tf1.x到tf2.x转换的各种API问题以及各种报错问题(持续总结)

AttributeError: ‘module’ object has no attribute ‘FixedLenFeature’

在tf2.x中已经变为tf.io.FixedLenFeature

包括parse_single_example以及decode_raw在tf2.x中都转换为:

tf.io.parse_single_example和tf.io.decode_raw

absl.flags._exceptions.UnparsedFlagAccessError: Trying to access flag --xxx before flags were parsed

此时你的代码应该是基于tensorflow2.x版本

from absl import flags FLAGS=flags.FLAGS 然后一大堆的DEFINE_sting等

解决办法是在flags前加上tf.compat.v1 如下图：

‘bert-base-chinese’ is the correct path to a directory containing a config.json file

这种情况就直接从网站上下载即可

PRETRAINED_MODEL_ARCHIVE_MAP = { 'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz", 'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz", 'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz", 'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz", 'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz", 'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz", 'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz", }

如上所示，根据对应的链接直接下载好即可，速度也快 然后transformers.BertModel.from_pretrained(下载的路径)

tensorflow屏蔽WARNING信息

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"#光有这行命令不行 tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)#这行才是关键

assert x_id in tensor_dict, 'Could not compute output ’ + str(x)

这种情况应该是在model.fit中喂入训练数据或者验证数据时字段不匹配， 比如需要两个张量作为输入数据，而你只传入一个张量

keras运行报错ValueError: Graph disconnected: cannot obtain value for tensor Tensor

这种情况是因为