OutOfRangeError: tensorflow iterator not reinitializing between runs

Question

I am fine-tuning an Inception model via tensorflow with the below setup, and am feeding batches tf.Dataset API. However, every time I attempt to train this model (before successfully retrieving any batches), I get an OutOfRangeError claiming that the iterator is exhausted:

Caught OutOfRangeError. Stopping Training. End of sequence
     [[node IteratorGetNext (defined at <ipython-input-8-c768436e70d8>:13)  = IteratorGetNext[output_shapes=[[?,224,224,3], [?,1]], output_types=[DT_FLOAT, DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](OneShotIterator)]]
with tf.Graph().as_default():

I created a function to feed in hard coded batches as the result of get_batch , and this runs and converges without any issues, leading me to believe that the graph and session code is working properly. I also tested the get_batch function to iterate in a session, and this causes no errors. The behavior I would expect is that restarting training (especially with reseting the notebook, etc. ) would produce a fresh iterator over the dataset.

Code to train model:

with tf.Graph().as_default():

    tf.logging.set_verbosity(tf.logging.INFO)
    images, labels = get_batch(filenames=tf_train_record_path+train_file)
    # Create the model, use the default arg scope to configure the batch norm parameters.
    with slim.arg_scope(inception.inception_v1_arg_scope()):
        logits, ax = inception.inception_v1(images, num_classes=1, is_training=True)

    # Specify the loss function:
    tf.losses.mean_squared_error(labels,logits)
    total_loss = tf.losses.get_total_loss()
    tf.summary.scalar('losses/Total_Loss', total_loss)


     # Specify the optimizer and create the train op:
    optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
    train_op = slim.learning.create_train_op(total_loss, optimizer)

    # Run the training:
    final_loss = slim.learning.train(
        train_op,
        logdir=train_dir,
        init_fn=get_init_fn(),
        number_of_steps=1)

Code to get batches using Dataset

def get_batch(filenames):
    dataset = tf.data.TFRecordDataset(filenames=filenames)

    dataset = dataset.map(parse)
    dataset = dataset.batch(2)

    iterator = dataset.make_one_shot_iterator()
    data_X, data_y = iterator.get_next()

    return data_X, data_y 

This previously asked question resembles the issue I am experiencing, however, I am not using a batch_join call. I am not if this is an issue with slim.learning.train, restoring from a checkpoint, or scope. Any help would be appreciated!

Answer 1

Your input pipeline looks ok. The problem might be with damaged TFRecords file. You can try your code with random data, or use your images as numpy arrays with tf.data.Dataset.from_tensor_slices() . Also your parse function may cause problems. Try to print your image/label with sess.run .

And I'd advise using Estimator API as train_op. It is much more convenient and slim will be deprecated soon.