ReST Example

Table (Playing with Kaggle; uses ReST includes)

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Model Epochs bs lr Momentum Result (local) Result (Kaggle) Remarks
SimpleNet 50 20 0.007 0.9 ~97    
ConvNet 50 25 0.008 0.9   99.257  
" 50 17 0.008 0.9 99.1964   augmented
" 50 17 0.008 0.9 99.3143 99.342 augmented bn
Binary Ensemble 25 17 0.007 0.9 >99    
" 22 17 0.0085 0.9 99.23928 99.328 augmented
" 22 17 0.0085 0.9 99.34643 99.357 augmented bn

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  1. augmented: one additional variant per image rotated randomly in [-5,5] degree.
  2. bn: batch-norm

Mathematics

Inline Math: fi(x)=∫τ(π)∞e(x,y)dyf_{i}(x) = \int_{\tau(\pi)}^\infty e(x,y)dy

Display Math:

[a11a12…⋮⋱an1ann]\begin{bmatrix} a_{11} & a_{12} & \dots \\ \vdots & \ddots & \\ a_{n1} & & a_{nn} \end{bmatrix}

Inline Math: (Ω,F,P)≔(Ω+×Ω−,F+⊗F−,P+×P−)(\Omega,\mathcal{F},P) \coloneqq (\Omega^+\times\Omega^-,\mathcal{F^+}\otimes\mathcal{F^-},P^+\times P^-)

Display Math:

The Wiener process in (Ω,F,P)(\Omega,\mathcal{F},P) is defined by

ω(t)≔{(ω+(t),0)t≥0(0,ω−(t))t<0\omega(t) \coloneqq \left\{ \begin{array}{cc} (\omega^+(t),0) & t\geq 0 \\ (0,\omega^-(t)) & t<0 \end{array} \right.

More math ...

dyt=∑i=1nxti∥xt∥2(∑j=1nuijxti dt+∑k=1m∑j=1nvijkxtj∘dWtk)+⋯⋯+12∑i,jn(δij∥xt∥2−2xtixtj∥xt∥4)  ∑km∑l,pnvilkvjpk xtlxtp  dt(3.1a)\tag{3.1a} \begin{aligned} dy_t &= \sum_{i=1}^n \frac{x_t^i}{\|x_t\|^2} \left( \sum_{j=1}^n u_{ij} x_t^i\,dt + \sum_{k=1}^m \sum_{j=1}^n v_{ij}^k x_t^j\circ dW_t^k\right) + \cdots\\ \cdots &+ \frac{1}{2} \sum_{i,j}^n \left( \frac{\delta_{ij}}{\|x_t\|^2} - \frac{2x_t^i x_t^j}{\|x_t\|^4} \right)\; \sum_k^m \sum_{l,p}^n v_{il}^k v_{jp}^k\, x_t^l x_t^p\;dt \end{aligned}

With zt≔xt∥xt∥z_t \coloneqq \frac{x_t}{\|x_t\|} we get the following differential equation on the unit sphere:

yt=y0+∫0tztTUzt−∥zt∥−2ztTV^V^Tzt+12 trace (V^V^T) dt+⋯⋯+∑k=1m∫0tztTVkzt∘dWtk(3.1b)\tag{3.1b} \begin{aligned} y_t &= y_0 + \int_0^t z_t^TUz_t- \|z_t\|^{-2} z_t^T\hat{V}\hat{V}^T z_t + \frac{1}{2}\text{ trace }(\hat{V}\hat{V}^T)\,dt +\cdots\\ \cdots &+ \sum_{k=1}^m \int_0^t z_t^TV^kz_t\circ dW_t^k \end{aligned}

TeX file inclusion

I was only able to include TeX as a ReST file containing the formulas as valid math directive.

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z=z(x,y)x=x(s1,s2)y=y(t1,t2)si=si(w)  ∀i∈{1,2}ti=ti(w)  ∀i∈{1,2}\begin{aligned} z &= z(x, y)\\ x &= x(s_1, s_2)\\ y &= y(t_1, t_2)\\ s_i &= s_i(w) \; \forall i \in \{1,2\} \\ t_i &= t_i(w) \; \forall i \in \{1,2\} \end{aligned}

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Single line:

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Single line (it seems, no real inlining is possible for ReST): start include ->

∂z∂w=∂z∂x⋅(∂x∂s1⋅∂s1∂w+∂x∂s2⋅∂s2∂w)+∂z∂y⋅(∂y∂t1⋅∂t1∂w+∂y∂t2⋅∂t2∂w){\partial z\over\partial w}={\partial z\over\partial x}\cdot\Bigg({\partial x\over\partial s_1}\cdot{\partial s_1\over\partial w}+{\partial x\over\partial s_2}\cdot{\partial s_2\over\partial w}\Bigg)+{\partial z\over\partial y}\cdot\Bigg({\partial y\over\partial t_1}\cdot{\partial t_1\over\partial w}+{\partial y\over\partial t_2}\cdot{\partial t_2\over\partial w}\Bigg)

<- stop include

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Footnotes

Autonumbered footnotes are possible, like using1 and2.

Citations

SVG Image

The conversion of the following figure directive will use an <object> tag around the result. This might be blocked by noScript et al.


Caption (Neuron image - 50%)

Code

Verbatim file inclusion

# include example

from torch.utils.data import TensorDataset
from torch import Tensor, LongTensor, FloatTensor


def loadData(path) -> Tuple[np.ndarray, np.ndarray]:
    '''
    Load data from kaggle mnist set.
    '''
    # Read
    df = pd.read_csv(str(path))  # 40.000 entries
    # tdata = pd.read_csv(data_raw_dir + sep + 'train.csv') # 28.000 entries

    has_labels = True if 'label' in df.columns else False

Normal code block

from torch.utils.data import TensorDataset
from torch import Tensor, LongTensor, FloatTensor


def loadData(path) -> Tuple[np.ndarray, np.ndarray]:
   '''
   Load data from kaggle mnist set.

   path -- input csv

   Return scaled images [0,1] and labels (if available)
   as numpy arrays (dtype: float32, int64)
   '''
   # Read
   df = pd.read_csv(str(path))  # 40.000 entries
   # tdata = pd.read_csv(data_raw_dir + sep + 'train.csv') # 28.000 entries

   has_labels = True if 'label' in df.columns else False

Some inline code.

[1]First footnote
[2]Second footnote
[3]Interesting in terms of content, by the way

References

Giovanni Dematteis, Tobias Grafke, and Eric Vanden-Eijnden. Rogue waves and large deviations in deep sea. Proceedings of the National Academy of Sciences, 115(5):855–860, January 2018. doi:10.1073/pnas.1710670115. ↩

Hugo Touchette. A basic introduction to large deviations: Theory, applications, simulations. arXiv:1106.4146 [cond-mat, physics:math-ph], February 2012. arXiv:1106.4146. ↩