类型错误：__call__() 需要 1 到 2 个位置参数，但给出了 3 个

Question

I'm trying to do mutli-label image classification and I'm trying to convert the image data set into TFRecords format.

Here is my Code:

        def _bytes_feature(value):
          if isinstance(value,type(tf.cosntant(0))):
            value = value.numpy()
          return tf.train.Feature(byte_list=tf.train.BytesList(value=[value]))
        
        def _float_feature(value):
          return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))
        
        def _int64_feature(value):
          return tf.train.Feature(int64_list = tf.train.Int64List(value=[value]))
        
        def image_example(image_string,image_name, df):
          image_shape = tf.image.decode_png(image_string).shape
          
          feature = {
          'height': _int64_feature(image_shape[0]),
          'width': _int64_feature(image_shape[1]),
          'depth': _int64_feature(image_shape[2]),
          'label_1': _int64_feature(df.loc(image_name,'Atelectasis')),
          'label_2': _int64_feature(df.loc(image_name,'Cardiomegaly')),
          'label_3': _int64_feature(df.loc(image_name,'Consolidation')),
          'label_4': _int64_feature(df.loc(image_name,'Edema')),
          'label_5': _int64_feature(df.loc(image_name,'Effusion')),
          'label_6': _int64_feature(df.loc(image_name,'Emphysema')),
          'label_7': _int64_feature(df.loc(image_name,'Fibrosis')),
          'label_8': _int64_feature(df.loc(image_name,'Hernia')),
          'label_9': _int64_feature(df.loc(image_name,'Infiltration')),
          'label_10': _int64_feature(df.loc(image_name,'Mass')),
          'label_11': _int64_feature(df.loc(image_name,'Nodule')),
          'label_12': _int64_feature(df.loc(image_name,'Pleural_Thickening')),
          'label_13': _int64_feature(df.loc(image_name,'Pneumonia')),
          'label_14': _int64_feature(df.loc(image_name,'Pneumothorax')),
          'label_15': _int64_feature(df.loc(image_name,'No Finding')),
          'image_raw': _bytes_feature(image_string),
          }
          return tf.train.example(features = tf.train.Features(feature=feature))


  

record_image = 'image.tfrecords'
with tf.io.TFRecordWriter(record_image) as write:
  for row in df.index:
    full_path = '/content/images/'+df['Image Index'][row]
    image_string = tf.io.read_file(full_path)
    image_name = pd.Series(df['Image Index'])[row]
    tf_example = image_example(image_string, image_name, df)
    write.write(tf_example.SerializeToString())

But it throws the error:

TypeError                                 Traceback (most recent call last)
<ipython-input-66-f42cc357d799> in <module>()
      5     image_string = tf.io.read_file(full_path)
      6     image_name = pd.Series(df['Image Index'])[row]
----> 7     tf_example = image_example(image_string,image_name,df)
      8     write.write(tf_example.SerializeToString())

<ipython-input-64-de0476ade753> in image_example(image_string, image_name, df)
     17   'width': _int64_feature(image_shape[1]),
     18   'depth': _int64_feature(image_shape[2]),
---> 19   'label_1': _int64_feature(df.loc(image_name,'Atelectasis')),
     20   'label_2': _int64_feature(df.loc(image_name,'Cardiomegaly')),
     21   'label_3': _int64_feature(df.loc(image_name,'Consolidation')),

TypeError: __call__() takes from 1 to 2 positional arguments but 3 were given

Q1) Is it okay if I store the features like this? Cant the models from tensorflowHub understand it. Can I "tell" the models what to expect as the input?

Q2) Why am I getting that error? Image_example clearly takes 3 arguments.

Answer 1

df.loc(image_name,'X') 

需要是：

df.loc[image_name,'X']