Deep-Learning Nan loss reasons
<p>Perhaps too general a question, but can anyone explain what would cause a Convolutional Neural Network to diverge?</p>
<p>Specifics:</p>
<p>I am using Tensorflow's iris_training model with some of my own data and keep getting</p>
<blockquote>
<p>ERROR:tensorflow:Model diverged with loss = NaN.</p>
<p>Traceback...</p>
<p>tensorflow.contrib.learn.python.learn.monitors.NanLossDuringTrainingError: NaN loss during training.</p>
</blockquote>
<p>Traceback originated with line:</p>
<pre><code> tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
                                        hidden_units=[300, 300, 300],
                                        #optimizer=tf.train.ProximalAdagradOptimizer(learning_rate=0.001, l1_regularization_strength=0.00001),                                                          
                                        n_classes=11,
                                        model_dir=&quot;/tmp/iris_model&quot;)
</code></pre>
<p>I've tried adjusting the optimizer, using a zero for learning rate, and using no optimizer. Any insights into network layers, data size, etc is appreciated.</p>