Visualizing TensorFlow 2 models with Weights & Biases

Measure your tensorflow model's performance in just a few lines of code

Created on September 18|Last edited on September 18

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It's easy to integrate your TensorFlow models with Weights & Biases. With this quick integration you can see your live metrics streaming in to our visualizations, and compare new results to your previous baselines.
﻿Try the Colab → to train a CNN, or this one → to train a Perceptron.
In one minute you can train these TensorFlow 2 models and see live results → streaming into a Weights & Biases project.
Integrating with Weights & BiasesTo visualize your own models, add a few lines of code to any TensorFlow script to start seeing results:
import wandb at the top of your script
wandb.init(config=param_dict) initialize a new run and pass in a dictionary of the model's hyperparameters
wandb.log({"loss": loss, "val_acc": val_acc}) log output metrics to see them graphed over time
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Next StepsTo see your live results in Weights & Biases:
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Get a free personal account. Sign up →﻿
Run the hosted notebook. Google Colab →﻿
See results appear in this W&B report →﻿
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Tags: TensorFlow Lite, Articles, Beginner

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