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# System prepended metadata

title: Koay Yeong Lin's log

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# Koay Yeong Lin's log

Project title: Stochastic Gradient Descent Algorithm with Multiple Adaptive Learning Rate for Deep Learning

1. Learning rate
2. RPCA
3. Application in image processing



# References

- markdown references
    - https://commonmark.org/help/
    - https://rreece.github.io/sw/markdown-memo/05-math.html
    - $e^{\pi i} + 1 = 0$
- markdown for presentation
    - markdown -> beamer (latex) -> pdf
    - remark.js
- pandoc 
    - convert markdown -> tex, pdf, doc, epub


# Meeting 9/2/2021

1. Example of ill-conditioned on Newton method
2. why need positive definite
3. Example problem of Hessian matrix (high-dimension)
4. How lbfgs solve high dimension problem
5.(mistake) large learning rate -> stuck in a local minimum
6. Example for AdaGrad, AdaDelta, RMSProp, Adam
7. Learning rate = step size 

# Meeting 4/3/2021

1. PCA and SVD

# Meeting 11/3/2021

1. PCA and RPCA

# Meeting 19/3/2021

1.Augmented Lagrange Multiplier (ALM) Method for Robust PCA

# Meeting 2/4/2021

1. Spectral Proximal Method

# Meeting 13/4/2021
1. Convergence analysis (steepest descent convergence properties)
2. Non-smooth optimization(f is non-differentiable)(other application)
3. Average status of current research (adaptive learning rate, RPCA)

# Meeting 20/4/2021

1. Simple neural network program (random dataset)
2. Proposal draft (introduction, objective, problem statement, literature review, methodology)

# Meeting 7/5/2021

1. Proposal draft (expected outcome, gannt chart)
2. Neural network 
    * replace $\alpha$ by $B^{-1}$
    * add more neuron and layer
    * compare multiple adaptive learning rate with fixed learning rate

# Meeting 12/5/2021
1. Average loss (multiple random dataset)
2. Proposal defence slide

# Meeting 19/5/2021
1. Proposal defence slide

# Meeting 8/6/2021

1. * Traditional NN

        1. download MNIST / cifar10
        2. two different classes dataset
        3. 28 x 28 pixles -> flatten
        4. try lecun website to find a baseline/benchmark (how many epoch)
        5. sklearn 'sgd', partial_fit -> how many epoch to achieve convergence
        6. multiple learning rate -> how many epoch?

    * ? comparison with fixed learning rate and stochastic learning rate
? comparison with AdGrad + RMSProp
? Can multiple learning rate also improve convergence in CNN?


# Meeting 16/6/2021

1. Ammendment of proposal
2. Histogram
    * Number of iterations 
    * Computational time

# Meeting 23/6/2021

1. A Modified Spectral Gradient Method for Solving Non Linear System
2. AdaGrad algorithm

# Meeting 30/6/2021

1. CIFAR dataset
2. cProfile (computational time)
3. sklearn (MAdaGrad)

# Meeting 7/7/2021
1. Histogram (scaling)
2. Cross entropy

# Meeting 14/7/2021
1. CIFAR (2 classes and 3 classes)
2. Iris dataset

# Meeting 21/7/2021
1. Extended abstract for iFSC2021 

# Meeting 28/7/2021
1. sklearn (MAdaGrad)

# Meeting 4/8/2021
1. sklearn (MAdaGrad)
2. Directed Reading Assignment

# Meeting 11/8/2021
1. sklearn (MAdaGrad)
2. Conference Slide

# Meeting 18/8/2021
1. MobaXterm 
https://ubuntu.com/tutorials/command-line-for-beginners#3-opening-a-terminal
2. sklearn (plot loss)

# Meeting 1/9/2021
1. Intializing neural network
https://www.deeplearning.ai/ai-notes/initialization/
2. MobaXterm

# Meeting 15/9/2021
1. Xavier initialization (MNIST dataset)

# Meeting 24/9/2021
1. _test_madagrad calculation
2. _fit_stochastic_annotated.pdf
3. sklearn _forward_pass, _forward_pass_fast, _backprop

# Meeting 29/9/2021
1. Neural network hand calculation 
    * Case 1: 2 neurons
    * Case 2: 2 features

# Meeting 7/10/2021
1. Amendment of paper (non linear)
2. safe_sparse_dot function
3. forward pass

# Meeting 14/10/2021
1. sklearn backpropagation

# Meeting 27/10/2021
1. Progress Report
2. Amendment of paper (non linear)

# Meeting 3/11/2021
1. Backpropagation derivation

# Meeting 17/11/2021
1. Backpropagation derivation

# Meeting 24/11/2021
1. sklearn (MAdaGrad)

# Meeting 1/12/2021
1. sklearn (MAdaGrad)

# Meeting 9/12/2021
1. sklearn (MAdaGrad)

# Meeting 16/12/2021
1. Histogram (3 histogram for 3 methods)
- Rescaling (B[i] = s^T y/s^T s * I)
- Restarting (B[i] = I)
- Restoring (B[i] = B[i])
2. script (replace B inverse by H)

# Meeting 6/1/2022
1. slide
2. script (replace B inverse by H)

# Meeting 13/1/2022
1. slide

# Meeting 19/1/2022
1. Amendment of paper (non linear)

# Meeting 27/1/2022
1. https://towardsdatascience.com/custom-optimizer-in-tensorflow-d5b41f75644a

# Meeting 8/2/2022
1. TensorFlow (MAdaGrad)
    * https://www.tensorflow.org/api_docs/python/tf/math
    * https://www.tensorflow.org/api_docs/cc/group/math-ops
    * https://www.tensorflow.org/api_docs/python/tf/Tensor

# Meeting 15/2/2022
1. TensorFlow (MAdaGrad)

# Meeting 22/2/2022
1. TensorFlow (MAdaGrad)

# Meeting 1/3/2022
1. TensorFlow (MAdaGrad)

# Meeting 8/3/2022
1. MAdaGrad - get minimum and maximum (learning rate * B_inverse) 

# Meeting 15/3/2022
1. Profiling Graph (MAdaGrad, Adam, SGD, SGD(max), SGD(min))

# Meeting 29/3/2022
1. sklearn(MAdaGrad_Adam)
2. Profiling Graph (MAdaGrad_Adam, Adam)

# Meeting 5/4/2022
1. MNIST Fashion (MobaXterm)

# Meeting 21/4/2022
1. MNIST Fashion (MobaXterm)

# Meeting 27/4/2022
1. Paper (Introduction and Literature Review)

# Meeting 10/5/2022
1. Latex Paper (Introduction and Literature Review)

# Meeting 17/5/2022
1. Paper (Methodology)
2. Count (if c1 > c2 ; else)

# Meeting 23/5/2022
1. Average loss value (MAdaGrad)

# Meeting 31/5/2022
1. 30 datasets, 500 number of iterations (loss value) (batch size = default)
- MAdaGrad, SGD (invscaling, constant, adaptive)
- MAdaGrad_Adam, Adam
2. 30 datasets, 500 number of iterations (loss value) (batch size=,50,100,200(default),500,1000)
- MAdaGrad, SGD (invscaling, constant, adaptive)
- MAdaGrad_Adam, Adam

# Meeting 14/6/2022
1. Add (1/self.t) into updating formula 
- learning rate * B_inverse * (1/t)
2. Profiling graph (loss value)

# Meeting 23/6/2022
1. SGD (invscaling, adaptive, constant)
2. MNIST Fashion (SGD, MAdaGrad(1/t), MAdaGrad(1/sqrt(t)))
3. Paper 
4. Work completion report
5. Thesis

# Meeting 27/6/2022
1. Profiling graph (SGD invscaling and SGD adaptive)
2. breast cancer dataset, diabetes dataset and wine dataset
3. Paper

# Meeting 4/7/2022
1. Paper
2. Work completion report
3. Datasets (Abalone, Dry Bean, Heart Disease, Raisin, Rice)

# Meeting 12/7/2022
1. Datasets (Abalone, Dry Bean, Heart Disease, Raisin, Rice, wine, breast cancer) - MAdaGrad and SGD

# Meeting 18/7/2022
1. Datasets (Abalone, Dry Bean, Heart Disease, Raisin, Rice, wine, breast cancer) - MAdaGrad and Adam
2. Profiling graph
- xlim (-0.2,8), ylim (0,1.05)
- remove SGD(constant) (overlapped with SGD(adaptive))

# Meeting 25/7/2022
1. Paper
2. Combined profiling graph (Breast Cancer, Wine and Abalone datasets) - total 5 graph (6 hidden layer sizes)

# Meeting 1/8/2022
1. Paper
2. Combined profiling graph (MNIST,Breast Cancer, Wine and Abalone datasets) - total 5 graph (6 hidden layer sizes)

# Meeting 15/8/2022
1. Paper

# Meeting 29/8/2022
1. Paper
2. Thesis

# Meeting 5/9/2022
1. Paper
2. Thesis
