list of data augmentation hyperparameters used for training
sample_no (int): sample no among training with same hyperparameters
reward (float): reward is basically last n validation accuracy before overfitting
history (dict): history returned by keras.model.fit()</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="notebook.Notebook.save">
<code class="descname">save</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/notebook.html#Notebook.save">