如何重塑TFRecord数据集以训练RNN模型？

如何解决如何重塑TFRecord数据集以训练RNN模型？

我正在尝试使用.tfrecords数据集（训练，测试）来提供RNN模型以进行训练。但是我收到有关input0大小的错误。
我认为train_data希望重塑为4个暗角，但我不确定。

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波纹管函数从.tfrecords返回具有特征和一个热编码标签的数据集。

def get_dataset(directory,num_classes=60,batch_size=32,drop_remainder=False,shuffle=False,shuffle_size=1000):
    # dictionary describing the features.
    feature_description = {
        'features': tf.io.FixedLenFeature([],tf.string),'label': tf.io.FixedLenFeature([],tf.int64)
    }

    # parse each proto and,the features within
    def _parse_feature_function(example_proto):
        features = tf.io.parse_single_example(example_proto,feature_description)
        data = tf.io.parse_tensor(features['features'],tf.float32)
        label = tf.one_hot(features['label'],num_classes)
        data = tf.reshape(data,(3,300,25,2))
        return data,label

    records = [os.path.join(directory,file) for file in os.listdir(directory) if file.endswith("tfrecord")]
    dataset = tf.data.TFRecordDataset(records,num_parallel_reads=len(records))
    dataset = dataset.map(_parse_feature_function)
    dataset = dataset.batch(batch_size,drop_remainder=drop_remainder)
    dataset = dataset.prefetch(batch_size)
    if shuffle:
        dataset = dataset.shuffle(shuffle_size)
    return dataset

if __name__ == "__main__":
    train_data = get_dataset('/TfRecords/xsub/')
    test_data = get_dataset('/TfRecords/xview/')
    print(train_data)

   # create LSTM
    verbose,epochs,batch_size = 1,100,32
    n_features=60
    n_length = 32
    model = Sequential()
    model.add(
        Timedistributed(Conv1D(filters=64,kernel_size=3,activation='relu'),input_shape=(None,n_length,n_features)))
    model.add(Timedistributed(Conv1D(filters=64,activation='relu')))
    model.add(Timedistributed(Dropout(0.5)))
    model.add(Timedistributed(MaxPooling1D(pool_size=2)))
    model.add(Timedistributed(Flatten()))
    model.add(LSTM(100))
    model.add(Dropout(0.5))
    model.add(Dense(100,activation='relu'))
    model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
    model.summary()
    history = model.fit(train_data,epochs=epochs,batch_size=batch_size,verbose=verbose,validation_data=test_data)

显示的错误：

ValueError：层顺序的输入0与层不兼容：预期ndim = 4，找到的ndim = 5。收到完整的图形：[无，3、300、25、2]