<p dir="auto">Here are the method of <code class="notranslate">fit</code> and <code class="notranslate">fit_generator</code>:</p>
<div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="    def fit(self,
            x=None,
            y=None,
            batch_size=None,
            epochs=1,
            verbose=1,
            callbacks=None,
            validation_split=0.,
            validation_data=None,
            shuffle=True,
            class_weight=None,
            sample_weight=None,
            initial_epoch=0,
            steps_per_epoch=None,
            validation_steps=None,
            **kwargs):"><pre class="notranslate">    def fit(self,
            x=None,
            y=None,
            batch_size=None,
            epochs=1,
            verbose=1,
            callbacks=None,
            validation_split=0.,
            validation_data=None,
            shuffle=True,
            class_weight=None,
            sample_weight=None,
            initial_epoch=0,
            steps_per_epoch=None,
            validation_steps=None,
            <span class="pl-k">**</span>kwargs):</pre></div>
<div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="    def fit_generator(self,
                      generator,
                      steps_per_epoch=None,
                      epochs=1,
                      verbose=1,
                      callbacks=None,
                      validation_data=None,
                      validation_steps=None,
                      class_weight=None,
                      max_queue_size=10,
                      workers=1,
                      use_multiprocessing=False,
                      shuffle=True,
                      initial_epoch=0):"><pre class="notranslate">    def fit_generator(self,
                      generator,
                      steps_per_epoch=None,
                      epochs=1,
                      verbose=1,
                      callbacks=None,
                      validation_data=None,
                      validation_steps=None,
                      class_weight=None,
                      max_queue_size=10,
                      workers=1,
                      use_multiprocessing=False,
                      shuffle=True,
                      initial_epoch=0):</pre></div>
<p dir="auto">The interface is almost the same while they are totally implemented separately, why not wrap <code class="notranslate">fit</code> with <code class="notranslate">fit_generator + IndexArrayGenerator</code>? Similarly, there are also <code class="notranslate">evaluate_generator</code>, etc.</p>
<p dir="auto">I'm using Keras to train a model with regularization.<br>
On each epoch, the loss is printed but it's the sum of both the loss and regularization term. How can I get it to print those separately instead of their sum?</p>