TensorFlow is a powerful open-source library that is commonly used in machine learning and deep learning projects. One of the key features of TensorFlow is its capability to save models in a format called TensorFlow SavedModel. This format is extremely useful for long-term storage, model sharing, and deploying models for inference in various environments.
Understanding TensorFlow SavedModel
The TensorFlow SavedModel format is a versatile, language-neutral, and recoverable file format that bundles together a TensorFlow graph, or a MetaGraph as it is known, along with detachable and reusable assets such as weights and vocabulary files. It allows you to restore the same TensorFlow model in a different environment, ensuring consistency and accuracy in deployments.
Saving a Model
The process of saving a TensorFlow model in the SavedModel format is straightforward. You can make use of the easy-to-use TensorFlow API.
import tensorflow as tf
# Assume that we have a trained model
model = ... # a tf.keras.Model object
# Save the model using ".save" method
model.save('path/to/saved_model')
In the example above, we use the model.save() function to persist the trained model in the {.tf} directory.
Loading a Model
Loading the SavedModel for inference is also facilitated by the TensorFlow API. Below is how you can utilize the SavedModel:
import tensorflow as tf
# Load the model
new_model = tf.keras.models.load_model('path/to/saved_model')
# Evaluate the loaded model
loss, acc = new_model.evaluate(test_data, test_labels)
print('Restored model, accuracy: {:5.2f}%'.format(100 * acc))
In the above code, tf.keras.models.load_model function is used to retrieve the saved model. It's vital because it allows you to validate your restored model's performance to ensure consistency with the original model behavior.
Using SavedModel for Inference
Once you load your model, inference can easily be done by simply passing data into the model's predict method.
# Assume you are using some input data
sample_data = ... # the input data for prediction
# Perform inference
predictions = new_model.predict(sample_data)
print(predictions) # This outputs the inference result
The above code shows that after restoring a model, you can conduct inference by passing in new data to the model's predict() method. Post-processing may be required to interpret the output results meaningfully.
Advantages of TensorFlow SavedModel
- Platform Independence: SavedModel allows cross-platform deployment without needing to tweak model code or assets with conditional logic.
- Comprehensive Export: It provides a whole snapshot including model weights, graphs, and variables.
- Rich Ecosystem: SavedModel supports versatile environments and interactive tools such as TensorFlow{' '} 'TensorBoard' and 'TensorFlow Serving'.
Deployment With TensorFlow Serving
Models saved in the SavedModel format can be easily deployed using TensorFlow Serving, which is a model serving system optimized for production environments.
To serve a model, first install TensorFlow Serving, then use the command:
tensorflow_model_server \
--rest_api_port=8501 \
--model_name=my_model \
--model_base_path="/path/to/saved_model"
This starts the model server and prepares it to accept RESTful requests, exemplifying an easy deployment approach for inference across varied platforms.
Conclusion
TensorFlow SavedModel is an instrumental feature that enables developers to save and share their machine learning models across different environments. It allows for robust deployment solutions and seamless transferability of models for inference and prediction in production settings.
