title: Warning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter
body: <p>can anyone help me solve this error:</p>
  <blockquote>
  <p>Warning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use <code>zero_division</code> parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))</p>
  </blockquote>
  <p>the error appears when I add Adam's tuning parameter.</p>
  <pre><code>#tunning parameter from keras.optimizers import Adam
  optimize = Adam(learning_rate=0.00001,beta_1=0.9,beta_2=0.99)
  model.compile(optimizer=optimize,loss='categorical_crossentropy', metrics=['accuracy'])
  </code></pre>
  <p>does anyone understand the error of this code?</p>
  <pre><code>from sklearn.metrics import confusion_matrix, classification_report
  prediksi = model.predict(test_data_generator)
  y_pred = np.argmax(prediksi, axis=1)
  print(confusion_matrix(test_data_generator.classes,y_pred))
  print(classification_report(test_data_generator.classes,y_pred))
  </code></pre>
  <p>I've also tried using <code>labels=np.unique(y_pred)</code> but the results do not show the value of the <strong>accuracy</strong> of</p>