Hacktoberfest 2026：维护者为十月标记出来的 issue，仍然开放、适合新手。 浏览 Hacktoberfest issue

quantize_model() rejects a valid standalone Keras 3 Sequential model

未关闭
  • 哪里不对？
#1,270 2 条评论 0 个 reaction 已指派 0 人 在 GitHub 查看

@MoeenAsim 已经在做这个了。

开始于 2026年7月28日。

评估

难度
3/5
预计耗时
1-2 天
新手友好度
50/100
Issue 类型
缺陷
描述清晰度
基本清楚
活跃度
冷清
技术栈
python
领域
machine-learning

调研方向

从 tfmot.quantization.keras.quantize_model 和报告中描述的 compat-Keras Sequential 类型检查开始。使用提供的独立 Keras 3 模型和环境重现该故障，然后验证 quantize_model 接受它，或者 API 明确将独立 Keras 3 记录为不受支持。

由索引模型根据 Issue 内容生成。

描述

bug

Describe the bug
tfmot.quantization.keras.quantize_model() rejects a valid standalone Keras 3 keras.Sequential model with:
ValueError: 'to_quantize' can only either be a keras Sequential or Functional model.

This appears to be a compatibility/type-check issue between standalone Keras 3 and TensorFlow Model Optimization Toolkit (TFMOT). The model is a real keras.Sequential instance, but TFMOT does not recognize it as a compatible Sequential model.

System information

TensorFlow version (installed from source or binary):
2.17.0 (installed from binary / pip wheel)

TensorFlow Model Optimization version (installed from source or binary):
0.8.0 (installed from binary / pip wheel)

Python version:
3.11.15

Describe the expected behavior
A valid standalone Keras 3 keras.Sequential model should be accepted by tfmot.quantization.keras.quantize_model() or the API/docs should clearly state that standalone Keras 3 models are unsupported.

Describe the current behavior
A standalone Keras 3 keras.Sequential model is created successfully, but calling:
tfmot.quantization.keras.quantize_model(model)
which primarily raises:
ValueError: 'to_quantize' can only either be a keras Sequential or Functional model.
In the same run:

  • isinstance(model, keras.Sequential) is True
  • the model type is keras.src.models.sequential.Sequential
  • TFMOT’s compat-Keras type check does not recognize it as compatible

Code to reproduce the issue

import tensorflow as tf
import keras
from keras import layers
import tensorflow_model_optimization as tfmot

model = keras.Sequential(
    [
        keras.Input(shape=(100,)),
        layers.Dense(10, activation="relu"),
        layers.Dense(2, activation="sigmoid"),
    ]
)

print("TF:", tf.__version__)
print("Keras:", keras.__version__)
print("TFMOT:", tfmot.__version__)
print("Model type:", type(model))
print("isinstance(model, keras.Sequential):", isinstance(model, keras.Sequential))

quantized_model = tfmot.quantization.keras.quantize_model(model)
print(quantized_model)

Observed Output:

TF: 2.17.0
Keras: 3.13.2
TFMOT: 0.8.0
Model type: <class 'keras.src.models.sequential.Sequential'>
isinstance(model, keras.Sequential): True
ValueError: `to_quantize` can only either be a keras Sequential or Functional model.

Screenshots
N/A

Additional context
This does not appear to be GPU-specific. The same failure occurs before any actual GPU-dependent computation.

My environment during reproduction:

- keras==3.13.2
- tf_keras==2.17.0
- tensorflow==2.17.0
- tensorflow-model-optimization==0.8.0
- python==3.11.15

I also observed that the model is recognized as a standalone Keras Sequential model, but not as a TFMOT compat-Keras Sequential model. This suggests a type-identity mismatch between standalone Keras 3 and the Keras compatibility layer used inside TFMOT.

Point to be noted:
This looks like a bug in TFMOT type checking rather than a model construction bug in Keras.

More details about my environment:

OS: Linux
Install method: pip wheels
Reproducible: yes
GPU required: no
主要语言
Python
星标
1.6k
派生
348
30 天内没有已合并 PR

环境准备

  • 没有 Dockerfile 或 Docker Compose 文件
  • 没有 Pull Request 模板
  • 阅读贡献指南

从这里开始

  1. 先读完整个 Issue，再读项目的贡献指南。
  2. 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
  3. Fork 仓库，在一个分支上完成修改。
  4. 提交 Pull Request，并在描述里引用这个 Issue 编号。

tensorflow/model-optimization 的其他 Issue

查看 tensorflow/model-optimization 的全部 Issue

相似的 Issue

更多 Python Issue

把新 issue 发到你的邮箱

精选适合新手参与的 GitHub issue 摘要。