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https://github.com/sshane/Konverter

Convert simple Keras models to pure Python 🐍+ NumPy
https://github.com/sshane/Konverter

keras keras-tensorflow model-converter neural-networks python tensorflow

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Convert simple Keras models to pure Python 🐍+ NumPy

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README 

        
# Konverter ![Konverter Tests](https://github.com/ShaneSmiskol/Konverter/workflows/Konverter%20Tests/badge.svg)

### Convert your Keras models into pure Python 🐍+ NumPy.



The goal of this tool is to provide a quick and easy way to execute Keras models on machines or setups where utilizing TensorFlow/Keras is impossible. Specifically, in my case, to replace SNPE (Snapdragon Neural Processing Engine) for inference on phones with Python.



## Supported Keras Model Attributes

- Models:

  - Sequential

- Layers:

  - Dense

  - Dropout

    - Will be ignored during inference (SNPE 1.19 does NOT support dropout with Keras!)

  - SimpleRNN

    - Batch predictions do not currently work correctly.

  - GRU

    - **Important:** The current GRU support is based on [`GRU v3`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GRU) in tf.keras 2.1.0. It will not work correctly with older versions of TensorFlow if not using [`implementation=2` (example)](https://github.com/ShaneSmiskol/Konverter/blob/master/tests/build_test_models.py#L47).

    - Batch prediction untested

  - BatchNormalization

    - Works with all supported layers

- Activations:

  - ReLU

  - LeakyReLU (supports custom alphas)

  - Sigmoid

  - Softmax

  - Tanh

  - Linear/None



#### Roadmap 🛣

The project to do list can be [found here](https://github.com/ShaneSmiskol/Konverter/projects/1).



## Features 🤖

- Super quick conversion of your models. Takes less than a second. 🐱‍👤

- Usually reduces the size of Keras models by about 69.37%. 👌

- In some cases, prediction is quicker than Keras or SNPE (dense models). 🏎

  - RNNs: Since we lose the GPU using NumPy, predictions may be slower

- Stores the weights and biases of your model in a separate compressed NumPy file. 👇



## Benchmarks 📈

Benchmarks can be found in [BENCHMARKS.md](BENCHMARKS.md).



## Installation & Usage 🌍

### Install Konverter using pip:

`pip install keras-konverter`



### Konverting using the CLI: 🖥

`konverter examples/test_model.h5 examples/test_model.py`  (py suffix is optional)



Type `konverter` to get all possible arguments and flags!



- Arguments 💢:

  - `input_model`: Either the the location of your tf.keras .h5 model, or a preloaded Sequential model if using with Python. This is required

  - `output_file`: Optional file path for your output model, along with the weights file. Default is same name, same directory

- Flags 🎌:

  - `--indent, -i`: How many spaces to use for indentation, default is 2

  - `--silent, -i`: Whether you want Konverter to silently Konvert

  - `--no-watermark, -nw`: Removes the watermark prepended to the output model file






### Konverting programmatically: 🤖

All parameters with defaults: `konverter.konvert(input_model, output_file=None, indent=2, silent=False, no_watermark=False, tf_verbose=False)`

```python

>>> import konverter

>>> konverter.konvert('examples/test_model.h5', output_file='examples/test_model')

```



*Note: The model file will be saved as `f'{output_file}.py'` and the weights will be saved as `f'{output_file}_weights.npz'` in the same directory.* ***Make sure to change the path inside the model wrapper if you move the files after Konversion.***



---

That's it! If your model is supported (check [Supported Keras Model Attributes](#Supported-Keras-Model-Attributes)), then your newly converted Konverter model should be ready to go.



To predict: Import your model wrapper and run the `predict()` function. **❗Always double check that the outputs closely match your Keras model's❗** Automatic verification will come soon. **For the integrity of the predictions, always make sure your input is a `np.float32` array.**



```python

import numpy as np

from examples.test_model import predict

predict([np.random.rand(3).astype(np.float32)])

```



[See limitations and issues.](#Current-Limitations-and-Issues)



## Demo




## Dependencies

Thanks to [@apiad](https://github.com/apiad) you can now use [Poetry](https://github.com/python-poetry/poetry) to install all the needed dependencies for this tool! However the requirements are a pretty short list:

- It seems most versions of TensorFlow that include Keras work perfectly fine. Tested from 1.14 to 2.2 using Actions and no issues have occurred. **(Make sure you use implementation 2/v3 with GRU layers if not on TF 2.x)**

  - **Important**: You must create your models with tf.keras currently (not keras)

- Python >= 3.6 (for the glorious f-strings!)



To install all needed dependencies, simply `cd` into the base directory of Konverter, and run:



```

poetry install --no-dev

```



If you would like to use this version of Konverter (not from pip), then you may need to also run `poetry shell` after to enter poetry's virtualenv environment. **If you go down this path, make sure to remove `--no-dev` so TensorFlow installs in the venv!**



## Current Limitations and Issues 😬

- Dimensionality of input data:



  When working with models using `softmax`, the dimensionality of the input data matters. For example, predicting on the same data with different input dimensionality sometimes results in different outputs:

  ```python

  >>> model.predict([[1, 3, 5]])  # keras model, correct output

  array([[14.792273, 15.59787 , 15.543163]])

  >>> predict([[1, 3, 5]])  # Konverted model, wrong output

  array([[11.97839948, 18.09931636, 15.48014805]])

  >>> predict([1, 3, 5])  # And correct output

  array([14.79227209, 15.59786987, 15.54316282])

  ```



  If trying a batch prediction with classes and `softmax`, the model fails completely:

  ```python

  >>> predict([[0.5], [0.5]])

  array([[0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5]])

  ```



  Always double check that predictions are working correctly before deploying the model.

- Batch prediction with SimpleRNN (and possibly all RNN) layers



  Currently, the converted model has no way of determining if you're feeding a single prediction or a batch of predictions, and it will fail to give the correct output in certain cases (more likely with recurrent layers and softmax dense outputs layers). Support will be added soon.