title: Deep-Learning Nan loss reasons
body: <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>