Resizing Layer In Tensorflow Crashes Because Of Different Picture Shapes

I'm new to TensorFlow. I have a image classification problem with different image sizes. In the documentation I read about how it is beneficial to do the resizing inside the model

Solution 1:

The error is that you cannot batch elements of different sizes. There's unfortunately no way around that. The documentation specifies that preprocessing inside the model is useful at inference (i.e. when you call model.predict()).

The key benefit to doing this is that it makes your model portable [...] When all data preprocessing is part of the model, other people can load and use your model without having to be aware of how each feature is expected to be encoded & normalized. Your inference model will be able to process raw images or raw structured data, and will not require users of the model to be aware of the details of e.g. the tokenization scheme used for text, the indexing scheme used for categorical features, whether image pixel values are normalized to [-1, +1] or to [0, 1], etc.

During training, if you want to use a batch size of >1, you will need to do the preprocessing yourself, if the images have different sizes. You can do that with tf.data.Dataset.map().