Keras history callback loss does not match with console output of loss

I am training a CNN in Keras at the moment.Now I want to log the history of the training process for later visualizations, which I do with:

history_callback = model.fit(train_generator,      steps_per_epoch=EPOCH_STEP_TRAIN,      validation_data=test_generator,      validation_steps=EPOCH_STEP_TEST,      epochs=NUM_OF_EPOCHS,      callbacks=callbacks)val_loss_history = history_callback.history['val_loss']loss_history = history_callback.history['loss']numpy_val_loss_history = np.array(val_loss_history)numpy_loss_history = np.array(loss_history)np.savetxt(checkpoint_folder +"valid_loss_history.txt", numpy_val_loss_history, delimiter=",")np.savetxt(checkpoint_folder +"loss_history.txt", numpy_loss_history, delimiter=",")

The validation loss is saved correctly and matches exactly the output from the console.

But the loss values, which I store, do not match the output values from the console, while training. See here:

121/121 [==============================] - 61s 438ms/step - loss: 0.9004 - recall: 0.5097 - precision: 0.0292 - acc: 0.8391 - val_loss: 0.8893 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_acc: 0.9995Epoch 2/3121/121 [==============================] - 52s 428ms/step - loss: 0.5830 - recall: 0.1916 - precision: 0.3660 - acc: 0.9898 - val_loss: 0.5422 - val_recall: 0.3007 - val_precision: 0.7646 - val_acc: 0.9996Epoch 3/3121/121 [==============================] - 52s 428ms/step - loss: 0.3116 - recall: 0.3740 - precision: 0.7848 - acc: 0.9920 - val_loss: 0.5248 - val_recall: 0.3119 - val_precision: 0.6915 - val_acc: 0.9996

And output of history_callback.history['loss'] is:

0.8124346733093262 0.4653359651565552 0.30956554412841797

My loss function is:

def dice_coef(y_true, y_pred, smooth=1e-9):    y_true_f = K.flatten(y_true)    y_pred_f = K.flatten(y_pred)    intersection = K.sum(y_true_f * y_pred_f)    return (2. * intersection + smooth) / (K.sum(y_true_f**2) + K.sum(y_pred_f**2) + smooth)def dice_loss(y_true, y_pred):    return (1-dice_coef(y_true, y_pred))

I also tried:

def dice_loss(y_true, y_pred):        return tf.reduce_mean(1-dice_coef(y_true, y_pred))

Which didn't change anything.

Is there anybody out there, that can explain this weird behavior?