Malvina  O'Hara

Malvina O'Hara

1621924048

Pytorch Implementation of Some Learning Rate Schedulers for Deep Learning Researcher

pytorch-lr-scheduler

PyTorch implementation of some learning rate schedulers for deep learning researcher.

Usage

WarmupReduceLROnPlateauScheduler

  • Visualize

  • Example code
import torch

from lr_scheduler.warmup_reduce_lr_on_plateau_scheduler import WarmupReduceLROnPlateauScheduler

if __name__ == '__main__':
    max_epochs, steps_in_epoch = 10, 10000

    model = [torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))]
    optimizer = torch.optim.Adam(model, 1e-10)

    scheduler = WarmupReduceLROnPlateauScheduler(
        optimizer, 
        init_lr=1e-10, 
        peak_lr=1e-4, 
        warmup_steps=30000, 
        patience=1,
        factor=0.3,
    )

    for epoch in range(max_epochs):
        for timestep in range(steps_in_epoch):
            ...
            ...
            if timestep < warmup_steps:
                scheduler.step()

        val_loss = validate()
        scheduler.step(val_loss)

TransformerLRScheduler

  • Visualize

  • Example code
import torch

from lr_scheduler.transformer_lr_scheduler import TransformerLRScheduler

if __name__ == '__main__':
    max_epochs, steps_in_epoch = 10, 10000

    model = [torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))]
    optimizer = torch.optim.Adam(model, 1e-10)

    scheduler = TransformerLRScheduler(
        optimizer=optimizer, 
        init_lr=1e-10, 
        peak_lr=0.1,
        final_lr=1e-4, 
        final_lr_scale=0.05,
        warmup_steps=3000, 
        decay_steps=17000,
    )

    for epoch in range(max_epochs):
        for timestep in range(steps_in_epoch):
            ...
            ...
            scheduler.step()

TriStageLRScheduler

  • Visualize

  • Example code
import torch

from lr_scheduler.tri_stage_lr_scheduler import TriStageLRScheduler

if __name__ == '__main__':
    max_epochs, steps_in_epoch = 10, 10000

    model = [torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))]
    optimizer = torch.optim.Adam(model, 1e-10)

    scheduler = TriStageLRScheduler(
        optimizer, 
        init_lr=1e-10, 
        peak_lr=1e-4, 
        final_lr=1e-7, 
        init_lr_scale=0.01, 
        final_lr_scale=0.05,
        warmup_steps=30000, 
        hold_steps=70000, 
        decay_steps=100000,
        total_steps=200000,
    )

    for epoch in range(max_epochs):
        for timestep in range(steps_in_epoch):
            ...
            ...
            scheduler.step()

ReduceLROnPlateauScheduler

  • Visualize

  • Example code
import torch

from lr_scheduler.reduce_lr_on_plateau_lr_scheduler import ReduceLROnPlateauScheduler

if __name__ == '__main__':
    max_epochs, steps_in_epoch = 10, 10000

    model = [torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))]
    optimizer = torch.optim.Adam(model, 1e-4)

    scheduler = ReduceLROnPlateauScheduler(optimizer, patience=1, factor=0.3)

    for epoch in range(max_epochs):
        for timestep in range(steps_in_epoch):
            ...
            ...

        val_loss = validate()
        scheduler.step(val_loss)

WarmupLRScheduler

  • Visualize

  • Example code
import torch

from lr_scheduler.warmup_lr_scheduler import WarmupLRScheduler

if __name__ == '__main__':
    max_epochs, steps_in_epoch = 10, 10000

    model = [torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))]
    optimizer = torch.optim.Adam(model, 1e-10)

    scheduler = WarmupLRScheduler(
        optimizer, 
        init_lr=1e-10, 
        peak_lr=1e-4, 
        warmup_steps=4000,
    )

    for epoch in range(max_epochs):
        for timestep in range(steps_in_epoch):
            ...
            ...
            scheduler.step()

Troubleshoots and Contributing

If you have any questions, bug reports, and feature requests, please open an issue on Github.

I appreciate any kind of feedback or contribution. Feel free to proceed with small issues like bug fixes, documentation improvement. For major contributions and new features, please discuss with the collaborators in corresponding issues.

Code Style

I follow PEP-8 for code style. Especially the style of docstrings is important to generate documentation.

Download Details:

Author: sooftware
Download Link: Download The Source Code
Official Website: https://github.com/sooftware/pytorch-lr-scheduler
License: MIT - see the LICENSE.md file for details

#deep-learning #pytorch

What is GEEK

Buddha Community

Pytorch Implementation of Some Learning Rate Schedulers for Deep Learning Researcher
Marget D

Marget D

1618317562

Top Deep Learning Development Services | Hire Deep Learning Developer

View more: https://www.inexture.com/services/deep-learning-development/

We at Inexture, strategically work on every project we are associated with. We propose a robust set of AI, ML, and DL consulting services. Our virtuoso team of data scientists and developers meticulously work on every project and add a personalized touch to it. Because we keep our clientele aware of everything being done associated with their project so there’s a sense of transparency being maintained. Leverage our services for your next AI project for end-to-end optimum services.

#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services

Mikel  Okuneva

Mikel Okuneva

1603735200

Top 10 Deep Learning Sessions To Look Forward To At DVDC 2020

The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.

Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:

Also Read: Why Deep Learning DevCon Comes At The Right Time


Adversarial Robustness in Deep Learning

By Dipanjan Sarkar

**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.

Read an interview with Dipanjan Sarkar.

Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER

By Divye Singh

**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.

Default Rate Prediction Models for Self-Employment in Korea using Ridge, Random Forest & Deep Neural Network

By Dongsuk Hong

About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.


#opinions #attend dldc 2020 #deep learning #deep learning sessions #deep learning talks #dldc 2020 #top deep learning sessions at dldc 2020 #top deep learning talks at dldc 2020

PyTorch For Deep Learning 

What is Pytorch ?

Pytorch is a Deep Learning Library Devoloped by Facebook. it can be used for various purposes such as Natural Language Processing , Computer Vision, etc

Prerequisites

Python, Numpy, Pandas and Matplotlib

Tensor Basics

What is a tensor ?

A Tensor is a n-dimensional array of elements. In pytorch, everything is a defined as a tensor.

#pytorch #pytorch-tutorial #pytorch-course #deep-learning-course #deep-learning

Tia  Gottlieb

Tia Gottlieb

1595356740

Converting deep learning research papers to useful code

If deep learning is a super power, then turning theories from a paper to usable code is a hyper power

Image for post

Why should I learn to implement machine learning research papers?

As I’ve said, being able to convert a paper to code is definitely a hyper power, especially in a field like machine learning which is moving faster and faster each day.

Most research papers come from people within giant tech companies or universities who may be PhD holders or the ones who are working on the cutting edge technologies.

What else can be more cool than being able to reproduce the research done by these top notch professionals. Another thing to note is that the ones who can reproduce research papers as code is in huge demand.

Once you get the knack of implementing research papers, you will be in a state on par with these researchers.

These researchers too has acquired these skills through the practice of reading and implementing research papers.

How do I read and implement papers?

You might say, “Hm, I have a general understanding of the deep learning algorithms like fully connected networks, convolutional neural networks, recurrent neural networks, but the problem is that I would like to develop SOTA(state of the art) voice cloning AI but I know nothing about voice cloning :( ”.

Okay, here is your answer(some parts of my method is taken from Andrew Ng’s advice on reading papers).

If you want to learn about a specific topic:

  1. Collect 5–6 papers related to the specific topic(you can simply search arxiv or similar websites to get papers related to a topic).
  2. Don’t read a single paper completely, instead skim through all of the papers and pick a paper that interests you or if you had a specific paper in mind, go pick it up, no one can stop you.
  3. Read the abstract carefully and understand the idea from a high level and see whether your interest still persists, if so continue to skim through the images and see whether you can make assumptions on what the paper might be about.
  4. Now read the introduction carefully line by line because most of what the paper contains will be explained here in the most simplest manner with minimal math.
  5. If you wish, you can skip the math equations in the first pass, don’t skip the math if the Greek letters are familiar.
  6. At any situation, if you get stuck or some words are confusing, never hesitate to google it. No one is born as master of everything ;)
  7. After completing the first pass, you will be in a state where you understand the high level view of what the paper is trying prove or improve.
  8. In the second pass, try to understand almost everything in the paper and if you encounter any pseudo-code, try to convert it into your python library of choice(PyTorch, TensorFlow…)
  9. You can get more papers to read and get a better understanding of the field by going to the references section of each paper(same as connecting the dots).

💡 Some tips for effectively understanding a paper:

  • If you are a beginner to reading research papers, it’s good to read some blog posts and videos related to that topic/research paper before reading the paper itself. This makes your job easier and you will not be discouraged by all that Greek letters.
  • Always take notes and highlight important points in the research paper for easier reference while implementing the code for the paper.
  • If you are new to implementing research papers and get stuck any where, it’s not a bad idea to go through open source implementations and see how others have done this.

#deep-learning #research #unsupervised-learning #machine-learning #deep learning

Malvina  O'Hara

Malvina O'Hara

1621924048

Pytorch Implementation of Some Learning Rate Schedulers for Deep Learning Researcher

pytorch-lr-scheduler

PyTorch implementation of some learning rate schedulers for deep learning researcher.

Usage

WarmupReduceLROnPlateauScheduler

  • Visualize

  • Example code
import torch

from lr_scheduler.warmup_reduce_lr_on_plateau_scheduler import WarmupReduceLROnPlateauScheduler

if __name__ == '__main__':
    max_epochs, steps_in_epoch = 10, 10000

    model = [torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))]
    optimizer = torch.optim.Adam(model, 1e-10)

    scheduler = WarmupReduceLROnPlateauScheduler(
        optimizer, 
        init_lr=1e-10, 
        peak_lr=1e-4, 
        warmup_steps=30000, 
        patience=1,
        factor=0.3,
    )

    for epoch in range(max_epochs):
        for timestep in range(steps_in_epoch):
            ...
            ...
            if timestep < warmup_steps:
                scheduler.step()

        val_loss = validate()
        scheduler.step(val_loss)

TransformerLRScheduler

  • Visualize

  • Example code
import torch

from lr_scheduler.transformer_lr_scheduler import TransformerLRScheduler

if __name__ == '__main__':
    max_epochs, steps_in_epoch = 10, 10000

    model = [torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))]
    optimizer = torch.optim.Adam(model, 1e-10)

    scheduler = TransformerLRScheduler(
        optimizer=optimizer, 
        init_lr=1e-10, 
        peak_lr=0.1,
        final_lr=1e-4, 
        final_lr_scale=0.05,
        warmup_steps=3000, 
        decay_steps=17000,
    )

    for epoch in range(max_epochs):
        for timestep in range(steps_in_epoch):
            ...
            ...
            scheduler.step()

TriStageLRScheduler

  • Visualize

  • Example code
import torch

from lr_scheduler.tri_stage_lr_scheduler import TriStageLRScheduler

if __name__ == '__main__':
    max_epochs, steps_in_epoch = 10, 10000

    model = [torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))]
    optimizer = torch.optim.Adam(model, 1e-10)

    scheduler = TriStageLRScheduler(
        optimizer, 
        init_lr=1e-10, 
        peak_lr=1e-4, 
        final_lr=1e-7, 
        init_lr_scale=0.01, 
        final_lr_scale=0.05,
        warmup_steps=30000, 
        hold_steps=70000, 
        decay_steps=100000,
        total_steps=200000,
    )

    for epoch in range(max_epochs):
        for timestep in range(steps_in_epoch):
            ...
            ...
            scheduler.step()

ReduceLROnPlateauScheduler

  • Visualize

  • Example code
import torch

from lr_scheduler.reduce_lr_on_plateau_lr_scheduler import ReduceLROnPlateauScheduler

if __name__ == '__main__':
    max_epochs, steps_in_epoch = 10, 10000

    model = [torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))]
    optimizer = torch.optim.Adam(model, 1e-4)

    scheduler = ReduceLROnPlateauScheduler(optimizer, patience=1, factor=0.3)

    for epoch in range(max_epochs):
        for timestep in range(steps_in_epoch):
            ...
            ...

        val_loss = validate()
        scheduler.step(val_loss)

WarmupLRScheduler

  • Visualize

  • Example code
import torch

from lr_scheduler.warmup_lr_scheduler import WarmupLRScheduler

if __name__ == '__main__':
    max_epochs, steps_in_epoch = 10, 10000

    model = [torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))]
    optimizer = torch.optim.Adam(model, 1e-10)

    scheduler = WarmupLRScheduler(
        optimizer, 
        init_lr=1e-10, 
        peak_lr=1e-4, 
        warmup_steps=4000,
    )

    for epoch in range(max_epochs):
        for timestep in range(steps_in_epoch):
            ...
            ...
            scheduler.step()

Troubleshoots and Contributing

If you have any questions, bug reports, and feature requests, please open an issue on Github.

I appreciate any kind of feedback or contribution. Feel free to proceed with small issues like bug fixes, documentation improvement. For major contributions and new features, please discuss with the collaborators in corresponding issues.

Code Style

I follow PEP-8 for code style. Especially the style of docstrings is important to generate documentation.

Download Details:

Author: sooftware
Download Link: Download The Source Code
Official Website: https://github.com/sooftware/pytorch-lr-scheduler
License: MIT - see the LICENSE.md file for details

#deep-learning #pytorch