Tensorflow-H5模型到Tflite转换错误 [英] Tensorflow - h5 model to tflite conversion error

问题描述

我已经使用预先训练的InceptionV3模型进行了学习转移，并保存了h5模型文件.之后，我就可以做出预测了. 现在，我想使用TFLiteConverter.convert()方法将h5模型转换为tflite文件，如下所示:

I've made a learning transfer using a pre-trained InceptionV3 model, and I saved the h5 model file. After that, I am able to make predictions. Now, I want to convert the h5 model to tflite file, using TFLiteConverter.convert() method, like this:

converter = lite.TFLiteConverter.from_keras_model_file('keras.model.h5')
tflite_model = converter.convert()

但我收到此错误:

File "from_saved_model.py", line 28, in <module>
    tflite_model = converter.convert()
  File "C:\Anaconda3\lib\site-packages\tensorflow\contrib\lite\python\lite.py", line 409, in convert
    "invalid shape '{1}'.".format(_tensor_name(tensor), shape))
ValueError: None is only supported in the 1st dimension. Tensor 'input_1' has invalid shape '[None, None, None, 3]'

我正在Windows 10 64位上运行Anaconda Python 3.6.8.预先感谢您的帮助！

I am running Anaconda Python 3.6.8 on Windows 10 64 bits. Thank you in advance for your help!

推荐答案

将模型从TensorFlow转换为TensorFlow Lite时，仅允许批量大小(索引0)为None.调用from_keras_model_file以获得有效的输入数组形状时，您应该能够使用input_shapes参数.对于InceptionV3模型，input_shapes参数通常为{'Mul' : [1,299,299,3]}.

Only the batch size (index 0) is allowed to be None when converting the model from TensorFlow to TensorFlow Lite. You should be able to use the input_shapes argument when calling from_keras_model_file to get the input array shape to be valid. For an InceptionV3 model, the input_shapes argument is often {'Mul' : [1,299,299,3]}.

TFLiteConverter.from_keras_model_file的文档可在此处.可接受的参数如下(从文档中复制):

The documentation for TFLiteConverter.from_keras_model_file is available here. The accepted parameters are as follows (copied from the documentation):

from_keras_model_file(
    cls,
    model_file,
    input_arrays=None,
    input_shapes=None,
    output_arrays=None
)

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