Made in collaboration with Chris George for Art + ML Spring 2019.

Overview

Deepdream Eyes

Made in collaboration with Chris George for Art + ML Spring 2019. Course Website: https://kangeunsu.com/artml19s/

Final Results

Original images are the inital_# images.

layer = ‘mixed4d_3x3_bottleneck_pre_relu’ T(layer)[:,:,:,142] + T(layer)[:,:,:,8]

All Channels Video

https://youtu.be/BRbcq71nEtY

Code

deep_dream_edit.py

The code we wrote was only for easily running a photo on a specific layer and every channel in that layer. We modified the render_deapdream function so that it returned the image to be saved into the correct directory.

def render_deepdream(...)
    ...
    return PIL.Image.fromarray(np.uint8(np.clip(img/255.0, 0, 1)*255))

image_name = 'insert_image_path'
layer = 'mixed4d_3x3_bottleneck_pre_relu'
new_file_path = './'+layer+'/'
for i in range(1, 84):
    img0 = PIL.Image.open(image_name)
    img0 = np.float32(img0)
    deep_dream_image = render_deepdream(T(layer)[:,:,:,i], img0)
    deep_dream_image.save(new_file_path+str(i)+'.jpeg')

We included two of those tests in this repo. One was for mized4d_3x3_bottleneck_pre_relu and another was for mixed4b_3x3_bottleneck_pre_relu.

Intermediate Results

We also had some intermediate results before we settled on eyes for our final project. Some of those results can be found in the intermediate results folder.

Owner
Francisco Cabrera
Francisco Cabrera
Machine Learning Study 혼자 해보기

Machine Learning Study 혼자 해보기 기여자 (Contributors) ✨ Teddy Lee 🏠 HongJaeKwon 🏠 Seungwoo Han 🏠 Tae Heon Kim 🏠 Steve Kwon 🏠 SW Song 🏠 K1A2 🏠 Wooil

Teddy Lee 1.7k Jan 01, 2023
Simple structured learning framework for python

PyStruct PyStruct aims at being an easy-to-use structured learning and prediction library. Currently it implements only max-margin methods and a perce

pystruct 666 Jan 03, 2023
This handbook accompanies the course: Machine Learning with Hung-Yi Lee

This handbook accompanies the course: Machine Learning with Hung-Yi Lee

RenChu Wang 472 Dec 31, 2022
learn python in 100 days, a simple step could be follow from beginner to master of every aspect of python programming and project also include side project which you can use as demo project for your personal portfolio

learn python in 100 days, a simple step could be follow from beginner to master of every aspect of python programming and project also include side project which you can use as demo project for your

BDFD 6 Nov 05, 2022
Uses WiFi signals :signal_strength: and machine learning to predict where you are

Uses WiFi signals and machine learning (sklearn's RandomForest) to predict where you are. Even works for small distances like 2-10 meters.

Pascal van Kooten 5k Jan 09, 2023
🚪✊Knock Knock: Get notified when your training ends with only two additional lines of code

Knock Knock A small library to get a notification when your training is complete or when it crashes during the process with two additional lines of co

Hugging Face 2.5k Jan 07, 2023
OptaPy is an AI constraint solver for Python to optimize planning and scheduling problems.

OptaPy is an AI constraint solver for Python to optimize the Vehicle Routing Problem, Employee Rostering, Maintenance Scheduling, Task Assignment, School Timetabling, Cloud Optimization, Conference S

OptaPy 208 Dec 27, 2022
slim-python is a package to learn customized scoring systems for decision-making problems.

slim-python is a package to learn customized scoring systems for decision-making problems. These are simple decision aids that let users make yes-no p

Berk Ustun 37 Nov 02, 2022
This is the code repository for Interpretable Machine Learning with Python, published by Packt.

Interpretable Machine Learning with Python, published by Packt

Packt 299 Jan 02, 2023
Greykite: A flexible, intuitive and fast forecasting library

The Greykite library provides flexible, intuitive and fast forecasts through its flagship algorithm, Silverkite.

LinkedIn 1.7k Jan 04, 2023
Hypernets: A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.

A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.

DataCanvas 216 Dec 23, 2022
A naive Bayes model for cancer classification using a set of documents

Naivebayes text classifcation model for cancer and noncancer documents Author: Alex King Purpose Requirements/files included How to use 1. Purpose The

Alex W King 1 Nov 24, 2021
A scikit-learn based module for multi-label et. al. classification

scikit-multilearn scikit-multilearn is a Python module capable of performing multi-label learning tasks. It is built on-top of various scientific Pyth

802 Jan 01, 2023
Book Item Based Collaborative Filtering

Book-Item-Based-Collaborative-Filtering Collaborative filtering methods are used

Şebnem 3 Jan 06, 2022
ThunderGBM: Fast GBDTs and Random Forests on GPUs

Documentations | Installation | Parameters | Python (scikit-learn) interface What's new? ThunderGBM won 2019 Best Paper Award from IEEE Transactions o

Xtra Computing Group 648 Dec 16, 2022
distfit - Probability density fitting

Python package for probability density function fitting of univariate distributions of non-censored data

Erdogan Taskesen 187 Dec 30, 2022
Nixtla is an open-source time series forecasting library.

Nixtla Nixtla is an open-source time series forecasting library. We are helping data scientists and developers to have access to open source state-of-

Nixtla 401 Jan 08, 2023
Kaggler is a Python package for lightweight online machine learning algorithms and utility functions for ETL and data analysis.

Kaggler is a Python package for lightweight online machine learning algorithms and utility functions for ETL and data analysis. It is distributed under the MIT License.

Jeong-Yoon Lee 720 Dec 25, 2022
The code from the Machine Learning Bookcamp book and a free course based on the book

The code from the Machine Learning Bookcamp book and a free course based on the book

Alexey Grigorev 5.5k Jan 09, 2023