► 开发者指南 / 编写自己的回调函数

编写自己的回调函数

作者：Rick Chao，Francois Chollet
创建日期 2019/03/20
上次修改日期 2023/06/25
描述：编写新的 Keras 回调函数的完整指南。

简介

回调函数是强大的工具，用于在训练、评估或推理期间自定义 Keras 模型的行为。例如，keras.callbacks.TensorBoard 用于使用 TensorBoard 可视化训练进度和结果，或者 keras.callbacks.ModelCheckpoint 用于在训练期间定期保存模型。

在本指南中，您将了解 Keras 回调函数是什么、它能做什么以及如何构建自己的回调函数。我们提供了一些简单的回调函数应用演示，帮助您入门。

设置

import numpy as np
import keras

Keras 回调函数概述

所有回调函数都继承自 keras.callbacks.Callback 类，并覆盖在训练、测试和预测的不同阶段调用的方法集。回调函数对于在训练期间查看模型的内部状态和统计信息非常有用。

您可以将回调函数列表（作为关键字参数 callbacks）传递给以下模型方法

keras.Model.fit()
keras.Model.evaluate()
keras.Model.predict()

回调函数方法概述

全局方法

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

在 fit/evaluate/predict 开始时调用。

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

在 fit/evaluate/predict 结束时调用。

训练/测试/预测的批次级方法

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

在训练/测试/预测期间处理批次之前调用。

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

在训练/测试/预测批次结束时调用。在此方法中，logs 是一个包含指标结果的字典。

时期级方法（仅限训练）

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

在训练期间时期开始时调用。

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

在训练期间时期结束时调用。

一个基本示例

让我们来看一个具体的示例。首先，导入 tensorflow 并定义一个简单的顺序 Keras 模型

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

然后，从 Keras 数据集 API 加载 MNIST 数据用于训练和测试

# Load example MNIST data and pre-process it
(x_train, y_train), (x_test, y_test) = 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]

现在，定义一个简单的自定义回调函数，用于记录

当 fit/evaluate/predict 开始和结束时
当每个时期开始和结束时
当每个训练批次开始和结束时
当每个评估（测试）批次开始和结束时
当每个推理（预测）批次开始和结束时

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))

让我们试一试

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']
 1/8 ━━[37m━━━━━━━━━━━━━━━━━━  0s 13ms/step...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: []
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step

`logs` 字典的使用

logs 字典包含批次或时期结束时的损失值和所有指标。例如，损失和平均绝对误差。

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   29.25.
Up to batch 1, the average loss is  485.36.
Up to batch 2, the average loss is  330.94.
Up to batch 3, the average loss is  250.62.
Up to batch 4, the average loss is  202.20.
Up to batch 5, the average loss is  169.51.
Up to batch 6, the average loss is  145.98.
Up to batch 7, the average loss is  128.48.
The average loss for epoch 0 is  128.48 and mean absolute error is    6.01.
Up to batch 0, the average loss is    5.10.
Up to batch 1, the average loss is    4.80.
Up to batch 2, the average loss is    4.96.
Up to batch 3, the average loss is    4.96.
Up to batch 4, the average loss is    4.82.
Up to batch 5, the average loss is    4.69.
Up to batch 6, the average loss is    4.51.
Up to batch 7, the average loss is    4.53.
The average loss for epoch 1 is    4.53 and mean absolute error is    1.72.
Up to batch 0, the average loss is    5.08.
Up to batch 1, the average loss is    4.66.
Up to batch 2, the average loss is    4.64.
Up to batch 3, the average loss is    4.72.
Up to batch 4, the average loss is    4.82.
Up to batch 5, the average loss is    4.83.
Up to batch 6, the average loss is    4.77.
Up to batch 7, the average loss is    4.72.

`self.model` 属性的使用

除了在调用其方法之一时接收日志信息外，回调函数还可以访问与当前训练/评估/推理轮次关联的模型：self.model。

以下是在回调函数中可以使用 self.model 执行的一些操作

设置 self.model.stop_training = True 以立即中断训练。
修改优化器的超参数（可作为 self.model.optimizer 使用），例如 self.model.optimizer.learning_rate。
定期保存模型。
记录每个时期结束时一些测试样本上的 model.predict() 输出，作为训练期间的健全性检查。
提取每个时期结束时中间特征的可视化效果，以监控模型随时间学习的内容。
等等。

让我们在几个示例中看看它是如何工作的。

Keras 回调函数应用示例

在最小损失时提前停止

第一个示例展示了如何创建一个 Callback，当损失达到最小值时停止训练，方法是设置属性 self.model.stop_training（布尔值）。可以选择提供一个参数 patience 来指定在达到局部最小值后我们应该等待多少个时期才能停止。

keras.callbacks.EarlyStopping 提供了更完整和通用的实现。

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().__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(f"Epoch {self.stopped_epoch + 1}: early stopping")


model = get_model()
model.fit(
    x_train,
    y_train,
    batch_size=64,
    epochs=30,
    verbose=0,
    callbacks=[LossAndErrorPrintingCallback(), EarlyStoppingAtMinLoss()],
)

Up to batch 0, the average loss is   25.57.
Up to batch 1, the average loss is  471.66.
Up to batch 2, the average loss is  322.55.
Up to batch 3, the average loss is  243.88.
Up to batch 4, the average loss is  196.53.
Up to batch 5, the average loss is  165.02.
Up to batch 6, the average loss is  142.34.
Up to batch 7, the average loss is  125.17.
Up to batch 8, the average loss is  111.83.
Up to batch 9, the average loss is  101.35.
Up to batch 10, the average loss is   92.60.
Up to batch 11, the average loss is   85.16.
Up to batch 12, the average loss is   79.02.
Up to batch 13, the average loss is   73.71.
Up to batch 14, the average loss is   69.23.
Up to batch 15, the average loss is   65.26.
The average loss for epoch 0 is   65.26 and mean absolute error is    3.89.
Up to batch 0, the average loss is    3.92.
Up to batch 1, the average loss is    4.34.
Up to batch 2, the average loss is    5.39.
Up to batch 3, the average loss is    6.58.
Up to batch 4, the average loss is   10.55.
Up to batch 5, the average loss is   19.29.
Up to batch 6, the average loss is   31.58.
Up to batch 7, the average loss is   38.20.
Up to batch 8, the average loss is   41.96.
Up to batch 9, the average loss is   41.30.
Up to batch 10, the average loss is   39.31.
Up to batch 11, the average loss is   37.09.
Up to batch 12, the average loss is   35.08.
Up to batch 13, the average loss is   33.27.
Up to batch 14, the average loss is   31.54.
Up to batch 15, the average loss is   30.00.
The average loss for epoch 1 is   30.00 and mean absolute error is    4.23.
Up to batch 0, the average loss is    5.70.
Up to batch 1, the average loss is    6.90.
Up to batch 2, the average loss is    7.74.
Up to batch 3, the average loss is    8.85.
Up to batch 4, the average loss is   12.53.
Up to batch 5, the average loss is   21.55.
Up to batch 6, the average loss is   35.70.
Up to batch 7, the average loss is   44.16.
Up to batch 8, the average loss is   44.82.
Up to batch 9, the average loss is   43.07.
Up to batch 10, the average loss is   40.51.
Up to batch 11, the average loss is   38.44.
Up to batch 12, the average loss is   36.69.
Up to batch 13, the average loss is   34.77.
Up to batch 14, the average loss is   32.97.
Up to batch 15, the average loss is   31.32.
The average loss for epoch 2 is   31.32 and mean absolute error is    4.39.
Restoring model weights from the end of the best epoch.
Epoch 3: early stopping

<keras.src.callbacks.history.History at 0x1187b7430>

学习率调度

在此示例中，我们展示了如何使用自定义回调函数在训练过程中动态更改优化器的学习率。

请参阅 callbacks.LearningRateScheduler 以获取更通用的实现。

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().__init__()
        self.schedule = schedule

    def on_epoch_begin(self, epoch, logs=None):
        if not hasattr(self.model.optimizer, "learning_rate"):
            raise ValueError('Optimizer must have a "learning_rate" attribute.')
        # Get the current learning rate from model's optimizer.
        lr = 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
        self.model.optimizer.learning_rate = scheduled_lr
        print(f"\nEpoch {epoch}: Learning rate is {float(np.array(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,
    epochs=15,
    verbose=0,
    callbacks=[
        LossAndErrorPrintingCallback(),
        CustomLearningRateScheduler(lr_schedule),
    ],
)

Epoch 0: Learning rate is 0.10000000149011612.
Up to batch 0, the average loss is   27.90.
Up to batch 1, the average loss is  439.49.
Up to batch 2, the average loss is  302.08.
Up to batch 3, the average loss is  228.83.
Up to batch 4, the average loss is  184.97.
Up to batch 5, the average loss is  155.25.
Up to batch 6, the average loss is  134.03.
Up to batch 7, the average loss is  118.29.
Up to batch 8, the average loss is  105.65.
Up to batch 9, the average loss is   95.53.
Up to batch 10, the average loss is   87.25.
Up to batch 11, the average loss is   80.33.
Up to batch 12, the average loss is   74.48.
Up to batch 13, the average loss is   69.46.
Up to batch 14, the average loss is   65.05.
Up to batch 15, the average loss is   61.31.
The average loss for epoch 0 is   61.31 and mean absolute error is    3.85.

Epoch 1: Learning rate is 0.10000000149011612.
Up to batch 0, the average loss is   57.96.
Up to batch 1, the average loss is   55.11.
Up to batch 2, the average loss is   52.81.
Up to batch 3, the average loss is   51.06.
Up to batch 4, the average loss is   50.58.
Up to batch 5, the average loss is   51.49.
Up to batch 6, the average loss is   53.24.
Up to batch 7, the average loss is   54.20.
Up to batch 8, the average loss is   54.39.
Up to batch 9, the average loss is   54.31.
Up to batch 10, the average loss is   53.83.
Up to batch 11, the average loss is   52.93.
Up to batch 12, the average loss is   51.73.
Up to batch 13, the average loss is   50.34.
Up to batch 14, the average loss is   48.94.
Up to batch 15, the average loss is   47.65.
The average loss for epoch 1 is   47.65 and mean absolute error is    4.30.

Epoch 2: Learning rate is 0.10000000149011612.
Up to batch 0, the average loss is   46.38.
Up to batch 1, the average loss is   45.16.
Up to batch 2, the average loss is   44.03.
Up to batch 3, the average loss is   43.11.
Up to batch 4, the average loss is   42.52.
Up to batch 5, the average loss is   42.32.
Up to batch 6, the average loss is   43.06.
Up to batch 7, the average loss is   44.58.
Up to batch 8, the average loss is   45.33.
Up to batch 9, the average loss is   45.15.
Up to batch 10, the average loss is   44.59.
Up to batch 11, the average loss is   43.88.
Up to batch 12, the average loss is   43.17.
Up to batch 13, the average loss is   42.40.
Up to batch 14, the average loss is   41.74.
Up to batch 15, the average loss is   41.19.
The average loss for epoch 2 is   41.19 and mean absolute error is    4.27.

Epoch 3: Learning rate is 0.05.
Up to batch 0, the average loss is   40.85.
Up to batch 1, the average loss is   40.11.
Up to batch 2, the average loss is   39.38.
Up to batch 3, the average loss is   38.69.
Up to batch 4, the average loss is   38.01.
Up to batch 5, the average loss is   37.38.
Up to batch 6, the average loss is   36.77.
Up to batch 7, the average loss is   36.18.
Up to batch 8, the average loss is   35.61.
Up to batch 9, the average loss is   35.08.
Up to batch 10, the average loss is   34.54.
Up to batch 11, the average loss is   34.04.
Up to batch 12, the average loss is   33.56.
Up to batch 13, the average loss is   33.08.
Up to batch 14, the average loss is   32.64.
Up to batch 15, the average loss is   32.25.
The average loss for epoch 3 is   32.25 and mean absolute error is    3.64.

Epoch 4: Learning rate is 0.05000000074505806.
Up to batch 0, the average loss is   31.83.
Up to batch 1, the average loss is   31.42.
Up to batch 2, the average loss is   31.05.
Up to batch 3, the average loss is   30.72.
Up to batch 4, the average loss is   30.49.
Up to batch 5, the average loss is   30.37.
Up to batch 6, the average loss is   30.15.
Up to batch 7, the average loss is   29.94.
Up to batch 8, the average loss is   29.75.
Up to batch 9, the average loss is   29.56.
Up to batch 10, the average loss is   29.27.
Up to batch 11, the average loss is   28.96.
Up to batch 12, the average loss is   28.67.
Up to batch 13, the average loss is   28.39.
Up to batch 14, the average loss is   28.11.
Up to batch 15, the average loss is   27.80.
The average loss for epoch 4 is   27.80 and mean absolute error is    3.43.

Epoch 5: Learning rate is 0.05000000074505806.
Up to batch 0, the average loss is   27.51.
Up to batch 1, the average loss is   27.25.
Up to batch 2, the average loss is   27.05.
Up to batch 3, the average loss is   26.88.
Up to batch 4, the average loss is   26.76.
Up to batch 5, the average loss is   26.60.
Up to batch 6, the average loss is   26.44.
Up to batch 7, the average loss is   26.25.
Up to batch 8, the average loss is   26.08.
Up to batch 9, the average loss is   25.89.
Up to batch 10, the average loss is   25.71.
Up to batch 11, the average loss is   25.48.
Up to batch 12, the average loss is   25.26.
Up to batch 13, the average loss is   25.03.
Up to batch 14, the average loss is   24.81.
Up to batch 15, the average loss is   24.58.
The average loss for epoch 5 is   24.58 and mean absolute error is    3.25.

Epoch 6: Learning rate is 0.01.
Up to batch 0, the average loss is   24.36.
Up to batch 1, the average loss is   24.14.
Up to batch 2, the average loss is   23.93.
Up to batch 3, the average loss is   23.71.
Up to batch 4, the average loss is   23.52.
Up to batch 5, the average loss is   23.32.
Up to batch 6, the average loss is   23.12.
Up to batch 7, the average loss is   22.93.
Up to batch 8, the average loss is   22.74.
Up to batch 9, the average loss is   22.55.
Up to batch 10, the average loss is   22.37.
Up to batch 11, the average loss is   22.19.
Up to batch 12, the average loss is   22.01.
Up to batch 13, the average loss is   21.83.
Up to batch 14, the average loss is   21.67.
Up to batch 15, the average loss is   21.50.
The average loss for epoch 6 is   21.50 and mean absolute error is    2.98.

Epoch 7: Learning rate is 0.009999999776482582.
Up to batch 0, the average loss is   21.33.
Up to batch 1, the average loss is   21.17.
Up to batch 2, the average loss is   21.01.
Up to batch 3, the average loss is   20.85.
Up to batch 4, the average loss is   20.71.
Up to batch 5, the average loss is   20.57.
Up to batch 6, the average loss is   20.41.
Up to batch 7, the average loss is   20.27.
Up to batch 8, the average loss is   20.13.
Up to batch 9, the average loss is   19.98.
Up to batch 10, the average loss is   19.83.
Up to batch 11, the average loss is   19.69.
Up to batch 12, the average loss is   19.57.
Up to batch 13, the average loss is   19.44.
Up to batch 14, the average loss is   19.32.
Up to batch 15, the average loss is   19.19.
The average loss for epoch 7 is   19.19 and mean absolute error is    2.77.

Epoch 8: Learning rate is 0.009999999776482582.
Up to batch 0, the average loss is   19.07.
Up to batch 1, the average loss is   18.95.
Up to batch 2, the average loss is   18.83.
Up to batch 3, the average loss is   18.70.
Up to batch 4, the average loss is   18.58.
Up to batch 5, the average loss is   18.46.
Up to batch 6, the average loss is   18.35.
Up to batch 7, the average loss is   18.24.
Up to batch 8, the average loss is   18.12.
Up to batch 9, the average loss is   18.01.
Up to batch 10, the average loss is   17.90.
Up to batch 11, the average loss is   17.79.
Up to batch 12, the average loss is   17.68.
Up to batch 13, the average loss is   17.58.
Up to batch 14, the average loss is   17.48.
Up to batch 15, the average loss is   17.38.
The average loss for epoch 8 is   17.38 and mean absolute error is    2.61.

Epoch 9: Learning rate is 0.005.
Up to batch 0, the average loss is   17.28.
Up to batch 1, the average loss is   17.18.
Up to batch 2, the average loss is   17.08.
Up to batch 3, the average loss is   16.99.
Up to batch 4, the average loss is   16.90.
Up to batch 5, the average loss is   16.80.
Up to batch 6, the average loss is   16.71.
Up to batch 7, the average loss is   16.62.
Up to batch 8, the average loss is   16.53.
Up to batch 9, the average loss is   16.44.
Up to batch 10, the average loss is   16.35.
Up to batch 11, the average loss is   16.26.
Up to batch 12, the average loss is   16.17.
Up to batch 13, the average loss is   16.09.
Up to batch 14, the average loss is   16.00.
Up to batch 15, the average loss is   15.92.
The average loss for epoch 9 is   15.92 and mean absolute error is    2.48.

Epoch 10: Learning rate is 0.004999999888241291.
Up to batch 0, the average loss is   15.84.
Up to batch 1, the average loss is   15.76.
Up to batch 2, the average loss is   15.68.
Up to batch 3, the average loss is   15.61.
Up to batch 4, the average loss is   15.53.
Up to batch 5, the average loss is   15.45.
Up to batch 6, the average loss is   15.37.
Up to batch 7, the average loss is   15.29.
Up to batch 8, the average loss is   15.23.
Up to batch 9, the average loss is   15.15.
Up to batch 10, the average loss is   15.08.
Up to batch 11, the average loss is   15.00.
Up to batch 12, the average loss is   14.93.
Up to batch 13, the average loss is   14.86.
Up to batch 14, the average loss is   14.79.
Up to batch 15, the average loss is   14.72.
The average loss for epoch 10 is   14.72 and mean absolute error is    2.37.

Epoch 11: Learning rate is 0.004999999888241291.
Up to batch 0, the average loss is   14.65.
Up to batch 1, the average loss is   14.58.
Up to batch 2, the average loss is   14.52.
Up to batch 3, the average loss is   14.45.
Up to batch 4, the average loss is   14.39.
Up to batch 5, the average loss is   14.33.
Up to batch 6, the average loss is   14.26.
Up to batch 7, the average loss is   14.20.
Up to batch 8, the average loss is   14.14.
Up to batch 9, the average loss is   14.08.
Up to batch 10, the average loss is   14.02.
Up to batch 11, the average loss is   13.96.
Up to batch 12, the average loss is   13.90.
Up to batch 13, the average loss is   13.84.
Up to batch 14, the average loss is   13.78.
Up to batch 15, the average loss is   13.72.
The average loss for epoch 11 is   13.72 and mean absolute error is    2.27.

Epoch 12: Learning rate is 0.001.
Up to batch 0, the average loss is   13.67.
Up to batch 1, the average loss is   13.60.
Up to batch 2, the average loss is   13.55.
Up to batch 3, the average loss is   13.49.
Up to batch 4, the average loss is   13.44.
Up to batch 5, the average loss is   13.38.
Up to batch 6, the average loss is   13.33.
Up to batch 7, the average loss is   13.28.
Up to batch 8, the average loss is   13.22.
Up to batch 9, the average loss is   13.17.
Up to batch 10, the average loss is   13.12.
Up to batch 11, the average loss is   13.07.
Up to batch 12, the average loss is   13.02.
Up to batch 13, the average loss is   12.97.
Up to batch 14, the average loss is   12.92.
Up to batch 15, the average loss is   12.87.
The average loss for epoch 12 is   12.87 and mean absolute error is    2.19.

Epoch 13: Learning rate is 0.0010000000474974513.
Up to batch 0, the average loss is   12.82.
Up to batch 1, the average loss is   12.77.
Up to batch 2, the average loss is   12.72.
Up to batch 3, the average loss is   12.68.
Up to batch 4, the average loss is   12.63.
Up to batch 5, the average loss is   12.58.
Up to batch 6, the average loss is   12.53.
Up to batch 7, the average loss is   12.49.
Up to batch 8, the average loss is   12.45.
Up to batch 9, the average loss is   12.40.
Up to batch 10, the average loss is   12.35.
Up to batch 11, the average loss is   12.30.
Up to batch 12, the average loss is   12.26.
Up to batch 13, the average loss is   12.22.
Up to batch 14, the average loss is   12.17.
Up to batch 15, the average loss is   12.13.
The average loss for epoch 13 is   12.13 and mean absolute error is    2.12.

Epoch 14: Learning rate is 0.0010000000474974513.
Up to batch 0, the average loss is   12.09.
Up to batch 1, the average loss is   12.05.
Up to batch 2, the average loss is   12.01.
Up to batch 3, the average loss is   11.97.
Up to batch 4, the average loss is   11.92.
Up to batch 5, the average loss is   11.88.
Up to batch 6, the average loss is   11.84.
Up to batch 7, the average loss is   11.80.
Up to batch 8, the average loss is   11.76.
Up to batch 9, the average loss is   11.72.
Up to batch 10, the average loss is   11.68.
Up to batch 11, the average loss is   11.64.
Up to batch 12, the average loss is   11.60.
Up to batch 13, the average loss is   11.57.
Up to batch 14, the average loss is   11.54.
Up to batch 15, the average loss is   11.50.
The average loss for epoch 14 is   11.50 and mean absolute error is    2.06.

<keras.src.callbacks.history.History at 0x168619c60>