| import tensorflow as tf | |
| import keras | |
| # Originally TensorFlow 2.13.0 Keras 2.13.1 | |
| print(tf.__version__) | |
| print(keras.__version__) | |
| # Load the old model using the folder | |
| inference_layer = keras.layers.TFSMLayer('saved_model/', call_endpoint='serving_default') | |
| # Specify the model architecture using an inference layer | |
| model = tf.keras.Sequential([ | |
| inference_layer, | |
| ]) | |
| # compile the model using the same parameters when first training the model | |
| model.compile(optimizer="adam", | |
| loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), | |
| metrics=["accuracy"]) | |
| # save the model as a .keras file (in line with Keras 3 specifications) | |
| model.save("leaf_model.keras") | |