![]() Summary is a special TensorBoard operation that takes in a regular tenor and outputs the summarized data to your disk (i.e. In this second part, we are now going to use a special operation called summary to visualize the model parameters (like weights and biases of a neural network), metrics (like loss or accuracy value), and images (like input images to a network). So far we only focused on how to visualize the graph in TensorBoard. Writing Summaries to Visualize Learning ¶ To get rid of the warning, delete the event files you no longer need or save them in different folders. TF will show only the latest graph and display the warning of multiple event files. ![]() *Note: If you run your code several times with the same, there will be multiple event files in your. Let's modify the code one more time and add the names: To make TensorBoard understand the names of your ops, you have to explicitly name them. They mean nothing for the internal TensorFlow. The names we give them (a, b, and c) are just Python-names which are for us to access them when we write code. “Const” and “Const_1” in the graph correspond to a and b, and the node “Add” corresponds to c. TensorBoard page visualizing the graph generated in Example 1 ctrl+left click on that link (or simply copy it into your browser or just open your browser and go to This will show the TensorBoard page which will look like:įig. $ tensorboard -logdir="./graphs" -port 6006 For example, here we can switch to the directory using Next, go to Terminal and make sure that the present working directory is the same as where you ran your Python code. ![]() Created directory which contains the event file Now if you run this code, it creates a directory inside your current directory (beside your Python code) which contains the event file.įig. Learning to use TensorBoard early and often will make working with TensorFlow much more enjoyable and productive. We'll cover this two main usages of TensorBoard in this tutorial. It is generally used for two main purposes:Ģ. TensorBoard was created as a way to help you understand the flow of tensors in your model so that you can debug and optimize it. When fully configured, TensorBoard window will look something like this: As explained in the previous tutorials, the idea is that you create a model that consists of a set of operations, feed data in to the model and the tensors will flow between the operations until you get an output tensor, your result. TensorFlow programs can range from very simple to super complex problems (using thousands of computations), and they all have two basic components, Operations and Tensors. To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard.” ![]() In Google’s words: “The computations you'll use TensorFlow for (like training a massive deep neural network) can be complex and confusing. TensorBoard is a visualization software that comes with any standard TensorFlow installation. ![]()
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