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# Neural Network Basics ## Neuron(Perceptron)# $z^l_j = \sum_k{\omega^l_{jk}a^{l-1}_k} + b^l_j$
$a^l_j = \sigma\left(z^l_j\right)$

## Loss function#

### L2 loss function#

$Loss \equiv \frac{1}{2} \lVert \mathbf{y} - \mathbf{a}^L \rVert^2 = \frac{1}{2} \sum_i{\left(y_i - a^L_i\right)^2}$
$Loss \geq 0 \quad \left(\mathbf{y}\text{ is the desired output}\right)$

## Neural Network Training#

What we need to look for through neural network training are weights and biases to minimize the consequences of the loss function. When $\mathbf{w}$ is a vector representing weights and biases,

$Loss_{next} = Loss + \Delta Loss \approx Loss + \nabla Loss \cdot \Delta \mathbf{w}$

It must be $\nabla Loss \cdot \Delta \mathbf{w} < 0$, because $Loss$ should decrease. Therfore, $\Delta \mathbf{w}$ can be determined as

$\Delta \mathbf{w} = - \eta \nabla Loss = - \epsilon \frac{\nabla Loss}{\lVert \nabla Loss \rVert} \quad ( \epsilon > 0)$

$\eta$ is called learning rate and $\epsilon$ is called step. If the step is large, $Loss$ may diverge, and if the step is small, the convergence speed may be slow, so an appropriate value should be determined.

If $\Delta \mathbf{w}$ is determined, then $\mathbf{w}_{next}$ can be

$\mathbf{w}_{next} = \mathbf{w} + \Delta \mathbf{w}$ ### Stochastic gradient descent#

$\nabla Loss = \frac{1}{n}\sum_x{\nabla Loss_x}$

When the number of training inputs is very large, this can take a long time. Stochastic gradient descent works by randomly picking out a small number $m$ of randomly chosen training inputs.

$\nabla Loss = \frac{1}{n}\sum_x{\nabla Loss_x} \approx \frac{1}{m}\sum^m_{i=1}{\nabla Loss_{X_i}}$

Those random training inputs $X_1, X_2, ..., X_m$ are called mini-batch.

### Forward-propagation#

Forward propagation (or forward pass) refers to the calculation and storage of intermediate variables (including outputs) for a neural network in order from the input layer to the output layer.

### Back-propagation# $z^l_j = \sum_k{\omega^l_{jk}a^{l-1}_k} + b^l_j$
$a^l_j = \sigma\left(z^l_j\right)$

Back-propagation is used to find $\nabla Loss$, because it is difficult for a computer to obtain $\nabla Loss$ by differentiating loss function.

Error $\delta^l_j$ of neuron $j$ in layer $l$ is defined as

$\delta^l_j \equiv \frac{\partial Loss}{\partial z^l_j}$

Since $z^l_j$ was obtained from forward propagation, If we know $\mathbf{\delta}^{l+1}$, we can get $\delta^l_j$ as below.

\begin{aligned} \delta^l_j = \frac{\partial Loss}{\partial z^l_j} & = \sum_i{\frac{\partial Loss}{\partial z^{l+1}_i} \frac{\partial z^{l+1}_i}{\partial z^l_j}} \quad \left( \frac{\partial z^{l+1}_i}{\partial z^l_j} = \omega^{l+1}_{ij} \, \sigma' \left(z^l_j\right) \right)\\ & = \sum_i{\frac{\partial Loss}{\partial z^{l+1}_i} \omega^{l+1}_{ij} \, \sigma' \left(z^l_j\right)} \\ & = \sum_i{\delta^{l+1}_i \omega^{l+1}_{ij} \, \sigma' \left(z^l_j\right)} \end{aligned}

If we use L2 loss, since $a^L_j$ was obtained from forward propagation and $\delta^L_j = (a^L_j - y_j) \, \sigma' \left( z^L_j \right)$, we can get the errors like this:

$\delta^L_j = (a^L_j - y_j) \, \sigma' \left( z^L_j \right)$
$\delta^{L-1}_j = \sum_i{ \delta^L_i \omega^L_{ij} \, \sigma' \left(z^{L-1}_j\right)} \\ \vdots$

Finally, $\nabla Loss$ can be obtained by using the errors obtained above.

$\frac{\partial Loss}{\partial b^l_j} = \frac{\partial Loss}{\partial z^l_j} \frac{\partial z^l_j}{\partial b^l_j} = \delta^l_j$
$\frac{\partial Loss}{\partial \omega^l_{jk}} = \frac{\partial Loss}{\partial z^l_j} \frac{\partial z^l_j}{\partial \omega^l_{jk}} = \delta^l_j a^{l-1}_k$

### Training#

Set initail weights and biases to random and repeat process Forward-propagation -> Back-propagation -> weights and biases update. When it is judged that $Loss$ cannot be made smaller, the final weights and biases are determined.

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