Plotting the training process
Regularization
Batch normalization
Saving and loading the weights and the architecture of a model
Visualize a Deep Learning Neural Network Model in Keras
- POSTECH EECE695J, "딥러닝 기초 및 철강공정에의 활용", 2017-11-10
- Contents: introduction to reccurent neural networks, LSTM, variants of RNN, implementation of RNN, case studies
- Video: https://youtu.be/pgqiEPb4pV8
Deep learning lecture - part 1 (basics, CNN)SungminYou
This presentation is a lecture with the Deep Learning book. (Bengio, Yoshua, Ian Goodfellow, and Aaron Courville. MIT press, 2017) It contains the basics of deep learning and theories about the convolutional neural network.
Basics of RNNs and its applications with following papers:
- Generating Sequences With Recurrent Neural Networks, 2013
- Show and Tell: A Neural Image Caption Generator, 2014
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention, 2015
- DenseCap: Fully Convolutional Localization Networks for Dense Captioning, 2015
- Deep Tracking- Seeing Beyond Seeing Using Recurrent Neural Networks, 2016
- Robust Modeling and Prediction in Dynamic Environments Using Recurrent Flow Networks, 2016
- Social LSTM- Human Trajectory Prediction in Crowded Spaces, 2016
- DESIRE- Distant Future Prediction in Dynamic Scenes with Interacting Agents, 2017
- Predictive State Recurrent Neural Networks, 2017
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
- POSTECH EECE695J, "딥러닝 기초 및 철강공정에의 활용", 2017-11-10
- Contents: introduction to reccurent neural networks, LSTM, variants of RNN, implementation of RNN, case studies
- Video: https://youtu.be/pgqiEPb4pV8
Deep learning lecture - part 1 (basics, CNN)SungminYou
This presentation is a lecture with the Deep Learning book. (Bengio, Yoshua, Ian Goodfellow, and Aaron Courville. MIT press, 2017) It contains the basics of deep learning and theories about the convolutional neural network.
Basics of RNNs and its applications with following papers:
- Generating Sequences With Recurrent Neural Networks, 2013
- Show and Tell: A Neural Image Caption Generator, 2014
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention, 2015
- DenseCap: Fully Convolutional Localization Networks for Dense Captioning, 2015
- Deep Tracking- Seeing Beyond Seeing Using Recurrent Neural Networks, 2016
- Robust Modeling and Prediction in Dynamic Environments Using Recurrent Flow Networks, 2016
- Social LSTM- Human Trajectory Prediction in Crowded Spaces, 2016
- DESIRE- Distant Future Prediction in Dynamic Scenes with Interacting Agents, 2017
- Predictive State Recurrent Neural Networks, 2017
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
A fast-paced introduction to Deep Learning that starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful.
NBDT : Neural-backed Decision Tree 2021 ICLRtaeseon ryu
안녕하세요 딥러닝 논문읽기 모임 입니다.
오늘 소개 드릴 논문은 2021년 ICLR 에 억셉된 NBDT : Neural-backed Decision Tree 라는 논문 입니다
초록 :
Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use. In response, previous work combines decision trees with deep learning, yielding models that (1) sacrifice interpretability for accuracy or (2) sacrifice accuracy for interpretability. We forgo this dilemma by jointly improving accuracy and interpretability using Neural-Backed Decision Trees (NBDTs). NBDTs replace a neural network's final linear layer with a differentiable sequence of decisions and a surrogate loss. This forces the model to learn high-level concepts and lessens reliance on highly-uncertain decisions, yielding (1) accuracy: NBDTs match or outperform modern neural networks on CIFAR, ImageNet and better generalize to unseen classes by up to 16%. Furthermore, our surrogate loss improves the original model's accuracy by up to 2%. NBDTs also afford (2) interpretability: improving human trustby clearly identifying model mistakes and assisting in dataset debugging. Code and pretrained NBDTs are at this https URL.
오늘 논문 리뷰를 이미지 처리팀 안종식님이 자세하고 친절한 리뷰 도와주셨습니다.
감사합니다
문의 : tfkeras@kakao.com
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
A Framework for Scene Recognition Using Convolutional Neural Network as Featu...Tahmid Abtahi
Scene recognition is one of the hallmark tasks of computer vision, allowing definition of a context for object recognition. Availability of large data sets like ImageNet and VGG has provided scopes of applying machine learning classifiers to train models. However high data dimensionality is an issue while training classifiers such as Support Vector Machine (SVM) and perceptron. To reduce data dimensionality and take advantage of parallel and distributed processing, we propose a framework with Convolutional Neural Network (CNN) as Feature extractor and SVM and perceptron as the classifier. MPI (Message passing interface) was used for programming clusters of CPUs. SVM showed 1.05x times improvement over perceptron in terms of run time and CNN reduced data dimensionality by 10x times.
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
The presentation is coverong the convolution neural network (CNN) design.
First,
the main building blocks of CNNs will be introduced. Then we systematically
investigate the impact of a range of recent advances in CNN architectures and
learning methods on the object categorization (ILSVRC) problem. In the
evaluation, the influence of the following choices of the architecture are
tested: non-linearity (ReLU, ELU, maxout, compatibility with batch
normalization), pooling variants (stochastic, max, average, mixed), network
width, classifier design (convolution, fully-connected, SPP), image
pre-processing, and of learning parameters: learning rate, batch size,
cleanliness of the data, etc.
We’rereleasinghighlyoptimizedGPUkernelsforanunderexploredclassofneural network architectures: networks with block-sparse weights. The kernels allow for efficient evaluation and differentiation of linear layers, including convolutional layers, with flexibly configurable block-sparsity patterns in the weight matrix. We findthatdependingonthesparsity,thesekernelscanrunordersofmagnitudefaster than the best available alternatives such as cuBLAS. Using the kernels we improve upon the state-of-the-art in text sentiment analysis and generative modeling of text and images. By releasing our kernels in the open we aim to spur further advancement in model and algorithm design.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Shifu (www.shifu.ml) is a fast and scalable machine learning platform. This presentation briefly describes how to convert the Logistic Regression and Neural Network model in Encog, Mahout, and Spark.
A fast-paced introduction to Deep Learning that starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful.
NBDT : Neural-backed Decision Tree 2021 ICLRtaeseon ryu
안녕하세요 딥러닝 논문읽기 모임 입니다.
오늘 소개 드릴 논문은 2021년 ICLR 에 억셉된 NBDT : Neural-backed Decision Tree 라는 논문 입니다
초록 :
Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use. In response, previous work combines decision trees with deep learning, yielding models that (1) sacrifice interpretability for accuracy or (2) sacrifice accuracy for interpretability. We forgo this dilemma by jointly improving accuracy and interpretability using Neural-Backed Decision Trees (NBDTs). NBDTs replace a neural network's final linear layer with a differentiable sequence of decisions and a surrogate loss. This forces the model to learn high-level concepts and lessens reliance on highly-uncertain decisions, yielding (1) accuracy: NBDTs match or outperform modern neural networks on CIFAR, ImageNet and better generalize to unseen classes by up to 16%. Furthermore, our surrogate loss improves the original model's accuracy by up to 2%. NBDTs also afford (2) interpretability: improving human trustby clearly identifying model mistakes and assisting in dataset debugging. Code and pretrained NBDTs are at this https URL.
오늘 논문 리뷰를 이미지 처리팀 안종식님이 자세하고 친절한 리뷰 도와주셨습니다.
감사합니다
문의 : tfkeras@kakao.com
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
A Framework for Scene Recognition Using Convolutional Neural Network as Featu...Tahmid Abtahi
Scene recognition is one of the hallmark tasks of computer vision, allowing definition of a context for object recognition. Availability of large data sets like ImageNet and VGG has provided scopes of applying machine learning classifiers to train models. However high data dimensionality is an issue while training classifiers such as Support Vector Machine (SVM) and perceptron. To reduce data dimensionality and take advantage of parallel and distributed processing, we propose a framework with Convolutional Neural Network (CNN) as Feature extractor and SVM and perceptron as the classifier. MPI (Message passing interface) was used for programming clusters of CPUs. SVM showed 1.05x times improvement over perceptron in terms of run time and CNN reduced data dimensionality by 10x times.
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
The presentation is coverong the convolution neural network (CNN) design.
First,
the main building blocks of CNNs will be introduced. Then we systematically
investigate the impact of a range of recent advances in CNN architectures and
learning methods on the object categorization (ILSVRC) problem. In the
evaluation, the influence of the following choices of the architecture are
tested: non-linearity (ReLU, ELU, maxout, compatibility with batch
normalization), pooling variants (stochastic, max, average, mixed), network
width, classifier design (convolution, fully-connected, SPP), image
pre-processing, and of learning parameters: learning rate, batch size,
cleanliness of the data, etc.
We’rereleasinghighlyoptimizedGPUkernelsforanunderexploredclassofneural network architectures: networks with block-sparse weights. The kernels allow for efficient evaluation and differentiation of linear layers, including convolutional layers, with flexibly configurable block-sparsity patterns in the weight matrix. We findthatdependingonthesparsity,thesekernelscanrunordersofmagnitudefaster than the best available alternatives such as cuBLAS. Using the kernels we improve upon the state-of-the-art in text sentiment analysis and generative modeling of text and images. By releasing our kernels in the open we aim to spur further advancement in model and algorithm design.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Shifu (www.shifu.ml) is a fast and scalable machine learning platform. This presentation briefly describes how to convert the Logistic Regression and Neural Network model in Encog, Mahout, and Spark.
Towards Safe Automated Refactoring of Imperative Deep Learning Programs to Gr...Raffi Khatchadourian
Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged at the expense of run-time performance. Though hybrid approaches aim for the “best of both worlds,” using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution. We present our ongoing work on automated refactoring that assists developers in specifying whether and how their otherwise eagerly-executed imperative DL code could be reliably and efficiently executed as graphs while preserving semantics. The approach, based on a novel imperative tensor analysis, will automatically determine when it is safe and potentially advantageous to migrate imperative DL code to graph execution and modify decorator parameters or eagerly executing code already running as graphs. The approach is being implemented as a PyDev Eclipse IDE plug-in and uses the WALA Ariadne analysis framework. We discuss our ongoing work towards optimizing imperative DL code to its full potential.
MLConf 2013: Metronome and Parallel Iterative Algorithms on YARNJosh Patterson
Online learning techniques, such as Stochastic Gradient Descent (SGD), are powerful when applied to risk minimization and convex games on large problems. However, their sequential design prevents them from taking advantage of newer distributed frameworks such as Hadoop/MapReduce. In this session, we will take a look at how we parallelize parameter estimation for linear models on the next-gen YARN framework Iterative Reduce and the parallel machine learning library Metronome. We also take a look at non-linear modeling with the introduction of parallel neural network training in Metronome as well.
I have conducted a workshop on Tensorflow2.0 at Facebook Dev CIrcle. This mostly covers the importance of TensorFlow to implement deep neural networks.
You can check the related demo at:
https://github.com/rayyan17/Introduction-To-Tensor-Flow.git
An introduction to Deep Learning concepts, with a simple yet complete neural network, CNNs, followed by rudimentary concepts of Keras and TensorFlow, and some simple code fragments.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and elementary calculus (derivatives), are helpful in order to derive the maximum benefit from this session.
Next we'll see a simple neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. (Bonus points if you know Zorn's Lemma, the Well-Ordering Theorem, and the Axiom of Choice.)
Josh Patterson, Principal at Patterson Consulting: Introduction to Parallel Iterative Machine Learning Algorithms on Hadoop’s NextGeneration YARN Framework
This tutor introduces the basic idea of machine learning with a very simple example. Machine learning teaches machines (and me too) to learn to carry out tasks and concepts by themselves. It is that simple, so here is an overview:
http://www.softwareschule.ch/examples/machinelearning.jpg
Getting your hands dirty with deep learning in javaDave Snowdon
Slides to accompany the introduction to deep learning workshop and exercises on github: https://github.com/davesnowdon/devoxxuk2018-dl-workshop
Workshop held at Devoxx UK 2018, London
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. Then we'll see a short introduction to TensorFlow and TensorBoard.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Immunizing Image Classifiers Against Localized Adversary Attacks
Training course lect3
1. Introduction for Deep Neural
Network DNN with Python
Asst. Prof. Dr.
Noor Dhia Al-Shakarchy
May 2021
Lecture 3
2. Outlines
Plotting the training process
Regularization
Batch normalization
Saving and loading the weights and the
architecture of a model
Visualize a Deep Learning Neural Network
Model in Keras
2
3. Plotting the training process
Matplotlib is a cross-platform, data visualization and
graphical plotting library for Python and its numerical
extension NumPy.
# Code:
history = model.fit(X, y, epochs=10, batch_size=10,
verbose=2)
print(history.history.keys())
print(history.history['acc'])
3
4. Plotting the training process
# Code:
# summarize history for accuracy
import matplotlib.pyplot
matplotlib.pyplot.plot(history.history['acc'])
matplotlib.pyplot.title('model accuracy')
matplotlib.pyplot.ylabel('accuracy')
matplotlib.pyplot.xlabel('epoch')
matplotlib.pyplot.legend(['train'], loc='upper left')
matplotlib.pyplot.show()
4
5. Plotting the training process
# Code:
import matplotlib.pyplot
# summarize history for loss
matplotlib.pyplot.plot(history.history['loss'])
matplotlib.pyplot.title('model loss')
matplotlib.pyplot.ylabel('loss')
matplotlib.pyplot.xlabel('epoch')
matplotlib.pyplot.legend(['train'], loc='upper left')
matplotlib.pyplot.show()
5
6. normalization layers
Batch normalization layers are added to accelerate
the training process and coordinate the update of
multiple layers in the model.
the general process is sketched in figure below:
6
Batch Normalization sketch for simple network
8. Regularization
One of the most important problems accrues during
training the model is overfitting, this issue occurs if
the model fits into the training set too well. This
caused the model becomes difficult to generalize to
unseen examples. That means the model accuracy
will be higher in the training set than the
validation/test set. The model can deal with this
problem by adding regularization layers
8
9. Regularization
The list of regularization parameters commonly used
for dense, and convolutional modules:
kernel_regularizer: Regularizer function applied
to the weight matrix
bias_regularizer: Regularizer function applied to
the bias vector
activity_regularizer: Regularizer function applied
to the output of the layer (its activation)
The regularization layers mostly used are:
Dropout,
L1/L2 regularization
9
10. Regularization
dropout layer
The dropout layer is reducing correlation between
neurons
The model present dropout layer after some layers to
avoid the overfitting and to effectively control
noise during the training process.
10
The dropout process
12. Regularization
L1/L2 regularization
This layer which also called “Elastic Net
Regularization” tend to decrease overfitting of
deep learning neural network model by
regularization the weight.
12
13. dropout layer Code
13
# in the Densew layer:
from keras.layers import regularizers
from keras.regularizers import l2
from keras.constraints import unit_norm
keras.regularizers.l1(0.01)
keras.regularizers.l2(0.01)
keras.regularizers.l1_l2(l1=0.01, l2=0.01)
model.add(Dense(15, activation='relu', name='fc1',
kernel_constraint=unit_norm(),
kernel_regularizer=l2(0.01),
bias_regularizer=l2(0.01)))
14. Saving and loading the weights
and the architecture of a model
Model architectures can be easily saved and loaded
as follows:
# save as JSON json_string = model.to_json()
# save as YAML yaml_string = model.to_yaml()
14
15. Saving and loading the weights and the
architecture of a model
# save model
model_json = model.to_json()
open('proposed_architecture.json', 'w').write(model_json)
# And the weights learned by our DNN on the training set
model.save_weights('proposed_weights.h5', overwrite=True)
15
16. Saving and loading the weights
and the architecture of a model
# load model
model_architecture = ‘proposed_architecture.json'
model_weights = ‘proposed_weights.h5'
model = model_from_json(open(model_architecture).read())
model.load_weights(model_weights)
print("load model done")
16
17. Visualize a Deep Learning
Neural Network Model in Keras
They are:
Summarize Model
Visualize Model
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18. Summarize Model
Keras provides a way to summarize a model.
The summary is textual and includes information
about:
The layers and their order in the model.
The output shape of each layer.
The number of parameters (weights) in each
layer.
The total number of parameters (weights) in the
model.
The summary can be created by calling
the summary() function on the model
Model. summary()
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