Hi everyone. I’m currently facing a challenge that I’m hoping to get some guidance on.

I had a model from tf.save_model.save(), and my goal is to load this model and extract the weights and constants contained within it. However, the loaded model is “_UserObject” and I cannot access the values inside. I’m curious if anyone has encountered a similar situation and found a solution or workaround.

Code is attached following. How can I get the parameters (weights/constants) from the model 2?

import tensorflow as tf
class Model(tf.Model):
def __init__(self):
super().__init__()
self.const = tf.constant(123.456, dtype=tf.float32)
@tf.function(jit_compile=True)
def call(self, x):
return self.const + x
input = tf.random.uniform([1, 2], dtype=tf.float32)
model1 = Model()
print(model1(input))
tf.saved_model.save(model1, "./aaa")
model2 = tf.saved_model.load("./aaa")
print(model2(input))
# how can I get the parameters (weights/constants) from the model 2?

@Kiran_Sai_Ramineni Thank you so much for your reply! It seems that trainable_variables only outputs weights that can be trained. It cannot find constant values. In my case, I would like to obtain the value of self.const, but this function cannot help. Code:

Hi everyone. I’m currently facing a challenge that I’m hoping to get some guidance on.

I had a model from tf.save_model.save(), and my goal is to load this model and extract the weights and constants contained within it. However, the loaded model is “_UserObject” and I cannot access the values inside. I’m curious if anyone has encountered a similar situation and found a solution or workaround.

Code:

import tensorflow as tf
class Model(tf.Model):
def __init__(self):
super().__init__()
self.const = tf.constant(123.456, dtype=tf.float32)
@tf.function(jit_compile=True)
def call(self, x):
return self.const + x
input = tf.random.uniform([1, 2], dtype=tf.float32)
model1 = Model()
print(model1(input))
tf.saved_model.save(model1, "./aaa")
model2 = tf.saved_model.load("./aaa")
print(model2(input))
# how can I get the parameters (weights/constants) from the model 2?

@Kiran_Sai_Ramineni Thank you again for your detailed reply! Perhaps I did not make my question clear. I cannot change the way of how the model is stored.

Suppose I have a model from tf.save_model.load(), how can I extract the constants contained within it? I highlight the part I cannot modify/observe.

import tensorflow as tf
input = tf.random.uniform([1, 2], dtype=tf.float32)
# =========
class Model(tf.Module):
def __init__(self):
super().__init__()
self.const = tf.constant(123.456, dtype=tf.float32)
@tf.function(jit_compile=True)
def __call__(self, x):
return self.const + x
model1 = Model()
tf.saved_model.save(model1, "./aaa")
# ======== what above is the part I cannot observe
model2 = tf.saved_model.load("./aaa")
print(model2(input))
# how can I get the parameters (weights/constants) from the model 2?