can't save compiled model as .tf

[Imacder]

Issue type

Bug

Have you reproduced the bug with TensorFlow Nightly?

Yes

Source

source

TensorFlow version

2.13.0

Custom code

Yes

OS platform and distribution

Mac mini m1

Mobile device

No response

Python version

3.9

Bazel version

No response

GCC/compiler version

No response

CUDA/cuDNN version

No response

GPU model and memory

m1 gpu and 8GB Ram

Current behavior?

can't save compile model as a .tf file.

Standalone code to reproduce the issue

import tensorflow as tf


class CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):
    def __init__(self, d_model, f=0.5, warmup_steps=4_000):
        super().__init__()

        self.d_model = d_model
        self.d_model = tf.cast(self.d_model, tf.float32)

        self.warmup_steps = warmup_steps
        self.f = f

    def __call__(self, step):
        step = tf.cast(step, dtype=tf.float32)
        arg1 = tf.math.rsqrt(step)
        arg2 = step * (self.warmup_steps ** -1.5)

        return self.f * tf.math.rsqrt(self.d_model) * tf.math.minimum(arg1, arg2)

    def get_config(self):
        config = {
            "d_model": self.d_model,
            "warmup_steps": self.warmup_steps,
            "f": self.f
        }
        return config


model = tf.keras.Sequential([
    tf.keras.layers.Input(shape=(784,)),
    tf.keras.layers.Dense(10)
])
learning_rate = CustomSchedule(1024)
optimizer = tf.keras.optimizers.legacy.Adam(learning_rate, beta_1=0.9, beta_2=0.98,
                                            epsilon=1e-5)
model.compile(
    optimizer=optimizer,
    loss="mse", metrics=['accuracy'])
model.save("model.tf")

Relevant log output

/opt/homebrew/Caskroom/miniconda/base/envs/pythonProject/bin/python /Users/albert/PycharmProjects/pythonProject/AI/test.py 
Traceback (most recent call last):
  File "/Users/albert/PycharmProjects/pythonProject/AI/test.py", line 40, in <module>
    model.save("model.tf")
  File "/opt/homebrew/Caskroom/miniconda/base/envs/pythonProject/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/opt/homebrew/Caskroom/miniconda/base/envs/pythonProject/lib/python3.10/json/encoder.py", line 199, in encode
    chunks = self.iterencode(o, _one_shot=True)
  File "/opt/homebrew/Caskroom/miniconda/base/envs/pythonProject/lib/python3.10/json/encoder.py", line 257, in iterencode
    return _iterencode(o, 0)
TypeError: Unable to serialize 1024.0 to JSON. Unrecognized type <class 'tensorflow.python.framework.ops.EagerTensor'>.

[sushreebarsa]

@Imacder Keras separates the concerns of saving your model architecture and saving your model weights.
The model structure can be described and saved using two different formats: JSON and YAML. You can save a model with model.save() or keras.models.save_model() (which is equivalent). You can load it back with keras.models.load_model(). Please make sure to follow this guide here to save a compiled model,
Thank you!

[github-actions]

This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.

[github-actions]

This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.

[google-ml-butler]

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Yes
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