%tensorboard -logdir logs/fitĪ brief overview of the visualizations created in this example and the dashboards (tabs in top navigation bar) where they can be found: On the command line, run the same command without '%'. In notebooks, use the %tensorboard line magic. The two interfaces are generally the same. Start TensorBoard through the command line or within a notebook experience. Tensorboard_callback = tf.(log_dir=log_dir, histogram_freq=1) Place the logs in a timestamped subdirectory to allow easy selection of different training runs. Additionally, enable histogram computation every epoch with histogram_freq=1 (this is off by default)

When training with Keras's Model.fit(), adding the tf. callback ensures that logs are created and stored. (x_train, y_train),(x_test, y_test) = mnist.load_data() Using the MNIST dataset as the example, normalize the data and write a function that creates a simple Keras model for classifying the images into 10 classes. # Clear any logs from previous runs rm -rf. # Load the TensorBoard notebook extension The remaining guides in this website provide more details on specific capabilities, many of which are not included here. This quickstart will show how to quickly get started with TensorBoard. It enables tracking experiment metrics like loss and accuracy, visualizing the model graph, projecting embeddings to a lower dimensional space, and much more. TensorBoard is a tool for providing the measurements and visualizations needed during the machine learning workflow. In machine learning, to improve something you often need to be able to measure it.

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