tensorflow: ImageDataGenerator does not work with tpu

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System information

• Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Yes
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Colab
• Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
• TensorFlow installed from (source or binary): tensorflow from colab
• TensorFlow version (use command below): 1.15
• Python version: 3.6.7
• Bazel version (if compiling from source):
• GCC/Compiler version (if compiling from source):
• CUDA/cuDNN version: None
• GPU model and memory: None

You can collect some of this information using our environment capture script You can also obtain the TensorFlow version with: 1. TF 1.0: python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)" 2. TF 2.0: python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"

Describe the current behavior Currently it is not possible to use fit_generator. Instead we need to use .fit function.

Using ImageDataGenetor, I tried to fit the model but I got an issue :

AssertionError                            Traceback (most recent call last)

<ipython-input-18-46d4a00b20c5> in <module>()
      5      train_gen.flow(X_train, y_train, batch_size=batch_size), # tf.data.Dataset.from_tensor_slices((X_train[:2000], y_train[:2000])).batch(batch_size).repeat(), #
      6     steps_per_epoch=  len(X_train)//batch_size,
----> 7     epochs=3,
      8      )

2 frames

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
    725         max_queue_size=max_queue_size,
    726         workers=workers,
--> 727         use_multiprocessing=use_multiprocessing)
    728 
    729   def evaluate(self,

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_distributed.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)
    617         validation_split=validation_split,
    618         shuffle=shuffle,
--> 619         epochs=epochs)
    620     if not dist_utils.is_distributing_by_cloning(model):
    621       with model._distribution_strategy.scope():

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py in _distribution_standardize_user_data(self, x, y, sample_weight, class_weight, batch_size, validation_split, shuffle, epochs, allow_partial_batch)
   2312         x = ds.batch(batch_size, drop_remainder=drop_remainder)
   2313       else:
-> 2314         assert isinstance(x, dataset_ops.DatasetV2)
   2315         training_utils.validate_dataset_input(x, y, sample_weight,
   2316                                               validation_split)

AssertionError: 

Describe the expected behavior from the keras documentation, it should work.

Code to reproduce the issue Provide a reproducible test case that is the bare minimum necessary to generate the problem. Here the link to the colab code : https://colab.research.google.com/drive/1bdNfb127n-VI6ab9_sUTOgRzkil88a9n Other info / logs Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.

Does that mean we cannot use generator with tensorflow 1.15 ?