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About

TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed up experimentations while remaining fully transparent and compatible with it. Easy-to-use and understand high-level API for implementing deep neural networks, with tutorial and examples. Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, metrics. Full transparency over Tensorflow. All functions are built over tensors and can be used independently of TFLearn. Powerful helper functions to train any TensorFlow graph, with support of multiple inputs, outputs, and optimizers. Easy and beautiful graph visualization, with details about weights, gradients, activations and more. The high-level API currently supports most of the recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, Generative networks.

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.

Platforms Supported

Windows Not Supported
Mac Supported
Linux Supported
Cloud Not Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

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

Audience

Researchers and developers looking for a modular and transparent deep learning library to facilitate their experimentations

Audience

Machine learning engineers, PyTorch developers, researchers and students exploring model architecture, tensor shapes and architecture changes.

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Supported

API

Offers API Supported

Screenshots and Videos

Screenshots and Videos

No images available

Pricing

No information available.
Free Version Not Supported
Free Trial Not Supported

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

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

TFLearn
tflearn.org

Company Information

tensorViz
tensorviz.dev/

Alternatives

Alternatives

AWS Neuron

AWS Neuron

Amazon Web Services
TF-Agents

TF-Agents

Tensorflow
TensorBoard

TensorBoard

Tensorflow

Categories

Deep Learning Supported
Neural Network Supported

Categories

Deep Learning Supported
Machine Learning Supported

Integrations

Claude Code Not Supported
Codex CLI Not Supported
Cursor Not Supported
GitHub Not Supported
Model Context Protocol (MCP) Not Supported
T3 Code Not Supported
TensorFlow Supported
Visual Studio Code Not Supported

Integrations

Claude Code Supported
Codex CLI Supported
Cursor Supported
GitHub Supported
Model Context Protocol (MCP) Supported
T3 Code Supported
TensorFlow Not Supported
Visual Studio Code Supported
Claim TFLearn and update features and information
Claim TFLearn and update features and information
Claim tensorViz and update features and information
Claim tensorViz and update features and information