Tensorflow 错误 "has type list, but expected one of: int, long, float"

Question

本来打算用tensorflow和librosa做点什么，但是用TFRecore的时候出错了。 google后没找到答案，想在这里咨询一下。

def create_record():
writer = tf.python_io.TFRecordWriter("./music_data/train.tfrecords")
for index, class_name in enumerate(classes):
    class_path = "f:/Classical music/"+class_name+"/dataset/"
    for a in os.listdir(class_path):
        wav_path = class_path + a
        print(wav_path)
        wav,sr = librosa.load(wav_path,sr=None)
        mfcc = librosa.feature.mfcc(wav,sr,n_mfcc=128)  # is a numpy.ndarray ,with shape (128,1293)
        print(index,mfcc.shape,type(mfcc))
       # mfcc_list=[]
       # for i in range(mfcc.shape[0]):
       #    mfcc_list.append([float(x) for x in mfcc[i]])
        example = tf.train.Example(
                    feartures = tf.train.Features(feature={
                        "label":tf.train.Feature(int64_list=tf.train.Int64List(value=[index])),
                        "mfcc":tf.train.Feature(float_list=tf.train.FloatList(value=mfcc.tolist()))
                    }))
        writer.write(example.SerializeToString())
writer.close()

TypeError                                 Traceback (most recent call last)
<ipython-input-137-9bb818ee02d5> in <module>()
----> 1 create_record()

<ipython-input-136-9a256cba70a6> in create_record()
     15                         feartures = tf.train.Features(feature={
     16                             "label":tf.train.Feature(int64_list=tf.train.Int64List(value=[index])),
---> 17                             "mfcc":tf.train.Feature(float_list=tf.train.FloatList(value=mfcc.tolist()))
     18                         }))
     19             writer.write(example.SerializeToString())

TypeError: [-389.381029172618, -393.08814551655723, -404.7248725876356, -407.1006984237564, -409.22695909850626 has type list, but expected one of: int, long, float

我尝试了 https://github.com/tensorflow/tensorflow/issues/9554,but 之类的解决方案，但它对我不起作用。

谢谢！

Answer 1

MFCC 给你一个二维数组，它会相应地转换为列表列表。但是，TFRrecords 只接受 "flat" 列表作为特征值。

您可以通过将 value=mfcc.flatten() 放入 float_list 来解决这个问题。稍后，当将 TFRecord 解析为模型的输入时，您将需要再次将其重塑为 2D。如果形状总是相同的 (128, 1293)，这很容易。如果形状是可变的，您可以将 mfcc.shape 作为另一个特征放入记录中，这样您就可以 "remember" 每个示例的原始形状。例如：

example = tf.train.Example(
                features = tf.train.Features(feature={           
    "label": tf.train.Feature(int64_list=tf.train.Int64List(value=[index])),
    "mfcc": tf.train.Feature(float_list=tf.train.FloatList(value=mfcc.flatten())),
    "shape": tf.train.Feature(int64_list=tf.train.Int64List(value=mfcc.shape))
                }))

Tensorflow 错误 "has type list, but expected one of: int, long, float"

Tensorflow error "has type list, but expected one of: int, long, float"

python

machine-learning

tensorflow

librosa