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Visualize and analyze tables

Customize your W&B Tables to answer questions about your machine learning model's performance, analyze your data, and more.

Interactively explore your data to:

  • Compare changes precisely across models, epochs, or individual examples
  • Understand higher-level patterns in your data
  • Capture and communicate your insights with visual samples
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W&B Tables posses the following behaviors:

  1. Stateless in an artifact context: any table logged alongside an artifact version will reset to its default state after you close the browser window
  2. Stateful in a workspace or report context: any changes you make to a table in a single run workspace, multi-run project workspace, or Report will persist.

For information on how to save your current W&B Table view, see Save your view.

How to view two tables​

Compare two tables with a merged view or a side-by-side view. For example, the image below demonstrates a table comparison of MNIST data.

Left: mistakes after 1 training epochs, Right: mistakes after 5 epochs

Follow these steps to compare two tables:

  1. Go to your project in the W&B App.
  2. Select the artifacts icon on the left panel.
  3. Select an artifact version.

In the following image we demonstrate a model's predictions on MNIST validation data after each of five epochs (view interactive example here).

Click on "predictions" to view the Table

  1. Hover over the second artifact version you want to compare in the sidebar and click Compare when it appears. For example, in the image below we select a version labeled as "v4" to compare to MNIST predictions made by the same model after 5 epochs of training.

Preparing to compare model predictions after training for 1 epoch (v0, shown here) vs 5 epochs (v4)

Merged view​

Initially you will see both tables merged together. The first table selected has index 0 and a blue highlight, and the second table has index 1 and a yellow highlight. View a live example of merged tables here.

In the merged view, numerical columns will appear as histograms by default

From the merged view, you can

  • choose the join key: use the dropdown at the top left to set the column to use as the join key for the two tables. Typically this will be the unique identifier of each row, such as the file name of a specific example in your dataset or an incrementing index on your generated samples. Note that it's currently possible to select any column, which may yield illegible tables and slow queries.
  • concatenate instead of join: select "concatenating all tables" in this dropdown to union all the rows from both tables into one larger Table instead of joining across their columns
  • reference each Table explicitly: use 0, 1, and * in the filter expression to explicitly specify a column in one or both table instances
  • visualize detailed numerical differences as histograms: compare the values in any cell at a glance

Side-by-side view​

To view the two tables side-by-side, change the first dropdown from "Merge Tables: Table" to "List of: Table" and then update the "Page size" respectively. Here the first Table selected is on the left and the second one is on the right. Also, you can compare these tables vertically as well by clicking on the "Vertical" checkbox.

In the side-by-side view, Table rows are independent of each other.

  • compare the tables at a glance: apply any operations (sort, filter, group) to both tables in tandem and spot any changes or differences quickly. For example, view the incorrect predictions grouped by guess, the hardest negatives overall, the confidence score distribution by true label, etc.
  • explore two tables independently: scroll through and focus on the side/rows of interest

Compare artifacts​

You can also compare tables across time or model variants.

Compare tables across time​

Log a table in an artifact for each meaningful step of training to analyze model performance over training time. For example, you could log a table at the end of every validation step, after every 50 epochs of training, or any frequency that makes sense for your pipeline. Use the side-by-side view to visualize changes in model predictions.

For each label, the model makes fewer mistakes after 5 training epochs (R) than after 1 (L)

For a more detailed walkthrough of visualizing predictions across training time, see this report and this interactive notebook example.

Compare tables across model variants​

Compare two artifact versions logged at the same step for two different models to analyze model performance across different configurations (hyperparameters, base architectures, and so forth).

For example, compare predictions between a baseline and a new model variant, 2x_layers_2x_lr, where the first convolutional layer doubles from 32 to 64, the second from 128 to 256, and the learning rate from 0.001 to 0.002. From this live example, use the side-by-side view and filter down to the incorrect predictions after 1 (left tab) versus 5 training epochs (right tab).

After 1 epoch, performance is mixed: precision improves for some classes and worsens for others.

Save your view​

Tables you interact with in the run workspace, project workspace, or a report will automatically save their view state. If you apply any table operations then close your browser, the table will retain the last viewed configuration when you next navigate to the table.

tip

Tables you interact with in the artifact context will remain stateless.

To save a table from a workspace in a particular state, export it to a W&B Report. To export a table to report:

  1. Select the kebob icon (three vertical dots) in the top right corner of your workspace visualization panel.
  2. Select either Share panel or Add to report.

Share panel creates a new report, Add to report lets you append to an existing report.

Examples​

These reports highlight the different use cases of W&B Tables:

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