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 callbacks TensorBoard log dir   Graph   histogram freq 0                              write graph True  write images True    as explained in keras io  When I run the callback I get  lt keras callbacks TensorBoard at 0x7f9abb3898 gt   but I don t get any file in my folder  Graph   Is there something wrong in how I have used this callback

User · Answer

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

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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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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

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You wrote log dir   Graph  did you mean   Graph instead  You sent it to  home user Graph at the moment

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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

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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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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

User · Answer

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

User · Answer

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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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

[keras] How do I use the Tensorboard callback of Keras?

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