在pyspark中使用带dropout的Keras序列化模型

from pyspark.ml.feature import VectorAssembler
from pyspark.ml.classification import MultilayerPerceptronClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.wrappers.scikit_learn import KerasClassifier

# 假设已经准备好了训练数据集和测试数据集
train_data = spark.read.format("libsvm").load("train_data.txt")
test_data = spark.read.format("libsvm").load("test_data.txt")

def create_model():
    model = Sequential()
    model.add(Dense(64, input_dim=10, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(64, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(2, activation='softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

keras_model = KerasClassifier(build_fn=create_model, epochs=10, batch_size=32)

assembler = VectorAssembler(inputCols=train_data.columns[1:], outputCol='features')
train_data = assembler.transform(train_data)
test_data = assembler.transform(test_data)

model = keras_model.fit(train_data)
predictions = model.transform(test_data)
evaluator = MulticlassClassificationEvaluator(labelCol='label', predictionCol='prediction', metricName='accuracy')
accuracy = evaluator.evaluate(predictions)
print("Accuracy:", accuracy)