Save and Load Model in TensorFlow
In this method, TensorFlow saves only the model architecture. To do this, it serializes the model architecture into JSON String which contains all the configuration details like layers and parameters. And when we call the load() method, TensorFlow uses this JSON String to reconstruct the model. Following code demonstrates this:
Python
# Save the model architecture to JSON filemodel_json = model.to_json()with open('my_model.json', 'w') as json_file: json_file.write(model_json)# Output confirmation messageprint("Model architecture saved successfully.")# Load the model architecture from JSON filewith open('my_model.json', 'r') as json_file: loaded_model_json = json_file.read()loaded_model = tf.keras.models.model_from_json(loaded_model_json)# Output confirmation messageprint("Model architecture loaded successfully.") |
Output:
Model architecture saved successfully.
Model architecture loaded successfully.
The json file gets saved as “my_model.json”.
Save and Load Models using TensorFlow in Json?
If you are looking to explore Machine Learning with TensorFlow, you are at the right place. This comprehensive article explains how to save and load the models in TensorFlow along with its brief overview. If you read this article till the end, you will not need to look for further guides on how to save and reuse the Model in Machine Learning.
TensorFlow has become the top-notch choice among Machine Learning Experts. This is because it offers a lot of high-level APIs and pre-built modules to create and train the Machine Learning Models. Thus, it becomes important to learn how to save and load models using the TensorFlow Library. There is not one way to do it, there are various methods. So, let us see what method will be the best one for saving the model object and loading it back from the memory.
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