We always use tf.train.Saver() to save and restore weights, like the following example(https://www.tensorflow.org/guide/saved_model).
But how to use eager execution to save? how to change the following example?
Another question, is it a good idea to use eager?
I fond tf.contrib.eager.Saver in (https://www.tensorflow.org/api_docs/python/tf/contrib/eager/Saver), but it says "Saver's name-based checkpointing strategy is fragile". What does it mean?
# Create some variables. v1 = tf.get_variable("v1", shape=, initializer = tf.zeros_initializer) v2 = tf.get_variable("v2", shape=, initializer = tf.zeros_initializer) inc_v1 = v1.assign(v1+1) dec_v2 = v2.assign(v2-1) # Add an op to initialize the variables. init_op = tf.global_variables_initializer() # Add ops to save and restore all the variables. saver = tf.train.Saver() # Later, launch the model, initialize the variables, do some work, and save the # variables to disk. with tf.Session() as sess: sess.run(init_op) # Do some work with the model. inc_v1.op.run() dec_v2.op.run() # Save the variables to disk. save_path = saver.save(sess, "/tmp/model.ckpt") print("Model saved in path: %s" % save_path)