我的模型使用预处理数据来预测客户是私人客户还是非私人客户。预处理步骤包括使用如feature_column.bucketized_column(…), feature_column.embedding_column(…)等操作。训练完成后，我尝试保存模型，但遇到了以下错误：
File “h5py_objects.pyx”, line 54, in h5py._objects.with_phil.wrapper
File “h5py_objects.pyx”, line 55, in h5py._objects.with_phil.wrapper
File “h5py\h5o.pyx”, line 202, in h5py.h5o.link
OSError: Unable to create link (name already exists)
我尝试了以下方法来解决这个问题：
- 我尝试了排除优化器，如这里提到的：https://github.com/tensorflow/tensorflow/issues/27688。
- 我尝试了不同版本的TensorFlow，如2.2和2.3。
- 我尝试了重新安装h5py，如这里提到的：RuntimeError: Unable to create link (name already exists) when I append hdf5 file?。
但这些方法都没有成功！
以下是模型的相关代码：
(feature_columns, train_ds, val_ds, test_ds) = preprocessing.getPreProcessedDatasets(args.data, args.zip, args.batchSize)feature_layer = tf.keras.layers.DenseFeatures(feature_columns, trainable=False)model = tf.keras.models.Sequential([ feature_layer, tf.keras.layers.Dense(1, activation=tf.nn.sigmoid) ])model.compile(optimizer='sgd', loss='binary_crossentropy', metrics=['accuracy'])paramString = "Arg-e{}-b{}-z{}".format(args.epoch, args.batchSize, bucketSizeGEO)...model.fit(train_ds, validation_data=val_ds, epochs=args.epoch, callbacks=[tensorboard_callback])model.summary()loss, accuracy = model.evaluate(test_ds)print("Accuracy", accuracy)paramString = paramString + "-a{:.4f}".format(accuracy)outputName = "logReg" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S") + paramStrinif args.saveModel: filepath = "./saved_models/" + outputName + ".h5" model.save(filepath, save_format='h5')在预处理模块中调用的函数：
def getPreProcessedDatasets(filepath, zippath, batch_size, bucketSizeGEO): print("start preprocessing...") path = filepath data = pd.read_csv(path, dtype={ "NAME1": np.str_, "NAME2": np.str_, "EMAIL1": np.str_, "ZIP": np.str_, "STREET": np.str_, "LONGITUDE":np.floating, "LATITUDE": np.floating, "RECEIVERTYPE": np.int64}) feature_columns = [] data = data.fillna("NaN") data = __preProcessName(data) data = __preProcessStreet(data) train, test = train_test_split(data, test_size=0.2, random_state=0) train, val = train_test_split(train, test_size=0.2, random_state=0) train_ds = __df_to_dataset(train, batch_size=batch_size) val_ds = __df_to_dataset(val, shuffle=False, batch_size=batch_size) test_ds = __df_to_dataset(test, shuffle=False, batch_size=batch_size) __buildFeatureColums(feature_columns, data, zippath, bucketSizeGEO, True) print("preprocessing completed") return (feature_columns, train_ds, val_ds, test_ds)调用不同特征的预处理函数：
def __buildFeatureColums(feature_columns, data, zippath, bucketSizeGEO, addCrossedFeatures): feature_columns.append(__getFutureColumnLon(bucketSizeGEO)) feature_columns.append(__getFutureColumnLat(bucketSizeGEO)) (namew1_one_hot, namew2_one_hot) = __getFutureColumnsName(__getNumberOfWords(data, 'NAME1PRO')) feature_columns.append(namew1_one_hot) feature_columns.append(namew2_one_hot) feature_columns.append(__getFutureColumnStreet(__getNumberOfWords(data, 'STREETPRO'))) feature_columns.append(__getFutureColumnZIP(2223, zippath)) if addCrossedFeatures: feature_columns.append(__getFutureColumnCrossedNames(100)) feature_columns.append(__getFutureColumnCrossedZIPStreet(100, 2223, zippath))与嵌入相关的函数：
def __getFutureColumnsName(name_num_words): vocabulary_list = np.arange(0, name_num_words + 1, 1).tolist() namew1_voc = tf.feature_column.categorical_column_with_vocabulary_list( key='NAME1W1', vocabulary_list=vocabulary_list, dtype=tf.dtypes.int64) namew2_voc = tf.feature_column.categorical_column_with_vocabulary_list( key='NAME1W2', vocabulary_list=vocabulary_list, dtype=tf.dtypes.int64) dim = __getNumberOfDimensions(name_num_words) namew1_embedding = feature_column.embedding_column(namew1_voc, dimension=dim) namew2_embedding = feature_column.embedding_column(namew2_voc, dimension=dim) return (namew1_embedding, namew2_embedding)def __getFutureColumnStreet(street_num_words): vocabulary_list = np.arange(0, street_num_words + 1, 1).tolist() street_voc = tf.feature_column.categorical_column_with_vocabulary_list( key='STREETW', vocabulary_list=vocabulary_list, dtype=tf.dtypes.int64) dim = __getNumberOfDimensions(street_num_words) street_embedding = feature_column.embedding_column(street_voc, dimension=dim) return street_embeddingdef __getFutureColumnZIP(zip_num_words, zippath): zip_voc = feature_column.categorical_column_with_vocabulary_file( key='ZIP', vocabulary_file=zippath, vocabulary_size=zip_num_words, default_value=0) dim = __getNumberOfDimensions(zip_num_words) zip_embedding = feature_column.embedding_column(zip_voc, dimension=dim) return zip_embedding回答：
在以h5格式保存模型时出现的错误OSError: Unable to create link (name already exists)是由一些重复的变量名称引起的。通过for i, w in enumerate(model.weights): print(i, w.name)检查发现，它们是嵌入权重的名称。
通常，在构建feature_column时，传递给每个特征列的不同key将用于构建不同的变量name。这在TF 2.1中正常工作，但在TF 2.2和2.3中出现问题，据说在TF 2.4夜间版本中已修复。
