What is a .H5 file?
HDF5 is a general-purpose container for large scientific datasets, and it is also where Keras used to save models - storing the architecture as a JSON string in a header attribute next to the weights.
- Did you know
- HDF5 came out of the National Center for Supercomputing Applications and stores everything from climate models to neutron-scattering data; Keras models are a small and fairly recent corner of its use.
- Analyser handles .H5 alongside related formats such as .ONNX, .Safetensors, .GGUF and more.
- In Analyser's format library, .H5 sits in the System & disk category.
- What Analyser shows you
- Open machine-learning models and read what is actually in them. ONNX models and frozen TensorFlow graphs are drawn as a graph: every operation as a box, every tensor flowing between them as a line, laid out left to right by depth so parallel branches sit side by side. Neither format stores the connections - a node follows another because one of its inputs is one of the other's outputs - so the edges are recovered by matching those names. Alongside the picture: the operation mix that fingerprints the architecture, the input and output shapes (including the dynamic ones a model leaves open), every weight tensor with its shape, and the total parameter count. Weight files are read too - Safetensors and GGUF give up their full tensor list, precision and parameter count, and a GGUF also declares its architecture, context length and quantisation. A PyTorch checkpoint is a Python pickle, which is a program rather than a document, so nothing in it is ever run: the opcodes are read as bytes and every module it would import is listed, with anything a file of numbers has no business touching flagged. Keras models have their layer list read from the architecture JSON.
- Open a .H5 file
- Drag a .H5 file onto the Analyser home page (or tap to pick one). It opens entirely in your browser - nothing is uploaded, there is no account, and it works offline once installed.