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مشاهده در TensorFlow.org

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معرفی

پاسخ به تماس ابزاری قدرتمند برای سفارشی کردن رفتار مدل Keras در طول آموزش، ارزیابی یا استنتاج است. مثالها عبارتند از tf.keras.callbacks.TensorBoard به تجسم پیشرفت آموزش و نتایج را با TensorBoard یا tf.keras.callbacks.ModelCheckpoint به صورت دوره ای صرفه جویی مدل خود را در طول آموزش.

در این راهنما، یاد خواهید گرفت که کال بک Keras چیست، چه کاری می تواند انجام دهد و چگونه می توانید کال بک خود را بسازید. ما چند نسخه نمایشی از برنامه های کاربردی پاسخگوی تماس ساده را برای شروع به شما ارائه می دهیم.

برپایی

import tensorflow as tf
from tensorflow import keras

مروری بر تماس‌های کراس

همه تماس مجدد زیر مجموعه keras.callbacks.Callback کلاس، و نادیده گرفتن مجموعه ای از روش به نام در مراحل مختلف آموزش، تست، و پیش بینی. تماس‌های تلفنی برای مشاهده وضعیت‌های داخلی و آمار مدل در طول آموزش مفید هستند.

شما می توانید یک لیست از تماس مجدد (به عنوان آرگومان کلمه کلیدی عبور callbacks ) به روش مدل های زیر است:

مروری بر روش های برگشت به تماس

روش های جهانی

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

نام در آغاز fit / evaluate / predict .

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

نام در پایان fit / evaluate / predict .

روش‌های دسته‌ای برای آموزش/آزمایش/پیش‌بینی

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

درست قبل از پردازش یک دسته در طول آموزش/آزمایش/پیش‌بینی تماس گرفته شده است.

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

در پایان آموزش / تست / پیش بینی یک دسته فراخوانی می شود. در این روش، logs بینی حاوی نتایج معیارهای است.

روش های دوره ای (فقط آموزش)

`on_epoch_begin(self, epoch, logs=None)`

در ابتدای یک دوره در طول تمرین نامیده می شود.

`on_epoch_end(self, epoch, logs=None)`

در پایان یک دوره در طول آموزش فراخوانی شد.

یک مثال اساسی

بیایید به یک مثال عینی نگاهی بیندازیم. برای شروع، اجازه دهید tensorflow را وارد کرده و یک مدل Keras متوالی ساده تعریف کنیم:

# 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

سپس داده‌های MNIST را برای آموزش و آزمایش از API مجموعه داده‌های Keras بارگیری کنید:

# 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]

اکنون، یک callback سفارشی ساده تعریف کنید که ثبت شود:

هنگامی که fit / evaluate / predict شروع می شود و به پایان می رسد
زمانی که هر دوره شروع می شود و به پایان می رسد
زمانی که هر دسته آموزشی شروع و به پایان می رسد
زمانی که هر دسته ارزیابی (تست) شروع و پایان می یابد
زمانی که هر دسته استنتاج (پیش‌بینی) شروع و پایان می‌یابد

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))

بیایید آن را امتحان کنیم:

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: []

استفاده از `logs` بینی

logs بینی شامل ارزش از دست دادن، و تمام معیارهای در پایان یک دسته یا مبدا. مثال شامل ضرر و میانگین خطای مطلق است.

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.

استفاده از `self.model` ویژگی

: علاوه بر دریافت اطلاعات ورود به سیستم زمانی که یکی از روش های خود را به نام، تماس دسترسی به مدل مرتبط با این دور از آموزش / ارزیابی / استنتاج دارند self.model .

در اینجا از چند از چیزهایی که شما می توانید با انجام هستند self.model در یک پاسخ به تماس:

مجموعه self.model.stop_training = True به آموزش بلافاصله وقفه.
hyperparameters جهش بهینه ساز (در دسترس به عنوان self.model.optimizer )، مانند self.model.optimizer.learning_rate .
مدل را در فواصل زمانی ذخیره کنید.
ضبط خروجی model.predict() در چند نمونه آزمون در پایان هر دوره، به عنوان بررسی سلامت عقل در طول آموزش استفاده کنید.
برای نظارت بر آنچه مدل در طول زمان یاد می‌گیرد، تجسم‌های ویژگی‌های میانی را در پایان هر دوره استخراج کنید.
و غیره.

بیایید این را در عمل در چند مثال ببینیم.

نمونه هایی از برنامه های Keras callback

توقف زودهنگام با حداقل ضرر

این مثال نشان می دهد برای اولین بار ایجاد یک Callback که متوقف می شود آموزش زمانی که حداقل از دست دادن است، رسیده است با تنظیم ویژگی self.model.stop_training (بولین). در صورت تمایل، شما می توانید یک استدلال ارائه patience مشخص کنید که چگونه بسیاری از دوره های ما باید قبل از توقف پس از حداقل محلی رسیده صبر کنید.

tf.keras.callbacks.EarlyStopping اجرای کامل تر و به طور کلی فراهم می کند.

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>

زمان بندی میزان یادگیری

در این مثال، نشان می‌دهیم که چگونه می‌توان از Callback سفارشی برای تغییر پویا نرخ یادگیری بهینه‌ساز در طول دوره آموزشی استفاده کرد.

مشاهده callbacks.LearningRateScheduler برای پیاده سازی کلی تر.

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>

پاسخ به تماس Keras داخلی

مطمئن باشید برای بررسی از تماس مجدد Keras موجود با خواندن اسناد API . برنامه‌های کاربردی شامل ورود به CSV، ذخیره مدل، تجسم معیارها در TensorBoard و موارد دیگر است!