# NN Initialization

### Weight Initialization

All-Zero Initialization

It is easy to think that we set all the weights to be zero, but it’s terribly wrong, cause using all zero initialization will make the neurons all the same during the backpropagation update. We don’t need so many identical neurons. Actually, this problem always exists if the weights are initialized to be the same.

Small random values

One guess to solve the problem of all-zero initialization is setting the weights to be small random values, such as $W=0.01*np.random.randn(D,H) ​$ . It is also problematic because very small weights cause very small updates and the update values become smaller and smaller during the backpropagation. In the deep network, this problem is very serious as you may find that the upper layers never update.

# Discriminatively Trained Part Based Models notes

The LSVM (SVM with latent variable) is mostly used for human figure detection, it is very efficiency because it puts the human figure’s structure into consideration: a human figure has hands, head and legs. The LSVM models the human figure structure with 6 parts, and the position of these 6 parts are latent value.

The basic logic is sliding a window on the image, for every position we get a small image patch, by scoring this image patch we can predict whether this image patch contains a human figure or not.

### Defining the score function

Anyway, the first thing to do: defining a score function:

# Structural SVM with Latent Variables Notes

Structural SVM is a variation of SVM, hereafter to be refered as SSVM

### Special prediction function of SSVM

Firstly let’s recall the normal SVM’s prediction function:

ω is the weight vector，x is the input，b is the bias，$sgn$ is sign function，$f(x)$ is the prediction result.

On of SSVM’s specialties is its prediction function：

y is the possible prediction result，Υ is y’s searching space，and Φ is some function of x and y.Φ will be a joint feature vector describes the relationship between x and y

Then for some given $\omega$, different prediction will be made according to different x.

# Lecture notes of LSTM's Inventor Sepp Hochreiter

Sepp Hochreiter was graduated from Technische Universität München, LSTM was invented when he was in TU and now he is the head of Institute of Bioinformatics, Johannes Kepler University Linz.

Today he comes by Munich and gives a lecture in Fakultät Informatik.

At first, Hochreiter praised how hot is Deep Learning (to be referred as DL) around the world these days, especially LSTM, which is now used in the new version of google translate published a few days ago. The improvements DL made in the fields of vision and NLP are very impressive.

Then he starts to tell the magic of DL, taking face recognition as an example, the so-called CNN (Convolution Neuro Networks):

# NN:one neuron

### Simple Neuron

The above diagram shows a neuron in NN, it simulates a real neuron:

it has inputs: $x_{0},x_{1}\dots x_{i}$

it has weights for each inputs: $\omega_{0},\omega_{1}\dots \omega_{i}$: weight vector

it has bias $b$

it has a threshold for the “activation function”

# SVM:multi-class SVM regularization

### Review

For the ith sample $(x_i,y_i)$ in the training set, we have the following loss function:

$w_j^T\cdot x_i$ is the score classifying $x_i$ to class j，and $w_{y_i}^T\cdot x_i$ is the score classifying correctly(classify to class $y_i$)，$\omega_i$ is the $j$th row of $W$.

### Problem

Problem 1:

Considering the geometrical meaning of the weight vector $\omega$, it is easy to find out that $\omega$ is not unique, $\omega$ can change in a small area and result in the same $L_i$.

Problem 2:

It the values in $\omega$ is scaled, the loss computed will also be scaled by the same ratio. Considering a loss of 15, if we scale all the weights in $\omega$ by 2, the loss will be scaled to 30. But this kind of scaling is meaningless, it doesn’t really represent the loss.