如何将numpy数组存储为tfrecord？

Z时代
2024-01-10
分类：问答

我想从numpy数组中创建tfrecord格式的数据集。我试图存储2D和3D坐标。如何将numpy数组存储为tfrecord？

2D坐标型的形状（2,10）的numpy的阵列float64 三维坐标型float64

的形状（3,10）的numpy的阵列，这是我的代码：

def _floats_feature(value): 
    return tf.train.Feature(float_list=tf.train.FloatList(value=value)) 
train_filename = 'train.tfrecords' # address to save the TFRecords file 
writer = tf.python_io.TFRecordWriter(train_filename) 
for c in range(0,1000): 
    #get 2d and 3d coordinates and save in c2d and c3d 
    feature = {'train/coord2d': _floats_feature(c2d), 
        'train/coord3d': _floats_feature(c3d)} 
    sample = tf.train.Example(features=tf.train.Features(feature=feature)) 
    writer.write(sample.SerializeToString()) 
writer.close()

当我运行此我得到的错误：

feature = {'train/coord2d': _floats_feature(c2d), 
    File "genData.py", line 19, in _floats_feature 
return tf.train.Feature(float_list=tf.train.FloatList(value=value)) 
    File "C:\Users\User\AppData\Local\Programs\Python\Python36\lib\site-packages\google\protobuf\internal\python_message.py", line 510, in init 
copy.extend(field_value) 
    File "C:\Users\User\AppData\Local\Programs\Python\Python36\lib\site-packages\google\protobuf\internal\containers.py", line 275, in extend 
new_values = [self._type_checker.CheckValue(elem) for elem in elem_seq_iter] 
    File "C:\Users\User\AppData\Local\Programs\Python\Python36\lib\site-packages\google\protobuf\internal\containers.py", line 275, in <listcomp> 
new_values = [self._type_checker.CheckValue(elem) for elem in elem_seq_iter] 
    File "C:\Users\User\AppData\Local\Programs\Python\Python36\lib\site-packages\google\protobuf\internal\type_checkers.py", line 109, in CheckValue 
raise TypeError(message) 
TypeError: array([-163.685, 240.818, -114.05 , -518.554, 107.968, 427.184, 
    157.418, -161.798, 87.102, 406.318]) has type <class 'numpy.ndarray'>, but expected one of: ((<class 'numbers.Real'>,),)

我不知道如何解决这个问题。我应该存储的功能为int64或字节？我不知道如何去做这件事，因为我对tensorflow完全陌生。任何帮助将是伟大的！感谢

回答：

tf.train.Feature的类只支持列表（或1-d阵列）使用float_list参数时。根据您的资料，您可以尝试以下方法之一：

拼合你的阵列中的数据将它传递给tf.train.Feature前：
```
def _floats_feature(value): 
    return tf.train.Feature(float_list=tf.train.FloatList(value=value.reshape(-1))) 
```
请注意，您可能需要另一个功能添加到表明这个数据应该如何重塑当你再次解析它（你可以使用为目的的int64_list功能）。