TypeError:调用层"conv1d“时遇到的异常(键入Conv1D)

Question

我正在做一个“新闻分类”项目。其中，模型必须将给定的文本分类为业务、娱乐、政治、体育和技术。。

我正在使用TensorFlow==2.7.0上的谷歌Colab。我训练了7种不同的模特。之后，对其进行训练和预测。与所有模型相比，Conv1d表现最好。保存了model_2.save('saved_model/my_model').It的表现最好的模型直到现在都做得很好。

但是，当我想使用代码加载保存的模型时

然后，loaded_model = tf.keras.models.load_model('saved_model/my_model')将得到以下异常：

TypeError                                 Traceback (most recent call last)
<ipython-input-129-c92edaf0db7f> in <module>()
----> 1 load_model = tf.keras.models.load_model('saved_model/my_model')
      2 # load_model.preditct(val_sentences)

1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape, allow_broadcast)
    547       str_values = [compat.as_bytes(x) for x in proto_values]
    548     except TypeError:
--> 549       raise TypeError(f"Failed to convert elements of {values} to Tensor. "
    550                       "Consider casting elements to a supported type. See "
    551                       "https://www.tensorflow.org/api_docs/python/tf/dtypes "

TypeError: Exception encountered when calling layer "conv1d" (type Conv1D).

Failed to convert elements of tf.RaggedTensor(values=tf.RaggedTensor(values=Tensor("Placeholder:0", shape=(None, 128), dtype=float32), row_splits=Tensor("Placeholder_1:0", shape=(None,), dtype=int64)), row_splits=Tensor("conv1d/Conv1D/RaggedExpandDims/RaggedFromUniformRowLength/RowPartitionFromUniformRowLength/mul:0", shape=(None,), dtype=int64)) to Tensor. Consider casting elements to a supported type. See https://www.tensorflow.org/api_docs/python/tf/dtypes for supported TF dtypes.

Call arguments received:
  • inputs=tf.RaggedTensor(values=Tensor("Placeholder:0", shape=(None, 128), dtype=float32), row_splits=Tensor("Placeholder_1:0", shape=(None,), dtype=int64))

Answer 1

看起来您需要在文件名上添加.h5。您需要将.h5添加到文件名的末尾。而不是：

model_2.save('saved_model/my_model')尝试

model_2.save('saved_model/my_model.h5')
#Notice the .h5 -------------------^

您还需要将loaded_model = tf.keras.models.load_model('saved_model/my_model')更改为loaded_model = tf.keras.models.load_model('saved_model/my_model.h5')

Answer 2

它用tensorflow 2.9修好了。更新tf版本将有助于提供更多信息：https://github.com/tensorflow/tensorflow/commit/d3b6494e82085a397bad2260a43ea6769aa20f5d