Related Products
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About
Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has extensive documentation and developer guides. Keras is the most used deep learning framework among top-5 winning teams on Kaggle. Because Keras makes it easier to run new experiments, it empowers you to try more ideas than your competition, faster. And this is how you win. Built on top of TensorFlow 2.0, Keras is an industry-strength framework that can scale to large clusters of GPUs or an entire TPU pod. It's not only possible; it's easy. Take advantage of the full deployment capabilities of the TensorFlow platform. You can export Keras models to JavaScript to run directly in the browser, to TF Lite to run on iOS, Android, and embedded devices. It's also easy to serve Keras models as via a web API.
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About
tensorViz helps developers explore PyTorch models and JAX/Flax programs as interactive graphs. For PyTorch, users can expand nested modules, inspect tensor shapes captured from one forward pass, jump to Python source, and review architecture changes beside code diffs. The website offers recorded model graphs to explore without installation. The free VS Code extension is available from the Visual Studio Marketplace and Open VSX. Local capture requires Python, model dependencies, and example inputs. Standalone coding-agent plugins are an Apple Silicon macOS preview; MCP agents can also connect to the extension. JAX/Flax graphs use CPU tracing with inferred shapes, and PyTorch source edits are unavailable for them.
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Platforms Supported
Windows
Supported
Mac
Supported
Linux
Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Supported
iPad
Supported
Android
Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Supported
Mac
Supported
Linux
Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
Developers interested in an deep learning API solution to minimize the number of user actions required for common use cases
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Audience
Machine learning engineers, PyTorch developers, researchers and students exploring model architecture, tensor shapes and architecture changes.
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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API
Offers API
Supported
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API
Offers API
Supported
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Screenshots and Videos |
Screenshots and VideosNo images available
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Pricing
No information available.
Free Version
Not Supported
Free Trial
Not Supported
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Pricing
$0
The VS Code extension is free to install. Core graph exploration and source-edit forms work without an agent or provider API key; connecting a coding agent is optional.
Free Version
Supported
Free Trial
Not Supported
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Reviews/
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Reviews/
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Pros & Cons from Real UsersPros
Cons
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
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Company InformationKeras
United States
keras.io
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Company InformationtensorViz
tensorviz.dev/
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Alternatives |
Alternatives |
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Categories |
Categories |
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Deep Learning Features
Convolutional Neural Networks
Supported
Document Classification
Not Supported
Image Segmentation
Not Supported
ML Algorithm Library
Not Supported
Model Training
Not Supported
Neural Network Modeling
Not Supported
Self-Learning
Not Supported
Visualization
Supported
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Integrations
Cameralyze
Supported
Codex CLI
Not Supported
Comet LLM
Supported
Dataiku
Supported
Flower
Supported
GitHub
Not Supported
Lambda
Supported
MLReef
Supported
MLflow
Supported
Model Context Protocol (MCP)
Not Supported
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Integrations
Cameralyze
Not Supported
Codex CLI
Supported
Comet LLM
Not Supported
Dataiku
Not Supported
Flower
Not Supported
GitHub
Supported
Lambda
Not Supported
MLReef
Not Supported
MLflow
Not Supported
Model Context Protocol (MCP)
Supported
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