代码块测试

公式

一个a可以产生a开头的句子$a \to ap | r$ 结果就是$a \Rightarrow rpppppppppppp$，不方便解析
单行公式
一个四元式$G=(V_t,V_n,S,P)$ $V_t$:非终结符集合 $V_n$:终结符集合 $S$:开始符号 $P$:产生式的非空有限集

来个复杂公式 $$L(f_{\vec w,b}(\vec x_i),y_i) =\begin{cases}-\log(f_{\vec w,b}(\vec x_i))\ ,\ y_i = 1\\-\log(1-f_{\vec w,b}(\vec x_i))\ ,\ y_i = 0\end{cases}$$

放置代码块

不声明语言

#include "FontEngine.h"

FontEngine::FontEngine() {
	if (FT_Init_FreeType(&library)) {
		SysError(L"Failed to init freetype library");
	}

	if (FT_New_Face(library, "resources\\font\\LLtype.ttf", 0, &face)) {
		SysError(L"Failed to load font");
	}

	debug_log("Loaded font \"%s\"\n", face->family_name);

	if (FT_Set_Pixel_Sizes(face, 0, 128)) {
		SysError(L"Failed to set font size");
	}

	if (FT_Select_Charmap(face, FT_ENCODING_UNICODE)) {
		SysError(L"Failed to select charset");
	}
#ifndef NDEBUG
	load_char(L'A');
	assert(face->glyph->bitmap.pixel_mode == FT_PIXEL_MODE_GRAY);
#endif // !NDEBUG
}

声明为c++

#include "FontEngine.h"

FontEngine::FontEngine() {
	if (FT_Init_FreeType(&library)) {
		SysError(L"Failed to init freetype library");
	}

	if (FT_New_Face(library, "resources\\font\\LLtype.ttf", 0, &face)) {
		SysError(L"Failed to load font");
	}

	debug_log("Loaded font \"%s\"\n", face->family_name);

	if (FT_Set_Pixel_Sizes(face, 0, 128)) {
		SysError(L"Failed to set font size");
	}

	if (FT_Select_Charmap(face, FT_ENCODING_UNICODE)) {
		SysError(L"Failed to select charset");
	}
#ifndef NDEBUG
	load_char(L'A');
	assert(face->glyph->bitmap.pixel_mode == FT_PIXEL_MODE_GRAY);
#endif // !NDEBUG
}

Python

import argparse
import torch
from torch.nn import Module
from common_layers import Conv2, Conv1

class Autoencoder(Module):
    encoder: Module
    decoder: Module

    def __init__(self):
        super(Autoencoder, self).__init__()
        self.encoder = torch.nn.Sequential(
            Conv2(3, 32),
            torch.nn.MaxPool2d(2),
            Conv2(32, 64),
            torch.nn.MaxPool2d(2),
            Conv2(64, 128),
            torch.nn.MaxPool2d(2),
            Conv2(128, 256),
            torch.nn.MaxPool2d(2),
            Conv2(256, 256)
        )

        self.decoder = torch.nn.Sequential(
            torch.nn.ConvTranspose2d(256, 256, 4, stride=2, padding=1),
            Conv2(256, 256),
            torch.nn.ConvTranspose2d(256, 128, 4, stride=2, padding=1),
            Conv2(128, 128),
            torch.nn.ConvTranspose2d(128, 64, 4, stride=2, padding=1),
            Conv2(64, 64),
            torch.nn.ConvTranspose2d(64, 32, 4, stride=2, padding=1),
            Conv2(32, 32),
            torch.nn.Conv2d(32, 3, 1)
        )


    def forward(self, x: torch.Tensor):
        encoded = self.encoder(x)
        decoded = self.decoder(encoded)
        return decoded

再放一张图图