作者:Rick Chao,Francois Chollet
创建日期 2019/03/20
最后修改日期 2023/06/25
描述:编写新的 Keras 回调函数的完整指南。
回调函数是强大的工具,可以在训练、评估或推理期间自定义 Keras 模型的行为。例如,keras.callbacks.TensorBoard
用于可视化训练进度和结果,或 keras.callbacks.ModelCheckpoint
用于在训练期间定期保存模型。
在本指南中,您将学习什么是 Keras 回调函数,它能做什么以及如何构建自己的回调函数。我们将提供一些简单的回调函数应用演示,帮助您入门。
import numpy as np
import keras
所有回调函数都是 keras.callbacks.Callback
类的子类,并覆盖一组在训练、测试和预测的各个阶段调用的方法。回调函数有助于在训练期间查看模型的内部状态和统计信息。
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()
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。
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/预测一个批次结束时调用。在此方法中,logs
是一个包含指标结果的字典。
on_epoch_begin(self, epoch, logs=None)
在训练期间一个时期的开始时调用。
on_epoch_end(self, epoch, logs=None)
在训练期间一个时期的结束时调用。
让我们来看一个具体的例子。首先,让我们导入 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,它记录
fit
/evaluate
/predict
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,包括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使用 self.model
可以做的一些事情
self.model.stop_training = True
以立即中断训练。self.model.optimizer
获取),例如 self.model.optimizer.learning_rate
。model.predict()
在一些测试样本上的输出,用作训练期间的健全性检查。让我们在几个例子中看看它是如何工作的。
第一个示例展示了如何创建一个 Callback
,当达到损失的最小值时停止训练,方法是设置属性 self.model.stop_training
(布尔值)。可选地,您可以提供一个参数 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
提供了一个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>
在本示例中,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>
请务必通过阅读 API 文档 来查看现有的 Keras 回调函数。应用包括记录到 CSV、保存模型、在 TensorBoard 中可视化指标等等!