Saving and Restoring Tensorflow Models

Machine learning software library.

In the world of machine learning and deep learning, the ability to save and restore models is of utmost importance. This process allows us to reuse models that have been trained on large datasets, saving us from the time-consuming process of training them again. In this unit, we will explore how to save and restore models in Tensorflow.

Importance of Saving and Restoring Models

Training a model can be a time-consuming and resource-intensive process, especially when dealing with large datasets. Once a model is trained, it is beneficial to save the model's parameters to disk so that it can be reused later. This allows us to make predictions at a later time without needing to retrain the model.

Understanding the SavedModel Format in Tensorflow

Tensorflow provides the SavedModel format as a universal format for exporting models. This format includes a complete Tensorflow program, including weights and computation. It does not require the original model building code to run, which makes it useful for sharing or deploying (with TFX, TensorFlow Serving, TensorFlow Lite, TensorFlow.js, etc).

How to Save and Load Models in Tensorflow

Tensorflow provides the tf.saved_model.save function to save a model. This function saves both the model architecture and its weights. Here is a simple example:

model = tf.keras.models.Sequential([
  tf.keras.layers.Dense(10, input_shape=(None, 5))
])
tf.saved_model.save(model, "/tmp/model")

To load a model, you can use the tf.saved_model.load function. This function returns a function that takes a tensor and produces the output of the computation:

loaded = tf.saved_model.load("/tmp/model")
print(loaded(tf.constant([[1., 2., 3., 4., 5.]])))

Exporting Models for Serving

Tensorflow Serving is a flexible, high-performance serving system for machine learning models. To serve a model using Tensorflow Serving, you first need to export your model in the SavedModel format. This can be done using the tf.saved_model.save function.

Versioning and Managing Saved Models

When you save a model, Tensorflow includes a timestamp in the saved model directory name. This allows you to keep multiple versions of your model. Tensorflow Serving can automatically serve the latest version of your model, or you can configure it to use a specific version.

In conclusion, saving and restoring models in Tensorflow is a crucial skill for any machine learning practitioner. It allows us to reuse models, share them with others, and deploy them in production environments.