Deep Learning Toolbox Converter for TensorFlow Models

作者: MathWorks Deep Learning Toolbox Team
Import and export TensorFlow models within MATLAB
2.9
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8.8K 次下载
更新时间 2024/12/11
The converter for TensorFlow models enables you to import a pretrained TensorFlow model and weights and export a MATLAB network or layergraph as a TensorFlow model.
Import:
To import a TensorFlow network in MATLAB, please refer to importNetworkFromTensorFlow.
Note: the model must be saved in TensorFlow in the SavedModel format before importing to MATLAB.
For TensorFlow 2 models up to version 2.15, save the model (myModelTF, expressed as a tf.Keras.Model object) with the following command in TensorFlow first:
model.save("myModelTF")
For TensorFlow 2 models with versions 2.16 or later, TensorFlow will be installed with Keras 3 instead of Keras 2. To ensure compatibility with importNetworkFromTensorFlow, please build and save the save the model using the Keras 2 API using the following Python commands.
First, install the Keras 2 (tf_keras) package in bash:
pip install tf_keras
Then, precede any import tensorflow statement by the following lines of code:
import os
os.environ["TF_USE_LEGACY_KERAS"] = "1"
import tensorflow
Next, instead of importing the keras (Keras 3) package directly, import tf_keras (Keras 2) as keras:
import tf_keras as keras
from tf_keras import layers
Finally, save the model:
model.save("myModelTF")
The TensorFlow Model Importer has been tested up to 2.15. SavedModels saved with versions 2.15 and higher may also work without issues. When using tf_keras for versions 2.15 and higher, Keras 3 specific features such as Keras Ops will not be supported.
Export:
To export a TensorFlow network from MATLAB, please refer to exportNetworkToTensorFlow.
Export supports:
  • TensorFlow v2.0 or later, no support for Keras 3.0
  • Python version 3.0 or later
MATLAB 版本兼容性
创建方式 R2017b
兼容 R2017b 到 R2025a 的版本
平台兼容性
Windows macOS (Apple 芯片) macOS (Intel) Linux
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