I'm training a linear regression model. I use tf.contrib.data to prepare the dataset, shuffle it and serve it in batches:

```
  dataset = tf.contrib.data.TFRecordDataset(filename)
  dataset = dataset.map(
      _parse_function, num_threads=16, output_buffer_size=100 * batch_size)
  dataset = dataset.repeat(5)
  dataset = dataset.shuffle(buffer_size=100000)
  dataset = dataset.padded_batch(batch_size, padded_shapes=([None], [None]))
  iterator = dataset.make_initializable_iterator()
  x_inputs, y_ = iterator.get_next()

```

The following is our training loss:
![training loss](https://i.stack.imgur.com/CJryo.png)

It is very strange that at the beginning of each epoch (iteration=100k) we have a pulse in the training loss. If the training process continues, we see the same pattern at the beginning of the following epochs.