小编mon*_*chi的帖子

嗨，我正在尝试构建一个图像输入管道。我的预处理训练数据存储在我使用以下代码行创建的 tfrecords 文件中：

def _bytes_feature(value):
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))

def _int64_feature(value):
    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))

..

img_raw = img.tostring()                                        # typeof(img) = np.Array with shape (50, 80) dtype float64
img_label_text_raw = str.encode(img_lable)
example = tf.train.Example(features=tf.train.Features(feature={
    'height': _int64_feature(height),                           #heigth (integer)
    'width': _int64_feature(width),                             #width (integer)
    'depth': _int64_feature(depth),                             #num of rgb channels (integer)
    'image_data': _bytes_feature(img_raw),                      #raw image data (byte string)
    'label_text': _bytes_feature(img_label_text_raw),           #raw image_lable_text (byte string)
    'lable': _int64_feature(lable_txt_to_int[img_lable])}))     #label index (integer)

writer.write(example.SerializeToString())

现在我尝试读取二进制数据以从中重建张量：

def read_and_decode(filename_queue):
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)

    features = tf.parse_single_example(
        serialized_example,
        # …