Training tensorflow RNN with large datasets
<p>I'm training an RNN in tensorflow. The function used is "rnn" from <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/rnn.py" rel="nofollow">https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/rnn.py</a>. </p>

<pre><code>outputs, states = rnn.rnn(cell, inputs, initial_state=initial_state, sequence_length=seq_length)
</code></pre>

<p>The reason I use this function is because my data sequences are of variable lengths. This function expects all data to be loaded at once. Since my data doesn't fit in to memory all at once, I need to load data piece by piece. Any pointers on how it can be done would be highly appreciated.</p>

<p>Thanks </p>