Replace Validation Monitors with tf.train.SessionRunHook when using Estimators : <p>I am running a DNNClassifier, for which I am monitoring accuracy while training. monitors.ValidationMonitor from contrib/learn has been working great, in my implementation I define it:</p>

<pre><code>validation_monitor = skflow.monitors.ValidationMonitor(input_fn=lambda: input_fn(A_test, Cl2_test), eval_steps=1, every_n_steps=50)
</code></pre>

<p>and then use call from:</p>

<pre><code>clf.fit(input_fn=lambda: input_fn(A, Cl2),
            steps=1000, monitors=[validation_monitor])
</code></pre>

<p>where: </p>

<pre><code>clf = tensorflow.contrib.learn.DNNClassifier(...
</code></pre>

<p>This works fine. That said, validation monitors appear to be deprecated and a similar functionality to be replaced with <code>tf.train.SessionRunHook</code>. </p>

<p>I am a newbie in TensorFlow, and it does not seem trivial to me how such a replacing implementation would look like. Any suggestion are highly appreciated. Again, I need to validate the training after a specific number of steps.
Thanks very much in advance.</p>
