TensorFlow utilities for complex neural networks.
Project description
tf-complex
This package was inspired by the work of Elizabeth Cole et al.: Image Reconstruction using an Unrolled DL Architecture including Complex-Valued Convolution and Activation Functions. Please cite their work appropriately if you use this package. The code for their work is available here.
Installation
You can install tf-complex
using pypi:
pip install tf-complex
Example use
You can define a complex convolution in the following way to use in one of your models:
from tf_complex.convolutions import ComplexConv2D
conv = ComplexConv2D(
16,
3,
padding='same',
activation='crelu',
)
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
tf-complex-0.0.3.tar.gz
(5.3 kB
view details)
Built Distribution
File details
Details for the file tf-complex-0.0.3.tar.gz
.
File metadata
- Download URL: tf-complex-0.0.3.tar.gz
- Upload date:
- Size: 5.3 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.6.0 requests-toolbelt/0.9.1 tqdm/4.48.0 CPython/3.6.8
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 551325a915d407970c0a5d90390afcca77cfa0aff4455f2e609aa301ad21dfab |
|
MD5 | bb21e9c477ac3da7038f9b8cb9f85d8e |
|
BLAKE2b-256 | d3d03174c11c0e906c74e74638023944f45a82a22de9b3132efc2375830bd419 |
File details
Details for the file tf_complex-0.0.3-py3-none-any.whl
.
File metadata
- Download URL: tf_complex-0.0.3-py3-none-any.whl
- Upload date:
- Size: 7.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.6.0 requests-toolbelt/0.9.1 tqdm/4.48.0 CPython/3.6.8
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 150b7a346fba4df2dfe9f7d63034198d65b055b29a0c8dda93175e7d95dfe8e1 |
|
MD5 | f2f61431766ae4899f3744d054f77fb3 |
|
BLAKE2b-256 | 78191218b6ee4c2c0c08c5bec675023e70eba186da547fe69a6656139c482810 |