How to save only essential parameters in TensorFlow?

@enrico 

To save only essential parameters in TensorFlow, you can follow the steps below:

1. Create a TensorFlow Saver object and specify the parameters that you want to save. This can be done by manually selecting the variables or by using a naming convention for the desired variables.

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saver = tf.train.Saver(var_list=[var1, var2, var3])

1. During training or after your model has been trained, you need to save the desired parameters. To achieve this, you can call the save method of the Saver object within a TensorFlow session, specifying the file path where the parameters will be saved.

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with tf.Session() as sess:
    # Restoring or training the model ...
    
    # Save only the essential parameters
    saver.save(sess, 'path/to/save/essential_params.ckpt')

When you save the parameters using the Saver object, TensorFlow will create a checkpoint file (with the specified file path) that contains only the essential parameters you selected.

Note that you need to have an active TensorFlow session in order to save the variables. Also, ensure that you have restored or trained the model before saving the parameters to include the latest updates.

@enrico 

Additionally, if you want to save and restore a subset of variables, you can use the 'var_list' argument in the tf.train.Saver() constructor to specify which variables to save/restore. This allows you to have more control over which parameters are saved and restored, especially if you have a large number of variables in your model.

For example:

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# Define the variables that you want to save
var1 = tf.Variable(...)
var2 = tf.Variable(...)
var3 = tf.Variable(...)

# Create a saver object and specify the variables you want to save
saver = tf.train.Saver(var_list=[var1, var2, var3])

# Training and saving the model
with tf.Session() as sess:
    # Training or restoring the model

    # Save only the specified variables
    saver.save(sess, 'path/to/save/essential_params.ckpt')

By using the var_list argument, you can save only the essential parameters, making the checkpoint files more lightweight and easier to manage.