在创建和加载 .tfrecord 文件的上下文中，我遇到了以下问题：
生成 dataset.tfrecord 文件
文件夹 /Batch_manager/assets 包含一些用于生成 dataset.tfrecord 文件的 *.tif 图像：
def _save_as_tfrecord(self, path, name):
self.__filename = os.path.join(path, name + '.tfrecord')
writer = tf.python_io.TFRecordWriter(self.__filename)
print('Writing', self.__filename)
for index, img in enumerate(self.load(get_iterator=True, n_images=1)):
img = img[0]
image_raw = img.tostring()
rows = img.shape[0]
cols = img.shape[1]
try:
depth = img.shape[2]
except IndexError:
depth = 1
example = tf.train.Example(features=tf.train.Features(feature={
'height': self._int64_feature(rows),
'width': self._int64_feature(cols),
'depth': self._int64_feature(depth),
'label': self._int64_feature(int(self.target[index])),
'image_raw': self._bytes_feature(image_raw)
}))
writer.write(example.SerializeToString())
writer.close()
从 dataset.tfrecord 文件中读取
接下来，我尝试使用指向 dataset.tfrecord 文件的路径从该文件中读取：
def dataset_input_fn(self, path):
dataset = tf.contrib.data.TFRecordDataset(path)
def parser(record):
keys_to_features = {
"height": tf.FixedLenFeature((), tf.int64, default_value=""),
"width": tf.FixedLenFeature((), tf.int64, default_value=""),
"depth": tf.FixedLenFeature((), tf.int64, default_value=""),
"label": tf.FixedLenFeature((), tf.int64, default_value=""),
"image_raw": tf.FixedLenFeature((), tf.string, default_value=""),
}
print(record)
features = tf.parse_single_example(record, features=keys_to_features)
print(features)
label = features['label']
height = features['height']
width = features['width']
depth = features['depth']
image = tf.decode_raw(features['image_raw'], tf.float32)
image = tf.reshape(image, [height, width, -1])
label = tf.cast(features["label"], tf.int32)
return {"image_raw": image, "height": height, "width": width, "depth":depth, "label":label}
dataset = dataset.map(parser)
dataset = dataset.shuffle(buffer_size=10000)
dataset = dataset.batch(32)
iterator = dataset.make_one_shot_iterator()
# `features` is a dictionary in which each value is a batch of values for
# that feature; `labels` is a batch of labels.
features = iterator.get_next()
return Features
错误信息：
TypeError: 预期 int64，得到了 '' 类型的 'str' 代替。
这段代码有什么问题？我成功验证了 dataset.tfrecord 实际上包含正确的图像和元数据！