How do I use the Tensorboard callback of Keras?

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 !

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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])

---

Change

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

to

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

and set your model

tbCallback.set_model(model)

Run in your terminal

tensorboard  --logdir Graph/

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If you are working with Keras library and want to use tensorboard to print your graphs of accuracy and other variables, Then below are the steps to follow.

step 1: Initialize the keras callback library to import tensorboard by using below command

from keras.callbacks import TensorBoard

step 2: Include the below command in your program just before "model.fit()" command.

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

Note: Use "./graph". It will generate the graph folder in your current working directory, avoid using "/graph".

step 3: Include Tensorboard callback in "model.fit()".The sample is given below.

model.fit(X_train,y_train, batch_size=batch_size, epochs=nb_epoch, verbose=1, validation_split=0.2,callbacks=[tensor_board])

step 4 : Run your code and check whether your graph folder is there in your working directory. if the above codes work correctly you will have "Graph" folder in your working directory.

step 5 : Open Terminal in your working directory and type the command below.

tensorboard --logdir ./Graph

step 6: Now open your web browser and enter the address below.

http://localhost:6006

After entering, the Tensorbaord page will open where you can see your graphs of different variables.

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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):

https://raw.githubusercontent.com/Vooban/Hyperopt-Keras-CNN-CIFAR-100/master/tensorboard_histogram_example.png

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

---

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

---

Create the Tensorboard callback:

from keras.callbacks import TensorBoard
from datetime import datetime
logDir = "./Graph/" + datetime.now().strftime("%Y%m%d-%H%M%S") + "/"
tb = TensorBoard(log_dir=logDir, histogram_freq=2, write_graph=True, write_images=True, write_grads=True)

Pass the Tensorboard callback to the fit call:

history = model.fit(X_train, y_train, epochs=200, callbacks=[tb])

When running the model, if you get a Keras error of

"You must feed a value for placeholder tensor"

try reseting the Keras session before the model creation by doing:

import keras.backend as K
K.clear_session()

---

You wrote log_dir='/Graph' did you mean ./Graph instead? You sent it to /home/user/Graph at the moment.

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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).

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There are few things.

First, not /Graph but ./Graph

Second, when you use the TensorBoard callback, always pass validation data, because without it, it wouldn't start.

Third, if you want to use anything except scalar summaries, then you should only use the fit method because fit_generator will not work. Or you can rewrite the callback to work with fit_generator.

To add callbacks, just add it to model.fit(..., callbacks=your_list_of_callbacks)

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