Official TensorFlow 2.16 + Python 3.12 – JARaaS Hybrid RAG  6/17/2024
Note: Sources at the end of the response
The issue arises because TensorFlow’s model saving and loading mechanisms, particularly for Keras models, have some limitations when it comes to handling constants or variables that are not standard tensors within the computational graph.
Here’s what’s happening in your example:

Model Definition:
You have defined a Keras model with an input layer and added a constant tensor to it.
input = tf.keras.layers.Input(shape=[N])
constant = tf.constant(np.random.random([1, N]))
x = tf.keras.layers.Add()([input, constant])
m = tf.keras.Model(inputs=[input], outputs=[x])

Running the Model:
The model runs successfully before saving:
print(m(np.random.random([1, N])))

Saving the Model:
The model is saved without any issues:
tf.keras.models.save_model(m, "test_dot.keras")

Loading the Model:
The failure occurs during model loading:
tf.keras.models.load_model("test_dot.keras") # fail
Explanation
The problem lies in how TensorFlow handles the constant when saving and loading the model. In TensorFlow, constants are static and can be serialized. However, during deserialization, the context and type of the constant (especially if it’s not a standard tensor but a more complex object like a list) become ambiguous.
Specifically, the error message:
TypeError: unhashable type: ‘list’
indicates that TensorFlow is encountering a list where it expects a different type. The list in question is likely coming from an intermediate representation of the constant during the loading process.
Possible Solution
To avoid this issue, you can redefine the constant tensor as a Keras layer, rather than directly using tf.constant
. Here’s an alternative approach:
import numpy as np
import tensorflow as tf
N = 4
input = tf.keras.layers.Input(shape=[N])
constant_layer = tf.keras.layers.Lambda(lambda x: tf.constant(np.random.random([1, N])))(input)
x = tf.keras.layers.Add()([input, constant_layer])
m = tf.keras.Model(inputs=[input], outputs=[x])
print(m(np.random.random([1, N]))) # can run before saving
tf.keras.models.save_model(m, "test_dot.keras")
loaded_model = tf.keras.models.load_model("test_dot.keras")
print(loaded_model(np.random.random([1, N]))) # should run after loading now
Summary
By using a Lambda
layer to wrap the constant, the constant becomes part of the model’s graph, which can be correctly serialized and deserialized by TensorFlow.
Sources
 Save and Load Models: save_and_load.ipynb (internal document)
 TensorFlow Model Serialization Guide: saved_model.ipynb (internal document)