Tensorflow wavelet Layers
Project description
tensorflow-wavelets is an implementation of Custom Layers for Neural Networks:
- Discrete Wavelets Transform Layer
- Duel Tree Complex Wavelets Transform Layer
- Multi Wavelets Transform Layer
git clone https://github.com/Timorleiderman/tensorflow-wavelets.git
cd tensorflow-wavelets
pip install -r requirements.txt
Installation
tested with python 3.8
pip install tensorflow-wavelets
Usage
from tensorflow import keras
import tensorflow_wavelets.Layers.DWT as DWT
import tensorflow_wavelets.Layers.DTCWT as DTCWT
import tensorflow_wavelets.Layers.DMWT as DMWT
# Custom Activation function Layer
import tensorflow_wavelets.Layers.Threshold as Threshold
Examples
DWT(name="haar", concat=0)
"name" can be found in pywt.wavelist(family)
concat = 0 means to split to 4 smaller layers
from tensorflow import keras
model = keras.Sequential()
model.add(keras.Input(shape=(28, 28, 1)))
model.add(DWT.DWT(name="haar",concat=0))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(nb_classes, activation="softmax"))
model.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dwt_9_haar (DWT) (None, 14, 14, 4) 0
_________________________________________________________________
flatten_9 (Flatten) (None, 784) 0
_________________________________________________________________
dense_9 (Dense) (None, 10) 7850
=================================================================
Total params: 7,850
Trainable params: 7,850
Non-trainable params: 0
_________________________________________________________________
name = "db4" concat = 1
model = keras.Sequential()
model.add(keras.layers.InputLayer(input_shape=(28, 28, 1)))
model.add(DWT.DWT(name="db4", concat=1))
model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dwt_db4 (DWT) (None, 34, 34, 1) 0
=================================================================
Total params: 0
Trainable params: 0
Non-trainable params: 0
_________________________________________________________________
DMWT
functional example with Sure Threshold
x_inp = keras.layers.Input(shape=(512, 512, 1))
x = DMWT.DMWT("ghm")(x_inp)
x = Threshold.Threshold(algo='sure', mode='hard')(x) # use "soft" or "hard"
x = DMWT.IDMWT("ghm")(x)
model = keras.models.Model(x_inp, x, name="MyModel")
model.summary()
Model: "MyModel"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 512, 512, 1)] 0
_________________________________________________________________
dmwt (DMWT) (None, 1024, 1024, 1) 0
_________________________________________________________________
sure_threshold (SureThreshol (None, 1024, 1024, 1) 0
_________________________________________________________________
idmwt (IDMWT) (None, 512, 512, 1) 0
=================================================================
Total params: 0
Trainable params: 0
Non-trainable params: 0
_________________________________________________________________
PyPi upload:
pip install --upgrade build
pip install --upgrade twine
python -m build
Free Software, Hell Yeah!
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
tensorflow-wavelets-1.0.29.tar.gz
(19.9 kB
view hashes)
Built Distribution
Close
Hashes for tensorflow-wavelets-1.0.29.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 40740b2193ee7bfc9b07885931911e279b0227bd9b70421db2ca80a10d326932 |
|
MD5 | ae707ebf7d0c9dd04e27778521b6312e |
|
BLAKE2b-256 | 6bedf628d8499a9202bd3beb0033b6a74ad48e870ee428fbfc3f79f98fd9fa71 |
Close
Hashes for tensorflow_wavelets-1.0.29-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 70d34572f5527b34310b7a1a6bf0e691dac784f8b2aa1ca4507b8921bcfb39ba |
|
MD5 | 6c71a8a65f7b76fd1ea602d9743c8b8e |
|
BLAKE2b-256 | 226d91b9620821de3894824c7bfc70cac3f08f6fec1931d50445b03dee918f4a |