Converting Models Back to VGSL Spec

VGSLify now includes the ability to convert a trained or existing model back into a VGSL specification string. This functionality is useful for:

  • Sharing model architectures in a concise format.

  • Reproducing models from the VGSL spec string.

  • Analyzing and understanding complex models via their VGSL representation.

How It Works

After you build or load a model using TensorFlow (with PyTorch support planned), you can convert it back into its VGSL specification string using the model_to_spec() function provided by VGSLify.

Example: Convert a Model to VGSL Spec

Here’s how you can convert an existing model to its VGSL spec:

from vgslify.utils import model_to_spec
from tensorflow.keras.models import load_model

# Load an existing TensorFlow model (previously saved)
model = load_model("path_to_your_model.keras")

# Convert the model to VGSL spec
vgsl_spec = model_to_spec(model)
print(vgsl_spec)

The above example will output the VGSL spec string corresponding to the architecture of the loaded model.

Saving and Reusing VGSL Spec

Once you’ve converted the model to a VGSL spec, you can easily save or share the spec string. This can be reused to rebuild the same model using VGSLify.

  1. Save the VGSL Spec:

    • Save the generated VGSL spec string to a file or store it in your project for later use.

with open("model_spec.txt", "w") as f:
    f.write(vgsl_spec)

  1. Rebuild the Model from the Spec:

    • You can use the saved VGSL spec to rebuild the exact same model at any time.

from vgslify.generator import VGSLModelGenerator

# Load the VGSL spec from file
with open("model_spec.txt", "r") as f:
    vgsl_spec = f.read()

# Rebuild the model from the spec
vgsl_gn = VGSLModelGenerator(backend="tensorflow")
model = vgsl_gn.generate_model(vgsl_spec)
model.summary()

By using this functionality, you can quickly share, reproduce, and analyze deep learning models in a concise format.