Question: When I try to save my ternsorflow model I get this error message. What is the problem here and how do I fix it?  model = tf.keras.models.Sequential() # define the neural network architecture model.add( tf.keras.layers.Dense(50, input_dim=hidden_dim, activation="relu") ) model.add(tf.keras.layers.Dense(n_classes)) k += 1 model.compile( optimizer=tf.keras.optimizers.Adam(learning_rate=lr), loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), metrics=["mse", "accuracy"], ) history = model.fit( x_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(x_test, y_test), verbose=0, ) folder = "model_mlp_lm" file = f"m{k}_model" os.makedirs(folder, exist_ok=True) path = f"{folder}/{file}" if os.path.isfile(path) is False: model.save(path)   module 'tensorflow.python.saved_model.registration' has no attribute 'get_registered_name'  This is the stack trace: Traceback (most recent call last): File "D:\Anaconda\lib\runpy.py", line 197, in _run_module_as_main return _run_code(code, main_globals, None, File "D:\Anaconda\lib\runpy.py", line 87, in _run_code exec(code, run_globals) File "c:\Users\hijik\.vscode\extensions\ms-python.python-2023.10.0\pythonFiles\lib\python\debugpy\__main__.py", line 39, in <module> cli.main() File "c:\Users\hijik\.vscode\extensions\ms-python.python-2023.10.0\pythonFiles\lib\python\debugpy/..\debugpy\server\cli.py", line 430, in main run() File "c:\Users\hijik\.vscode\extensions\ms-python.python-2023.10.0\pythonFiles\lib\python\debugpy/..\debugpy\server\cli.py", line 284, in run_file runpy.run_path(target, run_name="__main__") File "c:\Users\hijik\.vscode\extensions\ms-python.python-2023.10.0\pythonFiles\lib\python\debugpy\_vendored\pydevd\_pydevd_bundle\pydevd_runpy.py", line 321, in run_path return _run_module_code(code, init_globals, run_name, File "c:\Users\hijik\.vscode\extensions\ms-python.python-2023.10.0\pythonFiles\lib\python\debugpy\_vendored\pydevd\_pydevd_bundle\pydevd_runpy.py", line 135, in _run_module_code _run_code(code, mod_globals, init_globals, File "c:\Users\hijik\.vscode\extensions\ms-python.python-2023.10.0\pythonFiles\lib\python\debugpy\_vendored\pydevd\_pydevd_bundle\pydevd_runpy.py", line 124, in _run_code exec(code, run_globals) File "D:\_lodestar\personality-prediction\finetune_models\MLP_LM.py", line 273, in <module> File "D:\Anaconda\lib\site-packages\tensorflow\python\saved_model\save.py", line 1450, in _build_meta_graph_impl object_graph_proto = _serialize_object_graph( File "D:\Anaconda\lib\site-packages\tensorflow\python\saved_model\save.py", line 1022, in _serialize_object_graph _write_object_proto(obj, obj_proto, asset_file_def_index, File "D:\Anaconda\lib\site-packages\tensorflow\python\saved_model\save.py", line 1061, in _write_object_proto registered_name = registration.get_registered_name(obj) AttributeError: module 'tensorflow.python.saved_model.registration' has no attribute 'get_registered_name' 

Answer: Check if your tensorflow version is older or up-to-date. This seems to be a newer module https://www.tensorflow.org/api_docs/python/tf/keras/saving/get_registered_name Make sure you have this version of tensorflow installed in your environment pip install tensorflow==2.12.0  I don't know about the dataset so I assumed a small one. This is the code I ran import tensorflow as tf import numpy as np import os # Define placeholder values hidden_dim = 2 n_classes = 2 lr = 0.001 epochs = 10 batch_size = 32 # Create a simple dataset x_train = np.array([[1, 2], [3, 4], [5, 6], [7, 8]]) y_train = np.array([0, 1, 0, 1]) # Convert y_train to one-hot encoded format y_train = tf.keras.utils.to_categorical(y_train, num_classes=n_classes) model = tf.keras.models.Sequential() # Define the neural network architecture model.add( tf.keras.layers.Dense(50, input_dim=hidden_dim, activation="relu") ) model.add(tf.keras.layers.Dense(n_classes)) k = 0 # Initialize k k += 1 # Increment k model.compile( optimizer=tf.keras.optimizers.Adam(learning_rate=lr), loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), metrics=["mse", "accuracy"], ) history = model.fit( x_train, y_train, epochs=epochs, batch_size=batch_size, verbose=0, ) folder = "model_mlp_lm" file = f"m{k}_model" os.makedirs(folder, exist_ok=True) path = f"{folder}/{file}" if os.path.isfile(path) is False: model.save(path)  And it ran fine