InvalidArgumentError with model.fit in Tensorflow

Question

Image classification with CNN. When the model.fit() is called, it starts to train the model for a while and is interrupted in the middle of execution and returns an error message.

Error message as below

InvalidArgumentError: 2 root error(s) found.
  (0) Invalid argument:  Input size should match (header_size + row_size * abs_height) but they differ by 2
     [[{{node decode_image/DecodeImage}}]]
     [[IteratorGetNext]]
     [[IteratorGetNext/_4]]
  (1) Invalid argument:  Input size should match (header_size + row_size * abs_height) but they differ by 2
     [[{{node decode_image/DecodeImage}}]]
     [[IteratorGetNext]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_8873]

Function call stack:
train_function -> train_function

Update: My suggestion is to check the metadata of the dataset. It helped to fix my problem.

Answer 1

You have not to specified the parameter label_mode . In order to use SparseCategoricalCrossentropy as the loss function you need to set it to int . If you do not specify it then it is set to None as per the documentation .

You need to also specify the parameter labels to be the inferred based on the structure of the directory that you read the images from.

train_ds = tf.keras.preprocessing.image_dataset_from_directory(
  data_dir,
  labels="inferred",
  label_mode="int",
  validation_split=0.2,
  subset="training",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)
  
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
  data_dir,
  labels="inferred",
  label_mode="int",
  validation_split=0.2,
  subset="validation",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

Answer 2

I have just answered a very similar question in another thread . In fact, the underlying issue might be exactly the same. It contains an elaborate explanation of what is going on at least in my case. Long story short, one possible reason that I proved to be correct is corrupted JPEG files .