Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning

Related tags

Deep Learningskflow
Overview

SkFlow has been moved to Tensorflow.

SkFlow has been moved to http://github.com/tensorflow/tensorflow into contrib folder specifically located here. The development will continue there. Please submit any issues and pull requests to Tensorflow repository instead.

This repository will ramp down, including after next Tensorflow release we will wind down code here. Please see instructions on most recent installation here.

Comments
  • How do I do multilabel image classification?

    How do I do multilabel image classification?

    Do I have to make changes in the multioutput file? I ideally want to train any model, like Inception, on my training data which has multi labels. How do I do that?

    help wanted examples 
    opened by unography 21
  • Add early stopping and reporting based on validation data

    Add early stopping and reporting based on validation data

    This PR allows a user to specify a validation dataset that are used for early stopping (and reporting). The PR was created to address issue 85

    I made changes in 3 places.

    1. The trainer now takes a dictionary containing the validation data (in the same format as the output of the data feeder's get_dict_fn).
    2. The fit method now takes arguments for val_X and val_y. It converts these into the correct format for the trainer.
    3. The example file digits.py now uses early stopping, by supplying val_X and val_y.

    I can add early stopping to other examples if this approach looks good, though their behavior should not otherwise be affected by the current PR.

    cla: yes 
    opened by dansbecker 14
  • Class weight support

    Class weight support

    Hi,

    I am using skflow.ops.dnn to classify two - classes dataset (True and False). The percentage of True example is very small, so I have an imbalanced dataset.

    It seems to me that one way to resolve the issue is to use weighted classes. However, when I look to the implementation of skflow.ops.dnn, I do not know how could I do weighted classes with DNN.

    Is it possible to do that with skflow, or is there another technique to deal with imbalanced dataset problem in skflow?

    Thanks

    enhancement 
    opened by vinhqdang 13
  • Added verbose option

    Added verbose option

    I added an option to control the "verbosity". For this, I added the parameter "verbose" in the init method of the init.py file and to the train function in the trainers.py file. In addition, I passed this argument to the "self._trainer.train()" call in the init file and added a condition to make the prints in the trainer.py file.

    cla: no 
    opened by ivallesp 12
  • Predict batch size default

    Predict batch size default

    This changes the default batch size for prediction to be the same as for training, enabling efficient grid search. Previously GridSearchCV would try to make predictions in a single batch, which could take a lot of memory.

    This also adds a simple example of using skflow with GridSearchCV.

    cla: no 
    opened by mheilman 11
  • Add example accessing of weights

    Add example accessing of weights

    It wasn't clear how to access weights using classifier.get_tensor_value('foo') syntax. This adds some examples for the CNN model. They were figured out by logging the training as though for using TensorBoard, and then running strings on the logfile to look for the right namespace.

    Is there a better way to access these weights? Or to learn their names? The logging must walk through the graph and record these names. Maybe if there were a way to quickly list all the names, that'd be enough for advanced users to figure it out.

    cla: yes 
    opened by dvbuntu 10
  • Plotting neural network built by skflow

    Plotting neural network built by skflow

    Hi,

    Sorry I asked too much.

    I think plotting is always a nice feature. Is it possible right now for skflow (or can we do that through tensorflow directly)?

    opened by vinhqdang 10
  • move monitor and logdir arguments to init

    move monitor and logdir arguments to init

    opened by mheilman 8
  • Exception when running language model example

    Exception when running language model example

    Hi,

    Thanks for making this tool. It will definitely make things easier for NN newcomers.

    I just tried running your language model example and got the following exception:

    Traceback (most recent call last):
      File "test.py", line 84, in <module>
        estimator.fit(X, y)
      File "/Users/aleksandar/tensorflow/lib/python3.5/site-packages/skflow/estimators/base.py", line 243, in fit
        feed_params_fn=self._data_feeder.get_feed_params)
      File "/Users/aleksandar/tensorflow/lib/python3.5/site-packages/skflow/trainer.py", line 114, in train
        feed_dict = feed_dict_fn()
      File "/Users/aleksandar/tensorflow/lib/python3.5/site-packages/skflow/io/data_feeder.py", line 307, in _feed_dict_fn
        inp[i, :] = six.next(self.X)
    StopIteration
    

    I made sure that my python distribution has the correct version of six. I tried running it both in a virtual environment and in a normal Python 3 distro. Any ideas what might be causing this?

    opened by savkov 7
  • another ValidationMonitor with validation(+early stopping) per epoch

    another ValidationMonitor with validation(+early stopping) per epoch

    From what I understand, the existing ValidationMonitor performs validation every [print_steps] steps, and checks for stop condition every [early_stopping_rounds] steps. I'd like to add another ValidationMonitor that performs validation once and checks for stoping condition once every epoch. Is this the recommended practice in machine learning regarding validation and early stopping? I mean I'd like to add a fit process something like this:

    def fit(self, x_train, y_train, x_validate, y_validate):
        while (current_validation_loss < previous_validation_loss):
            estimator.train_one_more_epoch(x_train, y_train)
            previous_validation_loss = current_validation_loss
            current_validation_loss = some_error(y_validate, estimator.predict(x_validate))
    
    enhancement help wanted 
    opened by alanyuchenhou 7
  • Example of language model

    Example of language model

    Add an example of language model (RNN). For example character level on sheikspear book (similar to https://github.com/sherjilozair/char-rnn-tensorflow).

    examples 
    opened by ilblackdragon 7
  • .travis.yml: The 'sudo' tag is now deprecated in Travis CI

    .travis.yml: The 'sudo' tag is now deprecated in Travis CI

    opened by cclauss 1
  • Why hasn't this repo been archived yet?

    Why hasn't this repo been archived yet?

    New versions of TF have already been released since the last commit to this repo. As far as I've understood, after having read the README file of this project, you intended to close this repo. So, why hasn't it been done yet?

    opened by nbro 0
Releases(v0.1)
  • v0.1(Feb 14, 2016)

Public Code for NIPS submission SimiGrad: Fine-Grained Adaptive Batching for Large ScaleTraining using Gradient Similarity Measurement

Public code for NIPS submission "SimiGrad: Fine-Grained Adaptive Batching for Large Scale Training using Gradient Similarity Measurement" This repo co

Heyang Qin 0 Oct 13, 2021
TorchOk - The toolkit for fast Deep Learning experiments in Computer Vision

TorchOk - The toolkit for fast Deep Learning experiments in Computer Vision

52 Dec 23, 2022
RoFormer_pytorch

PyTorch RoFormer 原版Tensorflow权重(https://github.com/ZhuiyiTechnology/roformer) chinese_roformer_L-12_H-768_A-12.zip (提取码:xy9x) 已经转化为PyTorch权重 chinese_r

yujun 283 Dec 12, 2022
Implementation of the paper "Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning"

Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning This is the implementation of the paper "Self-Promoted Prototype Refinement

Kai Zhu 78 Dec 02, 2022
TorchDistiller - a collection of the open source pytorch code for knowledge distillation, especially for the perception tasks, including semantic segmentation, depth estimation, object detection and instance segmentation.

This project is a collection of the open source pytorch code for knowledge distillation, especially for the perception tasks, including semantic segmentation, depth estimation, object detection and i

yifan liu 147 Dec 03, 2022
Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics

Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics

14 Nov 06, 2022
YOLOX-Paddle - A reproduction of YOLOX by PaddlePaddle

YOLOX-Paddle A reproduction of YOLOX by PaddlePaddle 数据集准备 下载COCO数据集,准备为如下路径 /ho

QuanHao Guo 6 Dec 18, 2022
SlideGraph+: Whole Slide Image Level Graphs to Predict HER2 Status in Breast Cancer

SlideGraph+: Whole Slide Image Level Graphs to Predict HER2 Status in Breast Cancer A novel graph neural network (GNN) based model (termed SlideGraph+

28 Dec 24, 2022
modelvshuman is a Python library to benchmark the gap between human and machine vision

modelvshuman is a Python library to benchmark the gap between human and machine vision. Using this library, both PyTorch and TensorFlow models can be evaluated on 17 out-of-distribution datasets with

Bethge Lab 244 Jan 03, 2023
[ICCV'21] UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction

UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction Project Page | Paper | Supplementary | Video This reposit

331 Dec 28, 2022
A project that uses optical flow and machine learning to detect aimhacking in video clips.

waldo-anticheat A project that aims to use optical flow and machine learning to visually detect cheating or hacking in video clips from fps games. Che

waldo.vision 542 Dec 03, 2022
wmctrl ported to Python Ctypes

work in progress wmctrl is a command that can be used to interact with an X Window manager that is compatible with the EWMH/NetWM specification. wmctr

Iyad Ahmed 22 Dec 31, 2022
[CVPR 2021] Official PyTorch Implementation for "Iterative Filter Adaptive Network for Single Image Defocus Deblurring"

IFAN: Iterative Filter Adaptive Network for Single Image Defocus Deblurring Checkout for the demo (GUI/Google Colab)! The GUI version might occasional

Junyong Lee 173 Dec 30, 2022
Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation

Unseen Object Clustering: Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation Introduction In this work, we propose a new method

NVIDIA Research Projects 132 Dec 13, 2022
[Official] Exploring Temporal Coherence for More General Video Face Forgery Detection(ICCV 2021)

Exploring Temporal Coherence for More General Video Face Forgery Detection(FTCN) Yinglin Zheng, Jianmin Bao, Dong Chen, Ming Zeng, Fang Wen Accepted b

57 Dec 28, 2022
Welcome to The Eigensolver Quantum School, a quantum computing crash course designed by students for students.

TEQS Welcome to The Eigensolver Quantum School, a crash course designed by students for students. The aim of this program is to take someone who has n

The Eigensolvers 53 May 18, 2022
Remote sensing change detection tool based on PaddlePaddle

PdRSCD PdRSCD(PaddlePaddle Remote Sensing Change Detection)是一个基于飞桨PaddlePaddle的遥感变化检测的项目,pypi包名为ppcd。目前0.2版本,最新支持图像列表输入的训练和预测,如多期影像、多源影像甚至多期多源影像。可以快速完

38 Aug 31, 2022
A curated list of long-tailed recognition resources.

Awesome Long-tailed Recognition A curated list of long-tailed recognition and related resources. Please feel free to pull requests or open an issue to

Zhiwei ZHANG 542 Jan 01, 2023
PyTorch implementation of Memory-based semantic segmentation for off-road unstructured natural environments.

MemSeg: Memory-based semantic segmentation for off-road unstructured natural environments Introduction This repository is a PyTorch implementation of

11 Nov 28, 2022
PyTorch Code for "Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learning"

Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learning [Project Page] [Paper] Wenlong Huang1, Igor Mordatch2, Pieter Abbeel1,

Wenlong Huang 40 Nov 22, 2022