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)

Semi-supervised Representation Learning for Remote Sensing Image Classification Based on Generative Adversarial Networks

SSRL-for-image-classification Semi-supervised Representation Learning for Remote Sensing Image Classification Based on Generative Adversarial Networks

Feng 2 Nov 19, 2021
learning and feeling SLAM together with hands-on-experiments

modern-slam-tutorial-python Learning and feeling SLAM together with hands-on-experiments 😀 😃 😆 Dependencies Most of the examples are based on GTSAM

Giseop Kim 59 Dec 22, 2022
A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modeling

large-scale-ITE-UM-benchmark This repository contains code and data to reproduce the results of the paper "A Large Scale Benchmark for Individual Trea

10 Nov 19, 2022
BED: A Real-Time Object Detection System for Edge Devices

BED: A Real-Time Object Detection System for Edge Devices About this project Thi

Data Analytics Lab at Texas A&M University 44 Nov 18, 2022
Image Completion with Deep Learning in TensorFlow

Image Completion with Deep Learning in TensorFlow See my blog post for more details and usage instructions. This repository implements Raymond Yeh and

Brandon Amos 1.3k Dec 23, 2022
Code for unmixing audio signals in four different stems "drums, bass, vocals, others". The code is adapted from "Jukebox: A Generative Model for Music"

Status: Archive (code is provided as-is, no updates expected) Disclaimer This code is a based on "Jukebox: A Generative Model for Music" Paper We adju

Wadhah Zai El Amri 24 Dec 29, 2022
Code for Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights

Piggyback: https://arxiv.org/abs/1801.06519 Pretrained masks and backbones are available here: https://uofi.box.com/s/c5kixsvtrghu9yj51yb1oe853ltdfz4q

Arun Mallya 165 Nov 22, 2022
Official implementation of the NeurIPS 2021 paper Online Learning Of Neural Computations From Sparse Temporal Feedback

Online Learning Of Neural Computations From Sparse Temporal Feedback This repository is the official implementation of the NeurIPS 2021 paper Online L

Lukas Braun 3 Dec 15, 2021
Demonstrates how to divide a DL model into multiple IR model files (division) and introduce a simplest way to implement a custom layer works with OpenVINO IR models.

Demonstration of OpenVINO techniques - Model-division and a simplest-way to support custom layers Description: Model Optimizer in Intel(r) OpenVINO(tm

Yasunori Shimura 12 Nov 09, 2022
Neural Logic Inductive Learning

Neural Logic Inductive Learning This is the implementation of the Neural Logic Inductive Learning model (NLIL) proposed in the ICLR 2020 paper: Learn

36 Nov 28, 2022
My take on a practical implementation of Linformer for Pytorch.

Linformer Pytorch Implementation A practical implementation of the Linformer paper. This is attention with only linear complexity in n, allowing for v

Peter 349 Dec 25, 2022
IGCN : Image-to-graph convolutional network

IGCN : Image-to-graph convolutional network IGCN is a learning framework for 2D/3D deformable model registration and alignment, and shape reconstructi

Megumi Nakao 7 Oct 27, 2022
This code is the implementation of the paper "Coherence-Based Distributed Document Representation Learning for Scientific Documents".

Introduction This code is the implementation of the paper "Coherence-Based Distributed Document Representation Learning for Scientific Documents". If

tsc 0 Jan 11, 2022
This repo provides a demo for the CVPR 2021 paper "A Fourier-based Framework for Domain Generalization" on the PACS dataset.

FACT This repo provides a demo for the CVPR 2021 paper "A Fourier-based Framework for Domain Generalization" on the PACS dataset. To cite, please use:

105 Dec 17, 2022
Official code for 'Robust Siamese Object Tracking for Unmanned Aerial Manipulator' and offical introduction to UAMT100 benchmark

SiamSA: Robust Siamese Object Tracking for Unmanned Aerial Manipulator Demo video 📹 Our video on Youtube and bilibili demonstrates the evaluation of

Intelligent Vision for Robotics in Complex Environment 12 Dec 18, 2022
A-ESRGAN aims to provide better super-resolution images by using multi-scale attention U-net discriminators.

A-ESRGAN: Training Real-World Blind Super-Resolution with Attention-based U-net Discriminators The authors are hidden for the purpose of double blind

77 Dec 16, 2022
YOLOPのPythonでのONNX推論サンプル

YOLOP-ONNX-Video-Inference-Sample YOLOPのPythonでのONNX推論サンプルです。 ONNXモデルは、hustvl/YOLOP/weights を使用しています。 Requirement OpenCV 3.4.2 or later onnxruntime 1.

KazuhitoTakahashi 8 Sep 05, 2022
Technical Analysis Indicators - Pandas TA is an easy to use Python 3 Pandas Extension with 130+ Indicators

Pandas TA - A Technical Analysis Library in Python 3 Pandas Technical Analysis (Pandas TA) is an easy to use library that leverages the Pandas package

Kevin Johnson 3.2k Jan 09, 2023
Regularizing Generative Adversarial Networks under Limited Data (CVPR 2021)

Regularizing Generative Adversarial Networks under Limited Data [Project Page][Paper] Implementation for our GAN regularization method. The proposed r

Google 148 Nov 18, 2022
Repository for reproducing `Model-Based Robust Deep Learning`

Model-Based Robust Deep Learning (MBRDL) In this repository, we include the code necessary for reproducing the code used in Model-Based Robust Deep Le

Alex Robey 16 Sep 19, 2022