Tensorflow's Estimator stops training

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I am training a model using Tensorflow's Estimator and it suddenly stops training after 2600 steps after performing an evaluation. Isn't it supposed to continue training until the end of the last epoch?

def train():
    train_input_func = lambda: input_fn(mode='train')
    eval_input_func = lambda: input_fn(mode='eval')

    est_conf = tf.estimator.RunConfig(cfg.model_dir, save_checkpoints_secs=120)
    estimator = tf.estimator.Estimator(model_fn, cfg.model_dir, est_conf)

    Path(estimator.eval_dir()).mkdir(parents=True, exist_ok=True)
    train_spec = tf.estimator.TrainSpec(input_fn=train_input_func)
    eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_func, throttle_secs=120)
    tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)

if __name__ == '__main__':

And this is the input_fn function:

def input_fn(mode=None):
        data_generator = lambda: data_loader.data_generator(mode=mode)

        dataset = tf.data.Dataset.from_generator(data_generator,
                                                 output_types=(tf.int32, tf.int32),
                                                 output_shapes=([None], [None]))

        if mode is 'train':

        dataset = dataset.padded_batch(cfg.batch_size, padded_shapes=([None],[None])).prefetch(1)

        return dataset

When use tf.estimator.train_and_evaluate, to make max_steps work, you should not use repeat(1000), please use repeat(), it will repeat the input indefinitely, and will not throw OutOfRangeError.

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First You need to specify the max_stps in the TrainSpec definition like the following:

train_spec = tf.estimator.TrainSpec(input_fn=train_input_func, max_steps=num_steps_you_specify)

Second The training procedure will stop when the input_fn throws "OutOfRangeError" on which case the max_step will not work as it was designed to. So in order to make the training run through the whole epochs, you need to specify the input_fn like the folllowing:

dataset = dataset.repeat()# don't specify any number in the repeat()

Hope this will help you.

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The problem was that I did not assign dataset.shuffle(cfg.shuffle_buffer).repeat(1000). This will fix the problem:

dataset = dataset.shuffle(cfg.shuffle_buffer).repeat(1000)

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