深度学习模型优化器报错：

ValueError: decay is deprecated in the new Keras optimizer, pleasecheck the docstring for valid arguments, or use the legacy optimizer, e.g., tf.keras.optimizers.legacy.Adam.

报错原因：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[10], line 15
     13 model = Model(input, output) 
     14 model.summary() # 显示模型的输出
---> 15 opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, decay=0.01) # 设置优化器
     16 model.compile(optimizer=opt, # 优化器
     17               loss = 'binary_crossentropy', # 交叉熵
     18               metrics=['accuracy'])

File D:\anaconda3\lib\site-packages\keras\optimizers\adam.py:104, in Adam.__init__(self, learning_rate, beta_1, beta_2, epsilon, amsgrad, weight_decay, clipnorm, clipvalue, global_clipnorm, use_ema, ema_momentum, ema_overwrite_frequency, jit_compile, name, **kwargs)
     86 def __init__(
     87     self,
     88     learning_rate=0.001,
   (...)
    102     **kwargs
    103 ):
--> 104     super().__init__(
    105         name=name,
    106         weight_decay=weight_decay,
    107         clipnorm=clipnorm,
    108         clipvalue=clipvalue,
    109         global_clipnorm=global_clipnorm,
    110         use_ema=use_ema,
    111         ema_momentum=ema_momentum,
    112         ema_overwrite_frequency=ema_overwrite_frequency,
    113         jit_compile=jit_compile,
    114         **kwargs
    115     )
    116     self._learning_rate = self._build_learning_rate(learning_rate)
    117     self.beta_1 = beta_1

File D:\anaconda3\lib\site-packages\keras\optimizers\optimizer.py:1087, in Optimizer.__init__(self, name, weight_decay, clipnorm, clipvalue, global_clipnorm, use_ema, ema_momentum, ema_overwrite_frequency, jit_compile, **kwargs)
   1072 def __init__(
   1073     self,
   1074     name,
   (...)
   1083     **kwargs,
   1084 ):
   1085     """Create a new Optimizer."""
-> 1087     super().__init__(
   1088         name,
   1089         weight_decay,
   1090         clipnorm,
   1091         clipvalue,
   1092         global_clipnorm,
   1093         use_ema,
   1094         ema_momentum,
   1095         ema_overwrite_frequency,
   1096         jit_compile,
   1097         **kwargs,
   1098     )
   1099     self._distribution_strategy = tf.distribute.get_strategy()

File D:\anaconda3\lib\site-packages\keras\optimizers\optimizer.py:105, in _BaseOptimizer.__init__(self, name, weight_decay, clipnorm, clipvalue, global_clipnorm, use_ema, ema_momentum, ema_overwrite_frequency, jit_compile, **kwargs)
    103 self._variables = []
    104 self._create_iteration_variable()
--> 105 self._process_kwargs(kwargs)

File D:\anaconda3\lib\site-packages\keras\optimizers\optimizer.py:134, in _BaseOptimizer._process_kwargs(self, kwargs)
    132 for k in kwargs:
    133     if k in legacy_kwargs:
--> 134         raise ValueError(
    135             f"{k} is deprecated in the new Keras optimizer, please"
    136             "check the docstring for valid arguments, or use the "
    137             "legacy optimizer, e.g., "
    138             f"tf.keras.optimizers.legacy.{self.__class__.__name__}."
    139         )
    140     else:
    141         raise TypeError(
    142             f"{k} is not a valid argument, kwargs should be empty "
    143             " for `optimizer_experimental.Optimizer`."
    144         )

ValueError: decay is deprecated in the new Keras optimizer, pleasecheck the docstring for valid arguments, or use the legacy optimizer, e.g., tf.keras.optimizers.legacy.Adam.

可以看到错误源于第15行：
opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, decay=0.01) # 设置优化器
该优化器设置方法已失效，需要按报错提示更换legacy.Adam优化器.

解决方案：

1、导入模型方法修改为from tensorflow.keras.optimizers import legacy
2、Adam()优化器调用修改为legacy.Adam()，且其中的学习率参数lr必须要修改为learning_rate

from keras import layers # 导入各种层
from keras.models import Model # 导入模型
from tensorflow.keras.optimizers import legacy # 导入Adam优化器
input = layers.Input(shape=(3197, 1)) # Input
# 通过函数式API构建模型
x = layers.Conv1D(32, kernel_size=10, strides=4)(input)
x = layers.MaxPooling1D(pool_size=4, strides=2)(x)
x = layers.GRU(256, return_sequences=True)(x)
x = layers.Flatten()(x)
x = layers.Dropout(0.5)(x)
x = layers.BatchNormalization()(x)
output = layers.Dense(1, activation='sigmoid')(x) # Output
model = Model(input, output) 
model.summary() # 显示模型的输出
opt = legacy.Adam(learning_rate=0.0001, beta_1=0.9, beta_2=0.999, decay=0.01) # 设置优化器
model.compile(optimizer=opt, # 优化器
              loss = 'binary_crossentropy', # 交叉熵
              metrics=['accuracy']) # 准确率

这样模型就可以继续正常训练了。