Visualizers
Visualize model graph tensors using Visualizers
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Visualize model graph tensors using Visualizers
Last updated
Was this helpful?
Neural networks are made up of components that process generic, undefined data. In order to make sense of the data, Tensorleap enables you to visualize the data contained in any node, with the exception of the Optimizer, using a Visualizer node. You can visualize the input(s), output(s) (prediction), ground truth(s), and intermediate tensors. When running , the visualizations related to each Visualizer node is shown on the left panel.
When performing , error analysis algorithms analyze the sample's error throughout the model and project it to each input. This will generate various heat-maps highlighting different features. Therefore, at least one connection of the (model-graph input) must be connected to a Visualizer.
You can write your own Custom Visualizers in the Integration Script (see ), or use one of the following out-of-the-box Decoders:
HorizontalBar - visualize numeric data as horizontal bar with labels
Image - visualize 2D image
Graph - visualize numeric data as a graph
Numeric - presents a number or an array of numbers
ImageMask - visualize an image and an overlay mask (commonly used in image segmentation)
TextMask - visualize text and an overlay mask (commonly used in text segmentation)
To add a Visualizer to the model graph, follow the following steps:
On the Network view, right-click anywhere and select Visualizer from the menu.
(optional) - Add and connect an additional Visualizer nodes to selected nodes' outputs.
This example demonstrates how to add visualizers to an image classification model. An image decoder is added to the input, and two HorizontalBar visualizers are added to the prediction and ground-truth in order to compare the two:
Text - tokens representation of text (for token-to-text Visualizer, use the with your tokenizer)
When the Visualizer node appears in the Network view, click it to open a panel to the right. From here, you can set the Selected Visualizer to one of the out-of-the-box Decoders, or custom Decoders, set in the Integration Script (see ).
Connect the output you to the Visualizer node. (at least one visualizer must be connected)
