How do I show both Training loss and validation loss on the same graph in tensorboard through keras?

Question

I'm using Keras with the Tensorflow back end to train a CNN, and I'm using tensorboard to visualize the loss functions and accuracy. I would like to see the loss function of both the training data and validation data on the same graph, but I've only found ways to do so when using Tensorflow and not through keras.

Is there a way to do so?

Edit 1: I tried writing loss/acc in the Regex but instead of putting both of the graphs together it shows them side by side like so: http://imgur.com/a/oLIcL

Ive added what I use to log to tensor board:

tbCallBack=keras.callbacks.TensorBoard(log_dir='C:\\logs', histogram_freq=0, write_graph=False, write_images=True, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None)

model.fit_generator(train_generator,
                steps_per_epoch=x_train.shape[0] // batch_size,
                epochs=epochs,
validation_data=(x_test, y_test))

Answer 1

You can add a regex in the text box in the upper left corner of the Tensorboard window.

Add acc for accuracy of both train/validation data. Add lossfor the loss values. This works for me for Keras as well as Tensorflow.

Got this from this nice tutorial on TB: https://www.youtube.com/watch?v=eBbEDRsCmv4

As a code snippet I use this:

logdir = "_tf_logs/" + now.strftime("%Y%m%d-%H%M%S") + "/"
tb = TensorBoard(log_dir=logdir)
callbacks=[tb]
...
model.fit(X_train, Y_train, validation_data=val_data, epochs=10, verbose=2, callbacks=callbacks)