How do I use the Tensorboard callback of Keras?

Question

I have built a neural network with Keras. I would visualize its data by Tensorboard, therefore I have utilized:

keras.         


        

Answer 1

Here is some code:

K.set_learning_phase(1)
K.set_image_data_format('channels_last')

tb_callback = keras.callbacks.TensorBoard(
    log_dir=log_path,
    histogram_freq=2,
    write_graph=True
)
tb_callback.set_model(model)
callbacks = []
callbacks.append(tb_callback)

# Train net:
history = model.fit(
    [x_train],
    [y_train, y_train_c],
    batch_size=int(hype_space['batch_size']),
    epochs=EPOCHS,
    shuffle=True,
    verbose=1,
    callbacks=callbacks,
    validation_data=([x_test], [y_test, y_test_coarse])
).history

# Test net:
K.set_learning_phase(0)
score = model.evaluate([x_test], [y_test, y_test_coarse], verbose=0)

Basically, histogram_freq=2 is the most important parameter to tune when calling this callback: it sets an interval of epochs to call the callback, with the goal of generating fewer files on disks.

So here is an example visualization of the evolution of values for the last convolution throughout training once seen in TensorBoard, under the "histograms" tab (and I found the "distributions" tab to contain very similar charts, but flipped on the side):

In case you would like to see a full example in context, you can refer to this open-source project: https://github.com/Vooban/Hyperopt-Keras-CNN-CIFAR-100