Super_TF

Simple framework to construct machine learning models with tensorflow

Overview

SuperTF was initially conceived as a means to get familiar with Tensorflow by constructing machine learning models and executing the Tensorflow Tutorials

I have expanded SuperTF overtime and now it has a suite of tools to help in:

• Generation of datasets as tfrecords files (Currently supports Semantic segmentation, Classification and Sequence Generation)
• Rapid Prototyping of Deep learning models
• Network and Data visualization via tensorboard
• Session management for extended training sessions

Examples

Please refer to the examples for:

• Classification dataset generation
• Classification dataset reading
• Training LeNet
• Training AlexNet (Semantic segmentation examples will be added shortly)

Included Network Architectures

I’ve added a several neural network architectures:

Classification:

• LeNet - Gradient based learning applied to document recognition
• AlexNet - ImageNet Classification with Deep Convolutional Neural Networks
• Vgg16 - Very Deep Convolutional Networks for Large-Scale Image Recognition
• Vgg19 - Very Deep Convolutional Networks for Large-Scale Image Recognition
• Inception-resnet-v2-paper - Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
• Inception-resnet-v2-published - Improving Inception and Image Classification in TensorFlow

Segmentation:

• Full-Resolution Residual Network-A - Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes

Sequence-Generation:

• Im2txt: Caption generation model ported from Tensorflow im2txt

Custom:

I've edited and added to certain network architectures to fulfill a certain niche or to improve their performance. These networks are:

• Unet1024 - U-Net: Convolutional Networks for Biomedical Image Segmentation

  Unet1024 is a simple extension of the orginal Unet architecture, the network accepts an image of size 1024 x 1024 and has 7 encoder-decoder pairs.

• Full-Resolution Residual Network-C - Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes

  FRRN-C is build upon FRRN-A. Here the center Full-Resolution residual block is replaced by densely conected block of dialated convolutions. Moreover the Full-Resolution Residual Network is enclosed in an encoder decoder pair which doubles the input and output resolution.

• P-Net

• Fine-Net

• Attn-Lstm Attn_Lstm is a multilayer Long short term memory network with BahdanauAttention. Initial state is set via feature vectors extracted from inception-resent-v2a. Used for image to text generation.

Currently working on:

• Upgrading architectures to individual classes
• Preparing wrapper to work with both TF and Pytorch as backend