gan 订阅
氮化镓,化学式GaN,英文名称Gallium nitride,是氮和镓的化合物,是一种直接能隙(direct bandgap)的半导体,自1990年起常用在发光二极管中。此化合物结构类似纤锌矿,硬度很高。氮化镓的能隙很宽,为3.4电子伏特,可以用在高功率、高速的光电元件中,例如氮化镓可以用在紫光的激光二极管,可以在不使用非线性半导体泵浦固体激光器(Diode-pumped solid-state laser)的条件下,产生紫光(405nm)激光。2014年,日本名古屋大学和名城大学教授赤崎勇、名古屋大学教授天野浩和美国加州大学圣塔芭芭拉分校教授中村修二因发明蓝光LED而获的当年的诺贝尔物理奖。 [1-3] 展开全文
氮化镓,化学式GaN,英文名称Gallium nitride,是氮和镓的化合物,是一种直接能隙(direct bandgap)的半导体,自1990年起常用在发光二极管中。此化合物结构类似纤锌矿,硬度很高。氮化镓的能隙很宽,为3.4电子伏特,可以用在高功率、高速的光电元件中,例如氮化镓可以用在紫光的激光二极管,可以在不使用非线性半导体泵浦固体激光器(Diode-pumped solid-state laser)的条件下,产生紫光(405nm)激光。2014年,日本名古屋大学和名城大学教授赤崎勇、名古屋大学教授天野浩和美国加州大学圣塔芭芭拉分校教授中村修二因发明蓝光LED而获的当年的诺贝尔物理奖。 [1-3]
信息
化学式
GaN
英文名
gallium nitride
CAS登录号
25617-97-4
中文名
氮化镓
EINECS登录号
247-129-0
分子量
83.73
氮化镓基本信息
中文名称:氮化镓英文名称:gallium(iii) nitride英文别名:Gallium nitride; nitridogallium; gallium nitrogen(-3) anion分子量:83.7297熔点:1700℃密度:6.1g/mL,25/4℃
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  • GAN

    2018-10-08 17:28:08
  • gan

    2016-03-18 21:42:48
    first time I learned insist second time I konw just believe it

    first time I learned insist
    second time I konw just believe it

    展开全文
  • GAN教程:GAN教程-源码

    2021-02-17 19:14:58
    GAN教程 GAN论文简短回顾练习GAN-GAN_for_MNIST_Tutorial.ipynb
  • GAN代码F GAN图像优化

    2018-03-20 02:58:28
    GAN图像优化 GAN图像优化 GAN图像优化 GAN图像优化 GAN图像优化 GAN图像优化 GAN图像优化 GAN图像优化 GAN图像优化
  • 我们为CA-GAN和SCA-GAN提供PyTorch实施。 论文“通过合成辅助GAN实现逼真的面部照片素描合成” [ 发电机架构 样品结果 左:草图合成; 右:照片合成 (a)输入图像,(b)cGAN,(c)CA-GAN,(d)SCA-GAN 先决...
  • gan简介_GAN简介

    2020-08-12 18:53:36
    INTRODUCTION 介绍 HISTORY OF GANs GAN的历史 INTUITIVE EXPLANATION OF GANs GAN的直观说明 TRAINING GANs 训练甘 GAN TRAINING PROCESS GAN训练过程 GAN BLOCK DIAGRAM 甘块图 KERAS IMPLEMENTATION O...

    gan简介

    目录: (TABLE OF CONTENTS:)

    1. INTRODUCTION

      介绍
    2. HISTORY OF GANs

      GAN的历史
    3. INTUITIVE EXPLANATION OF GANs

      GAN的直观说明
    4. TRAINING GANs

      训练甘
    5. GAN TRAINING PROCESS

      GAN训练过程
    6. GAN BLOCK DIAGRAM

      甘块图
    7. KERAS IMPLEMENTATION OF GAN ON MNIST DATASET

      GAN在MNIST数据集上的KERAS实现。

    介绍 (INTRODUCTION)

    Generative Adversarial Networks also commonly referred to as GANs are used to generate images without very little or no input. GANs allow us to generate images created by our Neural Networks, completely removing a human (yes you) out of the loop. Before we dive into the theory, I like showing you the abilities of GANs to build your excitement. Turn Horses into Zebras (vice versa).

    生成对抗网络通常也称为GAN,用于生成很少输入或没有输入的图像。 GAN使我们能够生成由我们的神经网络创建的图像,从而完全消除了人类(是的)。 在我们深入理论之前,我喜欢向您展示GAN激发您的激情的能力。 将马匹变成斑马(反之亦然)。

    Image for post

    GAN的历史 (HISTORY OF GANs)

    Generative adversarial networks (GANs) was introduced by Ian Goodfellow (the GANFather of GANs) et al. in 2014, in his paper appropriately titled “Generative Adversarial Networks”. It was proposed as an alternative to Variational Auto Encoders (VAEs) which learn the latent spaces of images, to generate synthetic images. It’s aimed to create realistic artificial images that could be almost indistinguishable from real ones.

    生成对抗网络(GANs)由Ian Goodfellow(GANs的GANFather)等人引入。 2014年,他在论文中恰当地题为“ Generative Adversarial Networks”。 它被提议作为变分自动编码器(VAE)的替代方法,后者学习图像的潜在空间,以生成合成图像。 它旨在创建逼真的人造图像,与真实图像几乎无法区分。

    GAN的直观解释 (INTUITIVE EXPLANATION OF GAN)

    Imagine there’s an ambitious young criminal who wants to counterfeit money and sell to a mobster who specializes in handling counterfeit money. At first, the young counterfeiter is not good and our expert mobster tells him, he’s money is way off from looking real. Slowly he gets better and makes a good ‘copy’ every so often. The mobster tells him when it’s good. After some time, both the forger (our counterfeiter) and expert mobster get better at their jobs and now they have created almost real looking but fake money.

    想象一下,有一个雄心勃勃的年轻罪犯想要伪造货币并出售给专门处理伪造货币的黑帮。 起初,年轻的造假者不好,而我们的专家流氓告诉他,他的钱与真实的相去甚远。 慢慢地,他变得更好,并且每隔一段时间就会做出一个好的“副本”。 暴徒告诉他什么时候好。 一段时间之后,伪造者(我们的伪造者)和专家流氓都在工作上变得更好,现在他们创造了几乎是真实的但伪造的钱。

    生成器和鉴别器网络: (The Generator & Discriminator Networks:)

    ● The purpose of the Generator Network is to take a random image initialization and decode it into a synthetic image.● The purpose of the Discriminator Network is to take this input and predict whether this image came from a real dataset or is synthetic.

    ●生成器网络的目的是进行随机图像初始化并将其解码为合成图像。●鉴别器网络的目的是获取此输入并预测此图像是来自真实数据集还是合成图像。

    ●As we just saw, this is effectively what GANs are, two antagonistic networks that are contesting against each other. The two components are called:

    ●正如我们刚刚看到的,这实际上就是GAN,这是两个相互竞争的对立网络。 这两个组件称为:

    1. Generator Network — in our example this was the young criminal creating counterfeit money.

      生成器网络-在我们的示例中,这是年轻的罪犯制造假币。
    2. Discriminator Network — the mobster in our example.

      歧视者网络-本例中的流氓。

    训练甘 (TRAINING GANs)

    ● Training GANs is notoriously difficult. In CNN’s we used gradient descent to change our weights to reduce our loss.

    ●众所周知,训练GAN十分困难。 在CNN中,我们使用梯度下降来更改权重以减少损失。

    ● However, in a GANs, every weight change changes the entire balance of our dynamic system.

    ●但是,在GAN中,每次重量变化都会改变动态系统的整体平衡。

    ● In GAN’s we are not seeking to minimize loss, but finding an equilibrium between our two opposing Networks.

    ●在GAN中,我们并不是要尽量减少损失,而是要在两个相对的网络之间找到平衡。

    GAN训练过程 (THE GAN TRAINING PROCESS)

    1. Input randomly generates noisy images into our Generator Network to generate a sample image.

    1.输入将随机生成的噪声图像生成到我们的生成器网络中,以生成样本图像。

    2. We take some sample images from our real data and mix it with some of our generated images.

    2.我们从真实数据中获取一些样本图像,并将其与一些生成的图像混合。

    3. Input these mixed images to our Discriminator who will then be trained on this mixed set and will update it’s weights accordingly.

    3.将这些混合图像输入到我们的鉴别器中,鉴别器随后将在此混合集合上进行训练,并将相应地更新其权重。

    4. We then make some more fake images and input them into the Discriminator but we label all as real. This is done to train the Generator. We’ve frozen the weights of the discriminator at this stage (Discriminator learning stops), and we use the feedback from the discriminator to now update the weights of the generator. This is how we teach both our Generator (to make better synthetic images) and Discriminator to get better at spotting fakes.

    4.然后,我们制作更多伪造的图像并将其输入到鉴别器中,但我们将所有标签标记为真实。 这样做是为了训练发电机。 在此阶段,我们已经冻结了鉴别器的权重(区分器学习停止),现在我们使用鉴别器的反馈来更新生成器的权重。 这就是我们教导发生器(以生成更好的合成图像)和鉴别器以更好地发现假货的方式。

    GAN框图 (GAN Block Diagram)

    Image for post
    GAN Block Diagram
    GAN框图

    For this article, we will be generating handwritten numbers using the MNIST dataset. The architecture for this GAN is :

    对于本文,我们将使用MNIST数据集生成手写数字。 该GAN的体系结构为:

    Image for post

    GAN在MNIST数据集上的KERAS实现。 (KERAS IMPLEMENTATION OF GAN ON MNIST DATASET)

    The entire code for the project can be found here.

    该项目的完整代码可以在这里找到

    First, we load all the necessary libraries

    首先,我们加载所有必要的库

    import os
    os.environ["KERAS_BACKEND"] = "tensorflow"
    import numpy as np
    from tqdm import tqdm
    import matplotlib.pyplot as plt
    from keras.layers import Input
    from keras.models import Model, Sequential
    from keras.layers.core import Reshape, Dense, Dropout, Flatten
    from keras.layers.advanced_activations import LeakyReLU
    from keras.layers.convolutional import Convolution2D, UpSampling2D
    from keras.layers.normalization import BatchNormalization
    from keras.datasets import mnist
    from keras.optimizers import Adam
    from keras import backend as K
    from keras import initializers
    K.set_image_dim_ordering('th')
    # Deterministic output.
    # Tired of seeing the same results every time? Remove the line below.
    np.random.seed(1000)
    # The results are a little better when the dimensionality of the random vector is only 10.
    # The dimensionality has been left at 100 for consistency with other GAN implementations.
    randomDim = 100

    Now we load our dataset. For this blog MNIST dataset is being used, so no dataset needs to be downloaded separately.

    现在,我们加载数据集。 对于此博客,正在使用MNIST数据集,因此无需单独下载数据集。

    (X_train, y_train), (X_test, y_test) = mnist.load_data()
    X_train = (X_train.astype(np.float32) - 127.5)/127.5
    X_train = X_train.reshape(60000, 784)

    Next, we define the architecture of our generator and discriminator

    接下来,我们定义生成器和鉴别器的架构

    # Optimizer
    adam = Adam(lr=0.0002, beta_1=0.5)#generator
    generator = Sequential()
    generator.add(Dense(256, input_dim=randomDim, kernel_initializer=initializers.RandomNormal(stddev=0.02)))
    generator.add(LeakyReLU(0.2))
    generator.add(Dense(512))
    generator.add(LeakyReLU(0.2))
    generator.add(Dense(1024))
    generator.add(LeakyReLU(0.2))
    generator.add(Dense(784, activation='tanh'))
    generator.compile(loss='binary_crossentropy', optimizer=adam)#discriminator
    discriminator = Sequential()
    discriminator.add(Dense(1024, input_dim=784, kernel_initializer=initializers.RandomNormal(stddev=0.02)))
    discriminator.add(LeakyReLU(0.2))
    discriminator.add(Dropout(0.3))
    discriminator.add(Dense(512))
    discriminator.add(LeakyReLU(0.2))
    discriminator.add(Dropout(0.3))
    discriminator.add(Dense(256))
    discriminator.add(LeakyReLU(0.2))
    discriminator.add(Dropout(0.3))
    discriminator.add(Dense(1, activation='sigmoid'))
    discriminator.compile(loss='binary_crossentropy', optimizer=adam)

    Now we combine our generator and discriminator to train simultaneously.

    现在,我们将生成器和鉴别器结合起来同时进行训练。

    # Combined network
    discriminator.trainable = False
    ganInput = Input(shape=(randomDim,))
    x = generator(ganInput)
    ganOutput = discriminator(x)
    gan = Model(inputs=ganInput, outputs=ganOutput)
    gan.compile(loss='binary_crossentropy', optimizer=adam)
    dLosses = []
    gLosses = []

    Three functions to plot and save the results after every 20 epochs and save the model.

    每隔20个周期绘制并保存结果并保存模型的三个功能。

    # Plot the loss from each batch
    def plotLoss(epoch):
    plt.figure(figsize=(10, 8))
    plt.plot(dLosses, label='Discriminitive loss')
    plt.plot(gLosses, label='Generative loss')
    plt.xlabel('Epoch')
    plt.ylabel('Loss')
    plt.legend()
    plt.savefig('images/gan_loss_epoch_%d.png' % epoch)
    # Create a wall of generated MNIST images
    def plotGeneratedImages(epoch, examples=100, dim=(10, 10), figsize=(10, 10)):
    noise = np.random.normal(0, 1, size=[examples, randomDim])
    generatedImages = generator.predict(noise)
    generatedImages = generatedImages.reshape(examples, 28, 28)
    plt.figure(figsize=figsize)
    for i in range(generatedImages.shape[0]):
    plt.subplot(dim[0], dim[1], i+1)
    plt.imshow(generatedImages[i], interpolation='nearest', cmap='gray_r')
    plt.axis('off')
    plt.tight_layout()
    plt.savefig('images/gan_generated_image_epoch_%d.png' % epoch)
    # Save the generator and discriminator networks (and weights) for later use
    def saveModels(epoch):
    generator.save('models/gan_generator_epoch_%d.h5' % epoch)
    discriminator.save('models/gan_discriminator_epoch_%d.h5' % epoch)

    The train function

    火车功能

    def train(epochs=1, batchSize=128):
    batchCount = X_train.shape[0] / batchSize
    print 'Epochs:', epochs
    print 'Batch size:', batchSize
    print 'Batches per epoch:', batchCount
    for e in xrange(1, epochs+1):
    print '-'*15, 'Epoch %d' % e, '-'*15
    for _ in tqdm(xrange(batchCount)):
    # Get a random set of input noise and images
    noise = np.random.normal(0, 1, size=[batchSize, randomDim])
    imageBatch = X_train[np.random.randint(0, X_train.shape[0], size=batchSize)]
    # Generate fake MNIST images
    generatedImages = generator.predict(noise)
    # print np.shape(imageBatch), np.shape(generatedImages)
    X = np.concatenate([imageBatch, generatedImages])
    # Labels for generated and real data
    yDis = np.zeros(2*batchSize)
    # One-sided label smoothing
    yDis[:batchSize] = 0.9
    # Train discriminator
    discriminator.trainable = True
    dloss = discriminator.train_on_batch(X, yDis)
    # Train generator
    noise = np.random.normal(0, 1, size=[batchSize, randomDim])
    yGen = np.ones(batchSize)
    discriminator.trainable = False
    gloss = gan.train_on_batch(noise, yGen)
    # Store loss of most recent batch from this epoch
    dLosses.append(dloss)
    gLosses.append(gloss)
    if e == 1 or e % 20 == 0:
    plotGeneratedImages(e)
    saveModels(e)
    # Plot losses from every epoch
    plotLoss(e)
    train(200, 128)

    To stay connected follow me here.

    要保持联系,请在这里关注我。

    READ MY PREVIOUS BLOG: UNDERSTANDING U-Net from here.

    阅读我以前的博客: 从这里了解U-Net。

    翻译自: https://medium.com/analytics-vidhya/introduction-to-gans-38a7a990a538

    gan简介

    展开全文
  • GAN基础

    2020-08-14 12:41:17
    GAN基础GAN基础GAN基础

    GANGAN基础

    一 自编码器的实现与应用

    1.自编码器的定义与原理

    2.基本自编码器

    3.基本去噪自编码器

    4.上采样和反卷积

    5.卷积去噪自编码器

    6.基本自编码器的代码实现

    7.去噪自编码器

    8.卷积去噪自编码器

    二 GAN的原理与实现

    1.GAN的原理介绍

    2.数据准备

    3.编写生成器模型和辨别器模型

    4.编写loss函数,定义优化器

    5.定义批次训练函数

    6.GAN的训练与结果可视化

    7.简单GAN的代码实现(一)

    8.简单GAN的代码实现(二)

    展开全文
  • Gan图像生成器-BigGan

    2018-10-13 12:06:32
    丰富的背景和纹理图像的生成是各类生成模型追求的终极目标,ImageNet 的生成已然 成为检验生成... BigGAN 在 SAGAN 的基础上一举将 IS 提高了 100 分,达到了 166 分(真实图片也 才 233 分),可以说 BigGAN 是太秀了
  • Matlab-GAN:生成对抗网络的MATLAB实现-从GAN到Pixel2Pixel,CycleGAN
  • 简单理解与实验生成对抗网络GAN

    万次阅读 多人点赞 2017-05-26 21:31:49
    之前GAN网络是近两年深度学习领域的新秀,火的不行,本文旨在浅显理解传统GAN,分享学习心得。现有GAN网络大多数代码实现使用python、torch等语言,这里,后面用matlab搭建一个简单的GAN网络,便于理解GAN原理。GAN...
  • GaN∶Eu

    2021-02-22 14:10:03
    GaN∶Eu 3+作为红光发射材料,在GaN基单片集成全色显示器件应用方面具有很大的潜力。目前的研究重点是如何进一步调控和优化GaN∶Eu 3+材料的发光特性,促使其迈向实用阶段。本文主要从生长调控,Mg 2+、Zn 2+、Si 4+...
  • GAN简介 GAN(Generative Adversarial Networks)是在无监督机器学习中使用的模型,由两个神经网络在零和游戏框架中相互竞争的系统实现。 它是由Ian Goodfellow等人介绍的。 在2014年。 该存储库的目的是提供自2014...
  • StarGAN - Official PyTorch Implementation ***** New: StarGAN v2 is available at https://github.com/clovaai/stargan-v2 ***** This repository provides the official PyTorch implementation of the ...
  • Pix2Pix-基于GAN的图像翻译

    万次阅读 多人点赞 2017-12-16 16:49:21
    语言翻译是大家都知道的应用。但图像作为一种交流媒介,也有很多种表达方式,比如灰度图、彩色图、梯度...在GAN出现之后,这些任务一下子都可以用同一种框架来解决。这个算法的名称叫做Pix2Pix,基于对抗神经网络实现。
  • GaN/Al

    2021-02-11 06:08:43
    在Al2O3(0001)衬底上用MOCVD(金属有机物气相沉积)方法进行了GaN的外延生长,通过X射线衍射(同步辐射源)研究了GaN和Al2O3(0001)的匹配关系。结果表明,经充分氮化的衬底上,GaN以单一的匹配方式沿[0001]方向生长;在Al...
  • 原生GAN

    2020-03-03 03:16:12
    原生GAN
  • GAN GAN 有两个网络,一个是 generator,一个是 discriminator,通过两个网络互相对抗来达到最好的生成效果。 公式: 先固定 G,来求解最优的 D 对于一个给定的 x,得到最优的 D 如上图,范围在 (0,1) 内,...

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