![]() ![]() write_images when set to true, model weights are visualized as an image in TensorBoard.write_graph dictates if the graph will be visualized in TensorBoard.In order for this to work you have to set the validation data or the validation split. Setting this to 0 means that histograms will not be computed. histogram_freq is the frequency at which to compute activation and weight histograms for layers of the model.The TensorBoard callback also takes other parameters: With that in place, you can now create the TensorBoard callback and specify the log directory using log_dir. This callback is responsible for logging events such as Activation Histograms, Metrics Summary Plots, Profiling and Training Graph Visualizations. In order to do that you first have to import the TensorBoard callback. The next step is to specify the TensorBoard callback during the model’s fit method. Log_folder = 'logs' How to use TensorBoard callback Next, load in the TensorBoard notebook extension and create a variable pointing to the log folder. ![]() (X_train, y_train), (X_test, y_test) = mnist.load_data() For that purpose, you need to build a simple image classification model. Let’s now walk through an example where you will use TensorBoard to visualize model metrics. In this section you’ll see how to do this. Running Tensorboard involves just one line of code. Log_folder = "logs/fit/" + ().strftime( "%Y%m%d-%H%M%S") How to run TensorBoard To do that, use the command below: import datetime This can be achieved by creating logs that are timestamped. If you are running multiple experiments, you might want to store all logs so that you can compare their results. ![]() You can achieve that by running this command on Google Colab !rm -rf /logs/ You might want to clear the current logs so that you can write fresh ones to the folder. In the event that you want to reload the TensorBoard extension, the command below will do the magic - no pun intended. It will read from these logs in order to display the various visualizations. This is where TensorBoard will store all the logs. Once that is done you have to set a log directory. Note that you can use it in a Jupyter Notebook or Google’s Colab. With TensorBoard installed, you can now load it into your Notebook. How to install TensorBoardīefore you can start using TensorBoard you have to install it either via pip or via conda pip install tensorboardĬonda install -c conda-forge tensorboard Using TensorBoard with Jupyter notebooks and Google Colab ![]() This section will focus on helping you understand how to use TensorBoard in your machine learning workflow. □ The Best TensorBoard Alternatives (2021 Update) ![]()
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