[英]TypeError: <tf.Tensor ... has type <class 'tensorflow.python.framework.ops.EagerTensor'>, but expected one of: numbers.Real
I am writing a function to save images to TFRecord files in order to then read then using the Data API of TensorFlow. However, when trying to create a TFRecord to save it, I receive the following error message:我正在编写一个 function 以将图像保存到 TFRecord 文件,然后使用 TensorFlow 的数据 API 进行读取。但是,当尝试创建一个 TFRecord 来保存它时,我收到以下错误消息:
TypeError: <tf.Tensor ...> has type <class 'tensorflow.python.framework.ops.EagerTensor'>, but expected one of: numbers.Real
The function used to create the TFRecord is:用于创建 TFRecord 的 function 是:
def create_tfrecord(filepath, label):
image = tf.io.read_file(filepath)
image = tf.image.decode_jpeg(image, channels=1)
image = tf.image.convert_image_dtype(image, tf.float32)
image = tf.image.resize(image, [299, 299])
tfrecord = Example(
features = Features(
feature = {
'image' : Feature(float_list=FloatList(value=[image])),
'label' : Feature(int64_list=Int64List(value=[label]))
})).SerializeToString()
return tfrecord
If you need additional information, please let me know.如果您需要更多信息,请告诉我。
The problem is that image
is a tensor but you need a list of float values.问题是
image
是张量,但您需要一个浮点值列表。 Try something like this:尝试这样的事情:
import tensorflow as tf
def create_tfrecord(filepath, label):
image = tf.io.read_file(filepath)
image = tf.image.decode_jpeg(image, channels=1)
image = tf.image.convert_image_dtype(image, tf.float32)
image = tf.image.resize(image, [299, 299])
tfrecord = tf.train.Example(
features = tf.train.Features(
feature = {
'image' : tf.train.Feature(float_list=tf.train.FloatList(value=image.numpy().ravel().tolist())),
'label' : tf.train.Feature(int64_list=tf.train.Int64List(value=[label]))
})).SerializeToString()
return tfrecord
create_tfrecord('/content/result_image.png', 1)
Dummy data was created like this:虚拟数据是这样创建的:
import numpy
from PIL import Image
imarray = numpy.random.rand(300,300,3) * 255
im = Image.fromarray(imarray.astype('uint8')).convert('RGB')
im.save('result_image.png')
If you want to reproduce this example.如果你想重现这个例子。 When loading the tf-record, you just have to reshape the image to its original size.
加载 tf-record 时,只需将图像重塑为原始大小即可。
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