Keras difference beetween val_loss and loss during training
val_loss is the value of cost function for your cross-validation data and loss is the value of cost function for your training data. On validation data, neurons using drop out do not drop random neurons. The reason is that during training we use drop out in order to add some noise for avoiding over-fitting. During calculating cross-validation, we are in the recall phase and not in the training phase. We use all the capabilities of the network.

Thanks to one of our dear friends, I quote and explain the contents from here which are useful.


  validation_split: Float between 0 and 1. The fraction of the training
  data to be used as validation data. The model will set apart this
  fraction of the training data, will not train on it, and will evaluate
  the loss and any model metrics on this data at the end of each epoch.
  The validation data is selected from the last samples in the x and
  y data provided, before shuffling.
  
  validation_data: tuple (x_val, y_val) or tuple (x_val, y_val,
  val_sample_weights) on which to evaluate the loss and any model
  metrics at the end of each epoch. The model will not be trained on
  this data. This will override  validation_split.


As you can see

fit(self, x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None)


fit method used in Keras has a parameter named validation_split, which specifies the percentage of data used for evaluating the model which is created after each epoch. After evaluating the model using this amount of data, that will be reported by val_loss if you've set verbose to 1; moreover, as the documentation clearly specifies, you can use either validation_data or validation_split. Cross-validation data is used to investigate whether your model over-fits the data or does not. This is what we can understand whether our model has generalization capability or not.