Resolved ValueError: Layer weight shape (3, 3, 64, 128) not compatible with provided weight shape (3, 3,

Blogger Maotouhu () takes you to Go to New World?

Blog homepage:

  • Maotouhu’s blog
  • “Complete Column of Interview Questions” Articles with pictures and texts Vivid images Simple and easy to learn! Everyone is welcome to step in~
  • “IDEA Development Cheats Column” Learn the common operations of IDEA and double your work efficiency~
  • “Master Golang in 100 Days (Basic Introduction)” Learn the Golang language, play cloud native, and travel to large and small factories~

I hope this article can bring you some help The article is superficial, please criticize and correct me!

Article directory

  • Resolved ValueError: Layer weight shape (3, 3, 64, 128) not compatible with provided weight shape (3, 3, 128, 64)
    • Summary
    • Introduction
    • Text
      • 1. Cause of error
        • 1.1 Model structure mismatch
        • 1.2 Weight file version issue
      • 2. Solution
        • 2.1 Ensure that the model structure is consistent
        • 2.2 Use the correct version of the framework
      • 3. How to avoid
        • 3.1 Using weight checkpoints
        • 3.2 Maintain documentation
      • 4. Code and table examples
    • Summarize
    • References
  • Original statement

Resolved ValueError: Layer weight shape (3, 3, 64, 128) not compatible with provided weight shape (3, 3, 128, 64)

Summary

The cat-headed tiger blogger is here! Recently, I encountered a very common ValueError while training a deep learning model. This problem may have troubled everyone for a long time, especially those who are trying to load pre-trained models in Keras or TensorFlow. After thorough research, I found the cause of the problem, how to fix it, and how to avoid it. Below, I will share my experience in detail to help everyone successfully cross this hurdle.

Introduction

When we load a pre-trained model or migrate model weights, we may encounter different errors. One of them is the problem of weight shape mismatch. This problem may seem simple, but in fact the reasons behind it may involve multiple aspects and require careful investigation.

Text

1. Error reason

1.1 Model structure mismatch

If you change the structure of the model when loading the pre-trained model, or load the model weights with a different structure, then you are likely to encounter this problem.

1.2 Weight file version issue

Sometimes, the weight files of the pre-trained model may be incompatible with the framework version currently used, causing weight shape mismatch issues.

2. Solution

2.1 Ensure consistent model structure

Before loading weights, make sure your model structure is exactly the same as the pre-trained model.

from keras.models import Sequential
from keras.layers import Conv2D

model = Sequential()
model.add(Conv2D(64, (3,3), input_shape=(224,224,3)))
model.add(Conv2D(128, (3,3)))

# Load weights
model.load_weights('path_to_weights.h5')
2.2 Use the correct version of the framework

Make sure you are using a version of the framework that is compatible with the pretrained weight files. If you are not sure, you can check the official documentation of the weight file or inquire in the relevant community.

3. How to avoid

3.1 Using weight checkpoints

Use weight checkpoints to periodically save the model’s weights whenever you train a model. This way, if something goes wrong, you can easily roll back to a previous version.

3.2 Maintenance documentation

Every time you train a model or save weights, the structure of the model and the version of the framework used are documented in detail. This way, version or structure mismatch issues can be easily avoided when loading weights later.

4. Code and table examples

Version Is it compatible
2.1.0 ?
2.2.0 ? 
# Code examples
model.save_weights('path_to_save_weights.h5')

Summary

The problem of weight shape mismatch may cause headaches for many people, but as long as we deeply understand the reasons behind it and take appropriate precautions, such problems can be easily avoided. I hope this blog can help you, and you are welcome to share your experiences and opinions in the comment area.

Reference materials

  1. Keras official documentation
  2. TensorFlow weight loading related issues
  3. StackOverflow related discussions

Happy programming everyone!


Maotouhu recommends a list of necessary technology stacks for programmers:

Artificial Intelligence AI:

  1. Programming Language:
    • Python (currently the most popular AI development language)
    • R (mainly used for statistics and data analysis)
    • Julia (a high-performance scientific computing language that is gradually attracting attention)
  2. Deep Learning Framework:
    • TensorFlow (and its high-level API Keras)
    • ? PyTorch (and its high-level API torch.nn)
    • ?MXNet
    • Caffe
    • Theano (no longer maintained, but has great historical influence)
  3. Machine Learning Library:
    • scikit-learn (for traditional machine learning algorithms)
    • XGBoost, LightGBM (for decision trees and ensemble learning)
    • Statsmodels (for statistical models)
  4. Natural Language Processing:
    • NLTK
    • SpaCy
    • HuggingFace’s Transformers (for modern NLP models such as BERT and GPT)
  5. Computer Vision:
    • OpenCV
    • ? Pillow
  6. Reinforcement Learning:
    • OpenAI’s Gym
    • ? Ray’s Rllib
    • Stable Baselines
  7. Neural Network Visualization and Interpretation Tools:
    • TensorBoard (for TensorFlow)
    • Netron (for model structure visualization)
  8. Data processing and scientific computing:
    • Pandas (data processing)
    • NumPy, SciPy (scientific computing)
    • ?Matplotlib, Seaborn (data visualization)
  9. Parallel and distributed computing:
    • Apache Spark (for big data processing)
    • Dask (for parallel computing)
  10. GPU acceleration tools:
  • CUDA
  • cuDNN
  1. Cloud services and platforms:
  • AWS SageMaker
  • Google Cloud AI Platform
  • ? Microsoft Azure Machine Learning
  1. Model deployment and production:
  • Docker
  • Kubernetes
  • TensorFlow Serving
  • ONNX (for model exchange)
  1. Automated Machine Learning (AutoML):
  • H2O.ai
  • Google Cloud AutoML
  • Auto-sklearn

Original statement

======= ·

  • Original author: Maotouhu
  • Editor: AIMeowTiger

Author wx: [libin9iOak]
Public account: Maotouhu technical team

Study Review
? ?

This article is an original article and the copyright belongs to the author. Reprinting, duplication or quotation without permission is prohibited.

The author guarantees the authenticity and reliability of the information,but does not assume responsibility for its accuracy or completeness.

Commercial use without permission is prohibited.

If you have questions or suggestions, please contact the author.

Thank you for your support and respect.

Click on the business card below to join the IT technology core learning team. Explore the future of technology together and grow together.