kata kunci callbacks ) dengan metode Model berikut:

Ikhtisar metode panggilan balik

Metode global

on_(train|test|predict)_begin(self, logs=None)

Disebut pada awal fit / evaluate / predict .

on_(train|test|predict)_end(self, logs=None)

Disebut pada akhir fit / evaluate / predict .

Metode tingkat batch untuk pelatihan/pengujian/prediksi

on_(train|test|predict)_batch_begin(self, batch, logs=None)

Dipanggil tepat sebelum memproses batch selama pelatihan/pengujian/prediksi.

on_(train|test|predict)_batch_end(self, batch, logs=None)

Dipanggil di akhir pelatihan/pengujian/prediksi batch. Dalam metode ini, logs adalah dict berisi hasil metrik.

Metode tingkat zaman (khusus pelatihan)

on_epoch_begin(self, epoch, logs=None)

Disebut pada awal zaman selama pelatihan.

on_epoch_end(self, epoch, logs=None)

Disebut di akhir zaman selama pelatihan.

Contoh dasar

Mari kita lihat contoh konkretnya. Untuk memulai, mari impor tensorflow dan tentukan model Sequential Keras sederhana:

# Define the Keras model to add callbacks to
def get_model():
    model = keras.Sequential()
    model.add(keras.layers.Dense(1, input_dim=784))
    model.compile(
        optimizer=keras.optimizers.RMSprop(learning_rate=0.1),
        loss="mean_squared_error",
        metrics=["mean_absolute_error"],
    )
    return model

Kemudian, muat data MNIST untuk pelatihan dan pengujian dari API set data Keras:

# Load example MNIST data and pre-process it
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train = x_train.reshape(-1, 784).astype("float32") / 255.0
x_test = x_test.reshape(-1, 784).astype("float32") / 255.0

# Limit the data to 1000 samples
x_train = x_train[:1000]
y_train = y_train[:1000]
x_test = x_test[:1000]
y_test = y_test[:1000]

Sekarang, tentukan panggilan balik kustom sederhana yang mencatat:

  • Ketika fit / evaluate / predict dimulai & berakhir
  • Ketika setiap zaman dimulai & berakhir
  • Saat setiap batch pelatihan dimulai & berakhir
  • Ketika setiap evaluasi (tes) batch dimulai & berakhir
  • Ketika setiap inferensi (prediksi) batch dimulai & berakhir
class CustomCallback(keras.callbacks.Callback):
    def on_train_begin(self, logs=None):
        keys = list(logs.keys())
        print("Starting training; got log keys: {}".format(keys))

    def on_train_end(self, logs=None):
        keys = list(logs.keys())
        print("Stop training; got log keys: {}".format(keys))

    def on_epoch_begin(self, epoch, logs=None):
        keys = list(logs.keys())
        print("Start epoch {} of training; got log keys: {}".format(epoch, keys))

    def on_epoch_end(self, epoch, logs=None):
        keys = list(logs.keys())
        print("End epoch {} of training; got log keys: {}".format(epoch, keys))

    def on_test_begin(self, logs=None):
        keys = list(logs.keys())
        print("Start testing; got log keys: {}".format(keys))

    def on_test_end(self, logs=None):
        keys = list(logs.keys())
        print("Stop testing; got log keys: {}".format(keys))

    def on_predict_begin(self, logs=None):
        keys = list(logs.keys())
        print("Start predicting; got log keys: {}".format(keys))

    def on_predict_end(self, logs=None):
        keys = list(logs.keys())
        print("Stop predicting; got log keys: {}".format(keys))

    def on_train_batch_begin(self, batch, logs=None):
        keys = list(logs.keys())
        print("...Training: start of batch {}; got log keys: {}".format(batch, keys))

    def on_train_batch_end(self, batch, logs=None):
        keys = list(logs.keys())
        print("...Training: end of batch {}; got log keys: {}".format(batch, keys))

    def on_test_batch_begin(self, batch, logs=None):
        keys = list(logs.keys())
        print("...Evaluating: start of batch {}; got log keys: {}".format(batch, keys))

    def on_test_batch_end(self, batch, logs=None):
        keys = list(logs.keys())
        print("...Evaluating: end of batch {}; got log keys: {}".format(batch, keys))

    def on_predict_batch_begin(self, batch, logs=None):
        keys = list(logs.keys())
        print("...Predicting: start of batch {}; got log keys: {}".format(batch, keys))

    def on_predict_batch_end(self, batch, logs=None):
        keys = list(logs.keys())
        print("...Predicting: end of batch {}; got log keys: {}".format(batch, keys))

Mari kita mencobanya:

model = get_model()
model.fit(
    x_train,
    y_train,
    batch_size=128,
    epochs=1,
    verbose=0,
    validation_split=0.5,
    callbacks=[CustomCallback()],
)

res = model.evaluate(
    x_test, y_test, batch_size=128, verbose=0, callbacks=[CustomCallback()]
)

res = model.predict(x_test, batch_size=128, callbacks=[CustomCallback()])
Starting training; got log keys: []
Start epoch 0 of training; got log keys: []
...Training: start of batch 0; got log keys: []
...Training: end of batch 0; got log keys: ['loss', 'mean_absolute_error']
...Training: start of batch 1; got log keys: []
...Training: end of batch 1; got log keys: ['loss', 'mean_absolute_error']
...Training: start of batch 2; got log keys: []
...Training: end of batch 2; got log keys: ['loss', 'mean_absolute_error']
...Training: start of batch 3; got log keys: []
...Training: end of batch 3; got log keys: ['loss', 'mean_absolute_error']
Start testing; got log keys: []
...Evaluating: start of batch 0; got log keys: []
...Evaluating: end of batch 0; got log keys: ['loss', 'mean_absolute_error']
...Evaluating: start of batch 1; got log keys: []
...Evaluating: end of batch 1; got log keys: ['loss', 'mean_absolute_error']
...Evaluating: start of batch 2; got log keys: []
...Evaluating: end of batch 2; got log keys: ['loss', 'mean_absolute_error']
...Evaluating: start of batch 3; got log keys: []
...Evaluating: end of batch 3; got log keys: ['loss', 'mean_absolute_error']
Stop testing; got log keys: ['loss', 'mean_absolute_error']
End epoch 0 of training; got log keys: ['loss', 'mean_absolute_error', 'val_loss', 'val_mean_absolute_error']
Stop training; got log keys: ['loss', 'mean_absolute_error', 'val_loss', 'val_mean_absolute_error']
Start testing; got log keys: []
...Evaluating: start of batch 0; got log keys: []
...Evaluating: end of batch 0; got log keys: ['loss', 'mean_absolute_error']
...Evaluating: start of batch 1; got log keys: []
...Evaluating: end of batch 1; got log keys: ['loss', 'mean_absolute_error']
...Evaluating: start of batch 2; got log keys: []
...Evaluating: end of batch 2; got log keys: ['loss', 'mean_absolute_error']
...Evaluating: start of batch 3; got log keys: []
...Evaluating: end of batch 3; got log keys: ['loss', 'mean_absolute_error']
...Evaluating: start of batch 4; got log keys: []
...Evaluating: end of batch 4; got log keys: ['loss', 'mean_absolute_error']
...Evaluating: start of batch 5; got log keys: []
...Evaluating: end of batch 5; got log keys: ['loss', 'mean_absolute_error']
...Evaluating: start of batch 6; got log keys: []
...Evaluating: end of batch 6; got log keys: ['loss', 'mean_absolute_error']
...Evaluating: start of batch 7; got log keys: []
...Evaluating: end of batch 7; got log keys: ['loss', 'mean_absolute_error']
Stop testing; got log keys: ['loss', 'mean_absolute_error']
Start predicting; got log keys: []
...Predicting: start of batch 0; got log keys: []
...Predicting: end of batch 0; got log keys: ['outputs']
...Predicting: start of batch 1; got log keys: []
...Predicting: end of batch 1; got log keys: ['outputs']
...Predicting: start of batch 2; got log keys: []
...Predicting: end of batch 2; got log keys: ['outputs']
...Predicting: start of batch 3; got log keys: []
...Predicting: end of batch 3; got log keys: ['outputs']
...Predicting: start of batch 4; got log keys: []
...Predicting: end of batch 4; got log keys: ['outputs']
...Predicting: start of batch 5; got log keys: []
...Predicting: end of batch 5; got log keys: ['outputs']
...Predicting: start of batch 6; got log keys: []
...Predicting: end of batch 6; got log keys: ['outputs']
...Predicting: start of batch 7; got log keys: []
...Predicting: end of batch 7; got log keys: ['outputs']
Stop predicting; got log keys: []

Penggunaan logs dict

The logs dict berisi nilai kerugian, dan semua metrik pada akhir batch atau zaman. Contohnya termasuk kerugian dan kesalahan absolut rata-rata.

class LossAndErrorPrintingCallback(keras.callbacks.Callback):
    def on_train_batch_end(self, batch, logs=None):
        print(
            "Up to batch {}, the average loss is {:7.2f}.".format(batch, logs["loss"])
        )

    def on_test_batch_end(self, batch, logs=None):
        print(
            "Up to batch {}, the average loss is {:7.2f}.".format(batch, logs["loss"])
        )

    def on_epoch_end(self, epoch, logs=None):
        print(
            "The average loss for epoch {} is {:7.2f} "
            "and mean absolute error is {:7.2f}.".format(
                epoch, logs["loss"], logs["mean_absolute_error"]
            )
        )


model = get_model()
model.fit(
    x_train,
    y_train,
    batch_size=128,
    epochs=2,
    verbose=0,
    callbacks=[LossAndErrorPrintingCallback()],
)

res = model.evaluate(
    x_test,
    y_test,
    batch_size=128,
    verbose=0,
    callbacks=[LossAndErrorPrintingCallback()],
)
Up to batch 0, the average loss is   30.79.
Up to batch 1, the average loss is  459.11.
Up to batch 2, the average loss is  314.68.
Up to batch 3, the average loss is  237.97.
Up to batch 4, the average loss is  191.76.
Up to batch 5, the average loss is  160.95.
Up to batch 6, the average loss is  138.74.
Up to batch 7, the average loss is  124.85.
The average loss for epoch 0 is  124.85 and mean absolute error is    6.00.
Up to batch 0, the average loss is    5.13.
Up to batch 1, the average loss is    4.66.
Up to batch 2, the average loss is    4.71.
Up to batch 3, the average loss is    4.66.
Up to batch 4, the average loss is    4.69.
Up to batch 5, the average loss is    4.56.
Up to batch 6, the average loss is    4.77.
Up to batch 7, the average loss is    4.77.
The average loss for epoch 1 is    4.77 and mean absolute error is    1.75.
Up to batch 0, the average loss is    5.73.
Up to batch 1, the average loss is    5.04.
Up to batch 2, the average loss is    5.10.
Up to batch 3, the average loss is    5.14.
Up to batch 4, the average loss is    5.37.
Up to batch 5, the average loss is    5.24.
Up to batch 6, the average loss is    5.22.
Up to batch 7, the average loss is    5.16.

Penggunaan self.model atribut

Selain menerima informasi log ketika salah satu dari metode mereka disebut, callback memiliki akses ke model yang terkait dengan putaran saat pelatihan / evaluasi / inferensi: self.model .

Berikut adalah dari beberapa hal yang dapat Anda lakukan dengan self.model di callback:

  • Set self.model.stop_training = True untuk pelatihan segera interupsi.
  • Hyperparameters bermutasi dari optimizer (tersedia sebagai self.model.optimizer ), seperti self.model.optimizer.learning_rate .
  • Simpan model pada interval periode.
  • Merekam output dari model.predict() pada beberapa sampel uji pada akhir setiap zaman, untuk digunakan sebagai cek kewarasan selama pelatihan.
  • Ekstrak visualisasi fitur perantara di akhir setiap zaman, untuk memantau apa yang dipelajari model dari waktu ke waktu.
  • dll.

Mari kita lihat ini beraksi dalam beberapa contoh.

Contoh aplikasi panggilan balik Keras

Berhenti lebih awal dengan kerugian minimum

Contoh pertama ini menunjukkan penciptaan Callback yang berhenti pelatihan ketika minimum kerugian telah tercapai, dengan menetapkan atribut self.model.stop_training (boolean). Opsional, Anda dapat memberikan argumen patience untuk menentukan berapa banyak zaman kita harus menunggu sebelum berhenti setelah mencapai minimum lokal.

tf.keras.callbacks.EarlyStopping menyediakan implementasi yang lebih lengkap dan umum.

import numpy as np


class EarlyStoppingAtMinLoss(keras.callbacks.Callback):
    """Stop training when the loss is at its min, i.e. the loss stops decreasing.

  Arguments:
      patience: Number of epochs to wait after min has been hit. After this
      number of no improvement, training stops.
  """

    def __init__(self, patience=0):
        super(EarlyStoppingAtMinLoss, self).__init__()
        self.patience = patience
        # best_weights to store the weights at which the minimum loss occurs.
        self.best_weights = None

    def on_train_begin(self, logs=None):
        # The number of epoch it has waited when loss is no longer minimum.
        self.wait = 0
        # The epoch the training stops at.
        self.stopped_epoch = 0
        # Initialize the best as infinity.
        self.best = np.Inf

    def on_epoch_end(self, epoch, logs=None):
        current = logs.get("loss")
        if np.less(current, self.best):
            self.best = current
            self.wait = 0
            # Record the best weights if current results is better (less).
            self.best_weights = self.model.get_weights()
        else:
            self.wait += 1
            if self.wait >= self.patience:
                self.stopped_epoch = epoch
                self.model.stop_training = True
                print("Restoring model weights from the end of the best epoch.")
                self.model.set_weights(self.best_weights)

    def on_train_end(self, logs=None):
        if self.stopped_epoch > 0:
            print("Epoch %05d: early stopping" % (self.stopped_epoch + 1))


model = get_model()
model.fit(
    x_train,
    y_train,
    batch_size=64,
    steps_per_epoch=5,
    epochs=30,
    verbose=0,
    callbacks=[LossAndErrorPrintingCallback(), EarlyStoppingAtMinLoss()],
)
Up to batch 0, the average loss is   34.62.
Up to batch 1, the average loss is  405.62.
Up to batch 2, the average loss is  282.27.
Up to batch 3, the average loss is  215.95.
Up to batch 4, the average loss is  175.32.
The average loss for epoch 0 is  175.32 and mean absolute error is    8.59.
Up to batch 0, the average loss is    8.86.
Up to batch 1, the average loss is    7.31.
Up to batch 2, the average loss is    6.51.
Up to batch 3, the average loss is    6.71.
Up to batch 4, the average loss is    6.24.
The average loss for epoch 1 is    6.24 and mean absolute error is    2.06.
Up to batch 0, the average loss is    4.83.
Up to batch 1, the average loss is    5.05.
Up to batch 2, the average loss is    4.71.
Up to batch 3, the average loss is    4.41.
Up to batch 4, the average loss is    4.48.
The average loss for epoch 2 is    4.48 and mean absolute error is    1.68.
Up to batch 0, the average loss is    5.84.
Up to batch 1, the average loss is    5.73.
Up to batch 2, the average loss is    7.24.
Up to batch 3, the average loss is   10.34.
Up to batch 4, the average loss is   15.53.
The average loss for epoch 3 is   15.53 and mean absolute error is    3.20.
Restoring model weights from the end of the best epoch.
Epoch 00004: early stopping
<keras.callbacks.History at 0x7fd0843bf510>

Penjadwalan tingkat pembelajaran

Dalam contoh ini, kami menunjukkan bagaimana Callback kustom dapat digunakan untuk secara dinamis mengubah kecepatan pembelajaran pengoptimal selama pelatihan.

Lihat callbacks.LearningRateScheduler untuk implementasi yang lebih umum.

class CustomLearningRateScheduler(keras.callbacks.Callback):
    """Learning rate scheduler which sets the learning rate according to schedule.

  Arguments:
      schedule: a function that takes an epoch index
          (integer, indexed from 0) and current learning rate
          as inputs and returns a new learning rate as output (float).
  """

    def __init__(self, schedule):
        super(CustomLearningRateScheduler, self).__init__()
        self.schedule = schedule

    def on_epoch_begin(self, epoch, logs=None):
        if not hasattr(self.model.optimizer, "lr"):
            raise ValueError('Optimizer must have a "lr" attribute.')
        # Get the current learning rate from model's optimizer.
        lr = float(tf.keras.backend.get_value(self.model.optimizer.learning_rate))
        # Call schedule function to get the scheduled learning rate.
        scheduled_lr = self.schedule(epoch, lr)
        # Set the value back to the optimizer before this epoch starts
        tf.keras.backend.set_value(self.model.optimizer.lr, scheduled_lr)
        print("\nEpoch %05d: Learning rate is %6.4f." % (epoch, scheduled_lr))


LR_SCHEDULE = [
    # (epoch to start, learning rate) tuples
    (3, 0.05),
    (6, 0.01),
    (9, 0.005),
    (12, 0.001),
]


def lr_schedule(epoch, lr):
    """Helper function to retrieve the scheduled learning rate based on epoch."""
    if epoch < LR_SCHEDULE[0][0] or epoch > LR_SCHEDULE[-1][0]:
        return lr
    for i in range(len(LR_SCHEDULE)):
        if epoch == LR_SCHEDULE[i][0]:
            return LR_SCHEDULE[i][1]
    return lr


model = get_model()
model.fit(
    x_train,
    y_train,
    batch_size=64,
    steps_per_epoch=5,
    epochs=15,
    verbose=0,
    callbacks=[
        LossAndErrorPrintingCallback(),
        CustomLearningRateScheduler(lr_schedule),
    ],
)
Epoch 00000: Learning rate is 0.1000.
Up to batch 0, the average loss is   26.55.
Up to batch 1, the average loss is  435.15.
Up to batch 2, the average loss is  298.00.
Up to batch 3, the average loss is  225.91.
Up to batch 4, the average loss is  182.66.
The average loss for epoch 0 is  182.66 and mean absolute error is    8.16.

Epoch 00001: Learning rate is 0.1000.
Up to batch 0, the average loss is    7.30.
Up to batch 1, the average loss is    6.22.
Up to batch 2, the average loss is    6.76.
Up to batch 3, the average loss is    6.37.
Up to batch 4, the average loss is    5.98.
The average loss for epoch 1 is    5.98 and mean absolute error is    2.01.

Epoch 00002: Learning rate is 0.1000.
Up to batch 0, the average loss is    4.23.
Up to batch 1, the average loss is    4.56.
Up to batch 2, the average loss is    4.81.
Up to batch 3, the average loss is    4.63.
Up to batch 4, the average loss is    4.67.
The average loss for epoch 2 is    4.67 and mean absolute error is    1.73.

Epoch 00003: Learning rate is 0.0500.
Up to batch 0, the average loss is    6.24.
Up to batch 1, the average loss is    5.62.
Up to batch 2, the average loss is    5.48.
Up to batch 3, the average loss is    5.09.
Up to batch 4, the average loss is    4.68.
The average loss for epoch 3 is    4.68 and mean absolute error is    1.77.

Epoch 00004: Learning rate is 0.0500.
Up to batch 0, the average loss is    3.38.
Up to batch 1, the average loss is    3.83.
Up to batch 2, the average loss is    3.53.
Up to batch 3, the average loss is    3.64.
Up to batch 4, the average loss is    3.76.
The average loss for epoch 4 is    3.76 and mean absolute error is    1.54.

Epoch 00005: Learning rate is 0.0500.
Up to batch 0, the average loss is    3.62.
Up to batch 1, the average loss is    3.79.
Up to batch 2, the average loss is    3.75.
Up to batch 3, the average loss is    3.83.
Up to batch 4, the average loss is    4.37.
The average loss for epoch 5 is    4.37 and mean absolute error is    1.65.

Epoch 00006: Learning rate is 0.0100.
Up to batch 0, the average loss is    6.73.
Up to batch 1, the average loss is    6.13.
Up to batch 2, the average loss is    5.11.
Up to batch 3, the average loss is    4.57.
Up to batch 4, the average loss is    4.21.
The average loss for epoch 6 is    4.21 and mean absolute error is    1.61.

Epoch 00007: Learning rate is 0.0100.
Up to batch 0, the average loss is    3.37.
Up to batch 1, the average loss is    3.83.
Up to batch 2, the average loss is    3.80.
Up to batch 3, the average loss is    3.50.
Up to batch 4, the average loss is    3.31.
The average loss for epoch 7 is    3.31 and mean absolute error is    1.42.

Epoch 00008: Learning rate is 0.0100.
Up to batch 0, the average loss is    5.33.
Up to batch 1, the average loss is    4.84.
Up to batch 2, the average loss is    4.02.
Up to batch 3, the average loss is    3.87.
Up to batch 4, the average loss is    3.85.
The average loss for epoch 8 is    3.85 and mean absolute error is    1.53.

Epoch 00009: Learning rate is 0.0050.
Up to batch 0, the average loss is    1.84.
Up to batch 1, the average loss is    2.75.
Up to batch 2, the average loss is    3.16.
Up to batch 3, the average loss is    3.52.
Up to batch 4, the average loss is    3.34.
The average loss for epoch 9 is    3.34 and mean absolute error is    1.43.

Epoch 00010: Learning rate is 0.0050.
Up to batch 0, the average loss is    2.36.
Up to batch 1, the average loss is    2.91.
Up to batch 2, the average loss is    2.63.
Up to batch 3, the average loss is    2.93.
Up to batch 4, the average loss is    3.17.
The average loss for epoch 10 is    3.17 and mean absolute error is    1.36.

Epoch 00011: Learning rate is 0.0050.
Up to batch 0, the average loss is    3.32.
Up to batch 1, the average loss is    3.02.
Up to batch 2, the average loss is    2.96.
Up to batch 3, the average loss is    2.80.
Up to batch 4, the average loss is    2.92.
The average loss for epoch 11 is    2.92 and mean absolute error is    1.32.

Epoch 00012: Learning rate is 0.0010.
Up to batch 0, the average loss is    4.11.
Up to batch 1, the average loss is    3.70.
Up to batch 2, the average loss is    3.89.
Up to batch 3, the average loss is    3.76.
Up to batch 4, the average loss is    3.45.
The average loss for epoch 12 is    3.45 and mean absolute error is    1.44.

Epoch 00013: Learning rate is 0.0010.
Up to batch 0, the average loss is    3.38.
Up to batch 1, the average loss is    3.34.
Up to batch 2, the average loss is    3.26.
Up to batch 3, the average loss is    3.56.
Up to batch 4, the average loss is    3.62.
The average loss for epoch 13 is    3.62 and mean absolute error is    1.44.

Epoch 00014: Learning rate is 0.0010.
Up to batch 0, the average loss is    2.48.
Up to batch 1, the average loss is    2.38.
Up to batch 2, the average loss is    2.76.
Up to batch 3, the average loss is    2.63.
Up to batch 4, the average loss is    2.66.
The average loss for epoch 14 is    2.66 and mean absolute error is    1.29.
<keras.callbacks.History at 0x7fd08446c290>

Panggilan balik Keras bawaan

Pastikan untuk memeriksa Keras callback yang ada dengan membaca dokumentasi API . Aplikasi termasuk masuk ke CSV, menyimpan model, memvisualisasikan metrik di TensorBoard, dan banyak lagi!