TensorFlow Filesystem - access tensors by mounting your model into a filesystem
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README.md
print_tensor_dependencies.py
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tffs.py

README.md

TFFS

TensorFlow Filesystem - Access Tensors Differently

A funny way to access your tensorflow model's tensors.

Use this project to map your model into a filesystem. Then, access your tensors as if they were files, using your favorite UNIX commands.

tffs is implemented using Filesystem in Userspace (FUSE). It requires tensorflow and fusepy to be installed.

To learn more, read the accompanying blog post.

Usage

  1. Create a model - out of the scope of this project :)

  2. Mount your model so it'll be accessible through the filesystem:

    python tffs.py --model PATH_TO_MODEL --mount MOUNT_POINT

    PATH_TO_MODEL is either a directory containing a .meta file, or the .meta file itself.

    If there's also a file containing the weights with the same name as the .meta file (without the .meta extension), it'll be loaded as well.

  3. Reap the fruits. Assuming MOUNT_POINT is ~/tf:

Command Description
find ~/tf list all scopes and tensors
find ~/tf -type f list all tensors
xattr -l ~/tf/.../tensor get attributes of a tensor
cat ~/tf/.../tensor print the value found in a tensor
~/tf/bin/inputs -d 3 ~/tf/.../tensor print the inputs to a tensor, recursively
~/tf/bin/outputs --no-fs .../tensor print the outputs to a tensor, without using the mount prefix