Model versioning in TensorFlow

Introduction

Model versioning helps keep track of different saved versions of a machine learning model. It makes it easy to update, compare, and reuse models safely.

When you improve your model and want to save the new version without losing the old one.

When you want to test different model versions to see which works best.

When deploying models to production and need to switch between versions smoothly.

When sharing models with others and want to keep track of changes.

When you want to roll back to a previous model if the new one has problems.

Syntax

TensorFlow

model.save('path/to/model/version_number')

You save each model version in a separate folder or path.

Use clear version numbers or dates in folder names to organize versions.

Examples

Saves the model as version 1 in the 'models/model_v1' folder.

TensorFlow

model.save('models/model_v1')

Saves an improved model as version 2 in a new folder.

TensorFlow

model.save('models/model_v2')

Loads the saved model version 1 for use or evaluation.

TensorFlow

loaded_model = tf.keras.models.load_model('models/model_v1')

Sample Model

This code trains a simple model twice, saving two versions. Then it loads each version and shows their loss on the same data. This helps compare versions easily.

TensorFlow

import tensorflow as tf
from tensorflow.keras import layers

# Create a simple model
model = tf.keras.Sequential([
    layers.Dense(10, activation='relu', input_shape=(5,)),
    layers.Dense(1)
])

model.compile(optimizer='adam', loss='mse')

# Dummy data
import numpy as np
x = np.random.random((100, 5))
y = np.random.random((100, 1))

# Train the model
model.fit(x, y, epochs=2, verbose=0)

# Save version 1
model.save('saved_models/model_v1')

# Train more for version 2
model.fit(x, y, epochs=2, verbose=0)

# Save version 2
model.save('saved_models/model_v2')

# Load version 1
model_v1 = tf.keras.models.load_model('saved_models/model_v1')

# Evaluate version 1
loss_v1 = model_v1.evaluate(x, y, verbose=0)

# Load version 2
model_v2 = tf.keras.models.load_model('saved_models/model_v2')

# Evaluate version 2
loss_v2 = model_v2.evaluate(x, y, verbose=0)

print(f"Loss of model_v1: {loss_v1:.4f}")
print(f"Loss of model_v2: {loss_v2:.4f}")

Important Notes

Always use unique folder names for each version to avoid overwriting.

Keep track of what changed between versions for better management.

Model versioning works well with tools like Git or cloud storage for collaboration.

Summary

Model versioning saves different copies of your model safely.

It helps compare, update, and deploy models easily.

Use clear folder names and load models by their version when needed.