keras.callbacks.TensorBoard(log_dir='./Graph',

                     histogram_freq= 0 ,

                     write_graph=True,

                 write_images=True)

 tbCallBack = keras.callbacks.TensorBoard(log_dir='./Graph',

                                          histogram_freq= 0,

                                          write_graph=True,

                                          write_images=True)

 …

 …

 model.compile(optimizer=optim,

               loss=MultiboxLoss(NUM_CLASSES, neg_pos_ratio=2.0).compute_loss, metrics=['accuracy'])

 nb_epoch = 30

 history = model.fit_generator(gen.generate(True), gen.train_batches,

                               nb_epoch, verbose=1,

                              callbacks=[tbCallBack],

                              validation_data=gen.generate(False),

                               nb_val_samples=gen.val_batches,

                               nb_worker=1)

tensorboard --logdir path_to_current_dir/Graph 

 # define the function

 def training_vis(hist):

     loss = hist.history['loss']

     val_loss = hist.history['val_loss']

     acc = hist.history['acc']

     val_acc = hist.history['val_acc']

     # make a figure

     fig = plt.figure(figsize=(8,4))

     # subplot loss

     ax1 = fig.add_subplot(121)

     ax1.plot(loss,label='train_loss')

     ax1.plot(val_loss,label='val_loss')

     ax1.set_xlabel('Epochs')

     ax1.set_ylabel('Loss')

     ax1.set_title('Loss on Training and Validation Data')

     ax1.legend()

     # subplot acc

     ax2 = fig.add_subplot(122)

     ax2.plot(acc,label='train_acc')

     ax2.plot(val_acc,label='val_acc')

     ax2.set_xlabel('Epochs')

     ax2.set_ylabel('Accuracy')

     ax2.set_title('Accuracy  on Training and Validation Data')

     ax2.legend()

     plt.tight_layout()

 # train the model

 hist = model.fit(...)

 # call the function

 training_vis(hist)