To save a trained model in TensorFlow, follow these steps:

1. Create a tf.keras.Model object or subclass of it.

2. Train the model using the fit() method.

3. Create a tf.keras.ModelCheckpoint callback object and pass it to the fit() method as an argument.

4. Set the save_weights_only parameter to True in the ModelCheckpoint callback object. This will save only the weights of the model, not the entire model structure.

5. Set the filepath parameter in the ModelCheckpoint callback object to the desired file location where you want to save the model.

6. Run the fit() method to train the model and save it at the specified file location.

Example:

Copy code# Create a model
model = tf.keras.Sequential([
    tf.keras.layers.Dense(64, activation='relu', input_shape=(10,)),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

# Train the model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# Create a ModelCheckpoint callback to save the model weights
checkpoint = tf.keras.callbacks.ModelCheckpoint(filepath='/tmp/model.h5', save_weights_only=True)

# Train the model and save the weights
model.fit(x_train, y_train, epochs=10, callbacks=[checkpoint])

You can then load the saved model weights using the load_weights() method of the model object:

Copy code# Load the saved model weights
model.load_weights('/tmp/model.h5')

# Evaluate the model on the test data
loss, accuracy = model.evaluate(x_test, y_test)
print(f'Test loss: {loss}, Test accuracy: {accuracy}')

Hope you like it!

Keep coding! :)