For single-valued features (where each feature contains a single value as a number or string), the attribution should be a single number for that feature. Info: This package contains files in non-standard labels. A brief overview of the dashboards shown (tabs in top navigation bar): Additional TensorBoard plugins are automatically enabled when you log other types of data. Facets Dive is incredibly flexible, and can create multiple interesting visualizations through its ability to bucket, scatter, and color datapoints. This notebook demonstrates WIT on a smile detection classifier. Set the binning of X-axis by marital status, scattering of X-axis by age, scattering of Y-axis by inference score and color by inference label. Also, the current threshold point on the ROC curve moves up and to the right, meaning a higher true positive rate and higher false positive rate, as the model becomes more permissive in who it deems as high income. Here, the tool will calculate the distance from the selected datapoint to every other datapoint loaded, and will use that distance in the datapoints visualization. An example would be a feature named “attributions__age” containing attribution values for the input feature “age”. Now our selected datapoint, highlighted in yellow, is the left-most datapoint as it is completely similar to itself. Understanding biases in your datasets and data slices on which your model has disparate performance are very important parts of analyzing a model for fairness. It enables tracking experiment metrics like loss and accuracy, visualizing the model graph, projecting embeddings to a lower dimensional space, and much more. What-If Tool TensorBoard plugin. rather than the percent prefix (%) to invoke the colab magic. Now we can see a positive classification threshold slider, confusion matrix, and ROC curve for the model. For using the tool to compare two models, click the “add another model for comparison” button and fill out the model information for the second model in the same manner. Notice how with the high male threshold there are many more false negatives than before, and with the low female threshold there are many more false positives than before. WIT can automatically set the classification threshold to the optimal point, given the dataset loaded and the cost ratio, which specifies the relative cost of a false positive versus a false negative. We can set the x-axis scatter to a feature of the dataset, such as education level. When opening the What-If Tool dashboard in TensorBoard, you will see a setupscreen where you provide the host and port of the model server, the name ofthe model being served, the type of model, and the path to the TFRecords fileto load. On the command line, run the same command without "%". but do now know how to use it,should I add some code in my PY file in order to use this tool? To use the What-If Tool inside of TensorBoard, you need to serve your model through TensorFlow Serving’s prediction service API, in which models accept TensorFlow Example protocol buffers as input data points, or you can provide your own custom python function for generating model predictions. At each point, we'll highlight statements about the things WIT helped us learn about our dataset and trained model in bold. Above: The optimal classification threshold when using a cost ratio of 2.0. It also shows how we can use WIT with non-TensorFlow models by providing a custom predict function for the tool to use. The dataset can even contain results from running a model offline, for use by the What-If Tool. As we scrub the threshold down from its initial value of 0.5, we see the confusion matrix and ROC curve get updated. What does this mean for the future of AI, edge…, What I learned from hiring hundreds of engineers can help you land your next…, Hot Meta Posts: Allow for removal by moderators, and thoughts about future…, Goodbye, Prettify. What are the limitations of using the What-If Tool in TensorBoard? Open TensorBoard again, this time pointing it at the new log directory.

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