title: why val_loss and val_accuracy not showing in epochs
body: <p>I'm trying to classify images whether they're cats,dogs or pandas. the data contains all of images (cats + dogs + pandas) and the labels contains the labels of them but somehow when i fit the data to the model, the <code>val_loss</code> and <code>val_accuracy</code> does not show up, the only metrics shown in each epochs are <code>loss</code> and <code>accuracy</code>. I have no clue why it's not showing up but i have feeling that it's because i don't pass <code>validation_data</code> so i passed <code>X_test.all()</code> into <code>validation_data</code> but the <code>val_loss</code> and <code>val_accuracy</code> still does not show up, what should i do?</p>
  <pre><code>data = np.array(data, dtype=&quot;float&quot;) / 255.0
  labels = np.array(labels)
  
  X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2)
  
  model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(32, (2,2), activation = 'relu', input_shape= (height, width, n_channels)),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(64,(2,2), activation= 'relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(128,(2,2), activation= 'relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(256,(2,2), activation= 'relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(512, activation= 'relu'),
    tf.keras.layers.Dense(3, activation= 'softmax')
  ])
  
  model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
  
  y_train = np_utils.to_categorical(y_train, 3)
  
  model.fit(X_train, y_train, batch_size=32, epochs=25, verbose=1)
  </code></pre>