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

This is how you use the TensorBoard callback:

from keras.callbacks import TensorBoard

tensorboard = TensorBoard(log_dir='./logs', histogram_freq=0,
                          write_graph=True, write_images=False)
# define model
model.fit(X_train, Y_train,
          batch_size=batch_size,
          epochs=nb_epoch,
          validation_data=(X_test, Y_test),
          shuffle=True,
          callbacks=[tensorboard])

Answer 2

keras.callbacks.TensorBoard(log_dir='./Graph', histogram_freq=0,  
          write_graph=True, write_images=True)

This line creates a Callback Tensorboard object, you should capture that object and give it to the fit function of your model.

tbCallBack = keras.callbacks.TensorBoard(log_dir='./Graph', histogram_freq=0, write_graph=True, write_images=True)
...
model.fit(...inputs and parameters..., callbacks=[tbCallBack])

This way you gave your callback object to the function. It will be run during the training and will output files that can be used with tensorboard.

If you want to visualize the files created during training, run in your terminal

tensorboard --logdir path_to_current_dir/Graph 

Hope this helps !

Answer 3

If you are using google-colab simple visualization of the graph would be :

import tensorboardcolab as tb

tbc = tb.TensorBoardColab()
tensorboard = tb.TensorBoardColabCallback(tbc)


history = model.fit(x_train,# Features
                    y_train, # Target vector
                    batch_size=batch_size, # Number of observations per batch
                    epochs=epochs, # Number of epochs
                    callbacks=[early_stopping, tensorboard], # Early stopping
                    verbose=1, # Print description after each epoch
                    validation_split=0.2, #used for validation set every each epoch
                    validation_data=(x_test, y_test)) # Test data-set to evaluate the model in the end of training

Answer 4

You should check out Losswise (https://losswise.com), it has a plugin for Keras that's easier to use than Tensorboard and has some nice extra features. With Losswise you'd just use from losswise.libs import LosswiseKerasCallback and then callback = LosswiseKerasCallback(tag='my fancy convnet 1') and you're good to go (see https://docs.losswise.com/#keras-plugin).