“ValueError： Unknown optimizer： momentum” binary_crossentropy.请确保将此对象传递给“custom_objects”参数

问：

结合CNN-LSTM层，构建一个使用谐波搜索算法进行超参数调谐的模型。

设置参数似乎没问题，在运行时会出现与“custom-objects”相关的错误，这是 tensorflow 特有的。

ValueError：未知优化器：binary_crossentropy。请确保将此对象传递给参数。有关详细信息，请参阅 https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object。custom_objects

代码如下

import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from keras.layers import Input, Embedding, LSTM, Dense
from keras.layers import Conv1D, MaxPooling1D, Flatten
from keras.models import Model
from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error

# Define the CNN-LSTM model
inputs = Input(shape=(X_train.shape[1], X_train.shape[2]))
x = Conv1D(filters=32, kernel_size=2, activation='relu')(inputs)
x = MaxPooling1D(pool_size=1)(x)
x = Flatten()(x)
x = Embedding(input_dim=24, output_dim=128, input_length=24)(x)
x = LSTM(units=128, dropout=0.2, recurrent_dropout=0.2)(x)
outputs = Dense(1, activation='sigmoid')(x)
model = Model(inputs, outputs)

# Compile the model
model.compile(optimizer='adam', loss='binary_crossentropy',
              metrics=['accuracy'])

# Fit the model on the training data
model.fit(X_train, y_train, epochs=10, batch_size=32)

# Evaluate the model on the test data
results = model.evaluate(X_test, y_test)
print(results)

# Define the harmony search algorithm


def harmony_search(model, X_train, y_train, X_test, y_test, n_iterations=10):
    best_score = 0
    for i in range(n_iterations):
        # Randomly select a set of model parameters
        params = {
            'optimizer': np.random.choice(['adam', 'rmsprop', 'sgd']),
            'loss': 'binary_crossentropy',
            'metrics': np.random.choice(['accuracy', 'mae'])
        }

        # Compile the model with the selected parameters
        model.compile(params)
        

        # Fit the model on the training data
        model.fit(X_train, y_train, epochs=10, batch_size=32)

        # Evaluate the model on the test data
        score = model.evaluate(X_test, y_test)[1]

        # Update the best score if necessary
        if score > best_score:
            best_score = score
            best_params = params

    # Return the best set of model parameters
    return best_params


# Find the best set of model parameters using the harmony search algorithm
best_params = harmony_search(model, X_train, y_train, X_test, y_test)

# Print the best set of model parameters
print(best_params)

我尝试在类级别添加特定参数，同样的错误。

ValueError：未知优化器：binary_crossentropy。请确保将此对象传递给参数。有关详细信息，请参阅 https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object。custom_objects