Activity for iBoxDB
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iBoxDB committed [d7a98b] on Code
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#!/usr/bin/env python # coding: utf-8 '''The simplest deep learning, Continuous reinforcement, Run in desktop UI''' '''for LunarLander-v3 Continuous v1.5''' '''python gym_lunar.py''' __credits__ = ["iBoxDB", "Bruce Yang CL-N", "2025-2"] # 3rd parts #https://pytorch.org CPU version only import torch #pip install "gymnasium[box2d]" #pip install pygame import gymnasium #from gymnasium.envs.box2d.lunar_lander import LunarLander, LunarLanderContinuous # included in torch import numpy th = torch nn = th.nn...
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#!/usr/bin/env python # coding: utf-8 '''The simplest deep learning, CarRacing reinforcement, Run in desktop UI''' '''for CarRacing-v3 v1.4''' '''python gym_car14.py''' __credits__ = ["iBoxDB", "Bruce Yang CL-N", "2025-3"] # 3rd parts #https://pytorch.org CPU version only import torch #pip install "gymnasium[box2d]" #pip install pygame import gymnasium #from gymnasium.envs.box2d.car_racing import CarRacing # included in torch import numpy from copy import deepcopy th = torch nn = th.nn np = numpy...
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#!/usr/bin/env python # coding: utf-8 '''The simplest deep learning, CarRacing reinforcement, Run in desktop UI''' '''for CarRacing-v3 v1.4''' '''python gym_car14.py''' __credits__ = ["iBoxDB", "Bruce Yang CL-N", "2025-3"] # 3rd parts #https://pytorch.org CPU version only import torch #pip install "gymnasium[box2d]" #pip install pygame import gymnasium #from gymnasium.envs.box2d.car_racing import CarRacing # included in torch import numpy from copy import deepcopy th = torch nn = th.nn np = numpy...
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#!/usr/bin/env python # coding: utf-8 '''The simplest deep learning, CarRacing reinforcement, Run in desktop UI''' '''for CarRacing-v3 v1.3''' '''python gym_car13.py''' __credits__ = ["iBoxDB", "Bruce Yang CL-N", "2025-3"] # 3rd parts #https://pytorch.org CPU version only import torch #pip install "gymnasium[box2d]" #pip install pygame import gymnasium #from gymnasium.envs.box2d.car_racing import CarRacing # included in torch import numpy from copy import deepcopy th = torch nn = th.nn np = numpy...
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#!/usr/bin/env python # coding: utf-8 '''The simplest deep learning, CarRacing reinforcement, Run in desktop UI''' '''for CarRacing-v3 v1.3''' '''python gym_car13.py''' __credits__ = ["iBoxDB", "Bruce Yang CL-N", "2025-3"] # 3rd parts #https://pytorch.org CPU version only import torch #pip install "gymnasium[box2d]" #pip install pygame import gymnasium #from gymnasium.envs.box2d.car_racing import CarRacing # included in torch import numpy from copy import deepcopy th = torch nn = th.nn np = numpy...
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tried a WinForm GUI project and a Nancy Web project using SharpDevelop472 , built on Win8 and run on Rocky Linux9 by Mono, it works fine. only the Process.Start( "exe" ) has different command-text on Linux.
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SharpDevelop is great and Free , take a look on SharpDevelop472 https://sourceforge.net/p/sharpdevelop472/code/ don't need register!
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pom.xml <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd"> <modelVersion>4.0.0</modelVersion> <groupId>com.mycompany</groupId> <artifactId>Jersey_mavenproject1</artifactId> <packaging>war</packaging> <version>1.0-SNAPSHOT</version> <name>Jersey_mavenproject1</name> <build> <finalName>Jersey_mavenproject1</finalName> <plugins> <plugin> <groupId>org.apache.maven.plugins</groupId>...
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ShortLink Directly denoising Diffusion model, the Simplest deep learning. Version 1.1
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ShortLink 'Directly denoising Diffusion model, the Simplest deep learning. Version 1.1
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#!/usr/bin/env python # coding: utf-8 '''Directly denoising Diffusion model, the Simplest deep learning.''' '''Version 1.1''' '''python diff_c.py''' __credits__ = ["iBoxDB", "Bruce Yang CL-N", "2025-6"] # 3rd parts #https://pytorch.org CPU version only import torch import torch.nn as nn import torch.utils.data as tdata from torch.optim import Adam,AdamW,SGD from torchvision.datasets import MNIST, CIFAR10 from torchvision.utils import save_image, make_grid from PIL import ImageEnhance, Image import...
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ShortLink Directly denoising Diffusion model, the Simplest deep learning.
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#!/usr/bin/env python # coding: utf-8 '''Directly denoising Diffusion model, the Simplest deep learning.''' '''Version 1.0''' '''python diff.py''' __credits__ = ["iBoxDB", "Bruce Yang CL-N", "2025-5"] # 3rd parts #https://pytorch.org CPU version only import torch import torch.nn as nn import torch.utils.data as tdata from torch.optim import Adam,AdamW from torchvision.datasets import MNIST, CIFAR10 from torchvision.utils import save_image, make_grid import torchvision.transforms as transforms import...
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#!/usr/bin/env python # coding: utf-8 '''Mutant GAN generative adversarial network The simplest deep learning''' '''Version 1.0''' '''python mutant_gan.py''' __credits__ = ["iBoxDB", "Bruce Yang CL-N", "2025-4"] # 3rd parts #https://pytorch.org CPU version only import torch # pip install matplotlib import matplotlib.pyplot as plt # pip install pillow from PIL import Image, ImageDraw, ImageFont import torch.utils.data as data import matplotlib.pyplot as plt import matplotlib.animation as animation...
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#!/usr/bin/env python # coding: utf-8 '''The simplest deep learning, Seq2Seq, Sequence to Sequence. Version 1.3''' '''python seq2seq.py''' __credits__ = ["iBoxDB", "Bruce Yang CL-N", "2025-4"] # 3rd parts #https://pytorch.org CPU version only import torch import torch.utils.data as data import os th = torch nn = torch.nn class BrucePredictor(nn.Module): def __init__(self,max_seq,input_categories,out_categories,d_dim=128): super().__init__() self.embed = nn.Embedding(input_categories,d_dim) lin_dim...
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#!/usr/bin/env python # coding: utf-8 '''The simplest deep learning, CarRacing reinforcement, Run in desktop UI''' '''for CarRacing-v3 v1.1''' '''python gym_car.py''' __credits__ = ["iBoxDB", "Bruce Yang CL-N", "2025-3"] # 3rd parts #https://pytorch.org CPU version only import torch #pip install "gymnasium[box2d]" #pip install pygame import gymnasium #from gymnasium.envs.box2d.car_racing import CarRacing # included in torch import numpy from copy import deepcopy th = torch nn = th.nn np = numpy gym...
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