simple-tensor 0.6.13

a simple package for handling tensorflow tensor

README

NEWS

Date	News	Version
August 2019	deeplab semantic segmentation (preview version)was release	>= v0.6.10
Mei 2019	yolov3 (stable version) was relesed	> v0.5.1
Mei 2019	U-net semantic segmentation (stable version) was released	> v0.5.1
April 2019	yolov3 (preview version)	v0.4.18
April 2019	Unet-segmentation (preview version)	v0.4.18

Tensorflow Compatibility

Tensorflow version	Simple-Tensor Version
1.4.1 - 1.12	>=v0.4.0
1.13.1	>=v0.4.3

ABOUT PROJECT

This project is a simplification of tensorflow operations and related projects

DEPENDENCIES

1. Tensorflow (1.4.1 - 1.13)

For installing tensorflow, with GPU:

# python3 
pip3 install tensorflow-gpu
# python2
pip2 install tensorflow-gpu

Without GPU:

# python3 
pip3 install tensorflow
# python2
pip2 install tensorflow

HOW TO USE

:shipit: Installing The Package

python setup.py install

or

pip3 install simple-tensor

:shipit: Import The Package

Tensor Operations

import tensorflow as tf
# tensor operations
from simple_tensor.tensor_operations import *
# tensor losses
from simple_tensor.tensor_losses import *
# tensor metrics
from simple_tensor.tensor_metrics import *

This packages contains tensor operation (conv2d, conv1d, depthwise conv2d, fully connected, conv2d transpose), tensor losses (softmax & sigmoid cross entropy, MSE), and tensor metrics (accuracy). For more detail documentations about tensor operations, visit this page

Convert Keras Model to Tensorflow Serving

import tensorflow as tf
from simple_tensor.convert import *

Transfer Learning Package

import tensorflow as tf
from simple_tensor.transfer_learning.inception_utils import *
from simple_tensor.transfer_learning.inception_v4 import *

This package contains a library of tensorflow implementation of Inception-v4 for image classification. Densenet, Resnet, and VGG will be added in the future version. For more detail documentations about transfer learning package, visit this page

(img source: link)

Object Detector Package

import tensorflow as tf
from simple_tensor.object_detector.detector_utils import *
from simple_tensor.object_detector.yolo_v4 import *

This package contains a library of tensorflow implementation of Yolov3 (training and inferencing). You can customize your yolo detector with four types of network ("big", 'medium", "small", "very_small"). For more detail documentations about object detector package (yolov3), visit this page.

(img source: pjreddie)

Unet Segmentation Package

import tensorflow as tf
from simple_tensor.segmentation.unet import UNet

This package contains the tensorflow implementation of U-net for semantic segmentation. For more detail, visit this page

(img source: internal)

LSTM Package

still on progress ....

DOCKER

We already prepared the all in one docker for computer vision and deep learning libraries, including tensorflow 1.12, Opencv3.4.2 and contrib, CUDA 9, CUDNN 7, Keras, jupyter, numpy, sklearn, scipy, statsmodel, pandas, matplotlib, seaborn, flask, gunicorn etc. See the list of dockerfile below:

Docker: Ubuntu 16.04 with GPU (Cuda 9, cudnn 7.2) [TESTED]

• https://github.com/fatchur/Opencv-contribt-Tensorflow-GPU-DS-Tools-Docker/tree/master/docker-16.04

Docker: Ubuntu 18.04 with GPU (Cuda 9, cudnn 7.2)

• https://github.com/fatchur/Opencv-contribt-Tensorflow-GPU-DS-Tools-Docker/tree/master/docker-18.04

Docker: Ubuntu 16.04 without GPU (Cuda 9, cudnn 7.2) [TESTED]

• https://github.com/fatchur/Opencv-contribt-Tensorflow-GPU-DS-Tools-Docker/tree/without_gpu/docker-16.04

Docker: Ubuntu 18.04 without GPU (Cuda 9, cudnn 7.2) [TESTED]

• https://github.com/fatchur/Opencv-contribt-Tensorflow-GPU-DS-Tools-Docker/tree/without_gpu/docker-18.04