| Name | keras-data-format-converter JSON |
| Version |
0.1.17
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| home_page | |
| Summary | Generates equal keras models with the desired data format |
| upload_time | 2024-01-18 12:52:11 |
| maintainer | |
| docs_url | None |
| author | dorhar |
| requires_python | >=3.8,<3.11 |
| license | |
| keywords |
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No requirements were recorded.
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# Keras data format converter
Generates equal keras models with the desired data format
## Requirements
tensorflow >= 2.0
## API
`convert_channels_first_to_last(model: keras.Model, inputs_to_transpose: List[str] = None, verbose: bool = False) -> keras.Model`
`convert_channels_last_to_first(model: tf.keras.Model, inputs_to_transpose: List[str] = None, verbose: bool = False) \
-> tf.keras.Model`
`model`: Keras model to convert
`inputs_to_transpose`: list of input names that need to be transposed due tothe data foramt changing
`verbose`: detailed output
## Getting started
```python
from tensorflow import keras
from keras_data_format_converter import convert_channels_last_to_first
# Load Keras model
keras_model = keras.models.load_model("my_image_model")
# Call the converter (image_input is an input that needs to be transposed, can be different for your model)
converted_model = convert_channels_last_to_first(keras_model, ["image_input"])
```
## Supported Layers with Special handling
- [X] Normalization layers
- [x] Permute
- [x] Reshape
- [x] Concatenate
- [ ] Dot
- [ ] MultiHeadAttention
- [ ] TFOpLambda (Inserted by the Functional API construction whenever users call
a supported TF symbol on KerasTensors, see [here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/layers/core.py#L1284) at Tensorflow repo for more info)
## Unsupported Layers due to lack of data_format property
- Cropping1D
- Upsampling1D
- Zeropadding1D
- All layers in tensorflow.keras.preprocessing
## How to deploy
- Create a new release version on GitHub
- Update parameters in setup.py (usually `version` and `download_url`)
- Run `python setup.py sdist` in root directory
- Run `pip install twine`
- Run `twine upload dist/*`
## License
This software is covered by MIT License.
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"description": "# Keras data format converter\n\nGenerates equal keras models with the desired data format \n\n\n## Requirements\ntensorflow >= 2.0\n\n\n## API\n`convert_channels_first_to_last(model: keras.Model, inputs_to_transpose: List[str] = None, verbose: bool = False) -> keras.Model`\n\n`convert_channels_last_to_first(model: tf.keras.Model, inputs_to_transpose: List[str] = None, verbose: bool = False) \\\n -> tf.keras.Model`\n\n`model`: Keras model to convert\n\n`inputs_to_transpose`: list of input names that need to be transposed due tothe data foramt changing \n\n`verbose`: detailed output\n\n## Getting started\n\n```python\nfrom tensorflow import keras\nfrom keras_data_format_converter import convert_channels_last_to_first\n\n# Load Keras model\nkeras_model = keras.models.load_model(\"my_image_model\")\n\n# Call the converter (image_input is an input that needs to be transposed, can be different for your model)\nconverted_model = convert_channels_last_to_first(keras_model, [\"image_input\"])\n```\n\n## Supported Layers with Special handling\n- [X] Normalization layers\n- [x] Permute\n- [x] Reshape\n- [x] Concatenate\n- [ ] Dot\n- [ ] MultiHeadAttention\n- [ ] TFOpLambda (Inserted by the Functional API construction whenever users call\n a supported TF symbol on KerasTensors, see [here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/layers/core.py#L1284) at Tensorflow repo for more info)\n\n## Unsupported Layers due to lack of data_format property\n- Cropping1D\n- Upsampling1D\n- Zeropadding1D\n- All layers in tensorflow.keras.preprocessing\n\n## How to deploy\n- Create a new release version on GitHub\n- Update parameters in setup.py (usually `version` and `download_url`)\n- Run `python setup.py sdist` in root directory\n- Run `pip install twine`\n- Run `twine upload dist/*`\n \n\n\n## License\nThis software is covered by MIT License.\n",
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