<p>According to the original publication of <a href="https://arxiv.org/pdf/1606.07792.pdf" rel="nofollow noreferrer">deep-and-wide learning</a> they use logistic loss function for joined training. More specifically, model implementation relies on cross entropy loss applied to softmax output.</p>

<p>They use weighted sum to combine log-odds from two models before applying logistic regression.</p>

<p>Loss declaration can be found in the source code <a href="https://github.com/tensorflow/tensorflow/blob/r1.3/tensorflow/python/estimator/canned/dnn_linear_combined.py" rel="nofollow noreferrer">here</a>:</p>

<pre><code>...        
head = head_lib._multi_class_head_with_softmax_cross_entropy_loss(  # pylint: disable=protected-access
              n_classes,
              weight_column=weight_column,
label_vocabulary=label_vocabulary)
</code></pre>
Tensorflow's DNNLinearCombinedClassifier prints regression loss instead of classification loss
<p>I am trying Tensorflow's <code>DNNLinearCombinedClassifier</code> (version <code>1.3</code>) on the <a href="https://www.kaggle.com/dalpozz/creditcardfraud" rel="nofollow noreferrer">Kaggle's Credit Card Fraud</a> (classification) dataset:</p>
<pre><code>m = tf.estimator.DNNLinearCombinedClassifier(model_dir='/.../model', dnn_feature_columns=deep_columns,
                                            dnn_hidden_units=[20,5])

def input_fn(df, num_epochs):
    return tf.estimator.inputs.pandas_input_fn(
        x = df,
        y = df.Class,
        batch_size = 1000,
        num_epochs = num_epochs,
        shuffle=False)
</code></pre>
<p>with model's output (here <em>df.Class</em>) as a binary feature.
Tensorflow's logs on training</p>
<pre><code>m.train(input_fn(data, 3))
</code></pre>
<p>are:</p>
<blockquote>
<p>INFO:tensorflow:<strong>loss = 532.633</strong>, step = 2566
INFO:tensorflow:global_step/sec: 37.9815 INFO:tensorflow:loss =
560.574, step = 2666 (2.635 sec) INFO:tensorflow:global_step/sec: 38.3186</p>
</blockquote>
<p>What is the loss function being used here?</p>