基于gan图像分类 代码
时间: 2023-05-09 11:02:23 浏览: 127
GAN图像分类代码,简单来说就是使用了生成对抗网络(GAN)来进行图像分类任务。GAN是一种创新的深度学习框架,它可以实现通过生成数据来提高分类准确度和算法鲁棒性的目的。
GAN图像分类代码的实现过程,通常包括以下几个步骤:
1. 数据预处理:通过对原始图像进行处理,提取出有效的特征信息,去掉一些无用的噪声,从而让网络更加容易学习到分类规律。
2. 建立模型:基于GAN的模型,通过两个组成部分,即生成器和判别器来创造伪造的数据进行图像分类。生成器从随机噪声中生成图像,而判别器则评估这些数据的真实程度,并且将其分类给定的几个类别。
3. 训练模型:通过传递相应的训练样本,让模型进行学习,从而逐步提高分类准确度和分类鲁棒性。
4. 模型评估:通过对测试数据进行预测和分类,计算分类的准确率和精度,从而评估所建立的GAN图像分类模型的优劣。
总之,GAN图像分类代码的实现需要专业的技术人员在深度学习和图像处理技术方面拥有显著的理论和实践经验。这项技术具有广泛的应用前景,例如在人脸识别、数据分类、卫星图像等领域都有着重要的作用。
相关问题
基于GAN的图像压缩代码
以下是基于GAN的图像压缩代码的示例:
```python
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Reshape, Flatten, Conv2D, Conv2DTranspose
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.datasets import mnist
import numpy as np
# 定义图像大小和压缩因子
img_rows = 28
img_cols = 28
channels = 1
img_shape = (img_rows, img_cols, channels)
latent_dim = 100
# 构建生成器
def build_generator():
noise = Input(shape=(latent_dim,))
x = Dense(128 * 7 * 7, activation="relu")(noise)
x = Reshape((7, 7, 128))(x)
x = Conv2DTranspose(128, kernel_size=3, strides=2, padding="same")(x)
x = Conv2DTranspose(64, kernel_size=3, strides=1, padding="same")(x)
x = Conv2DTranspose(channels, kernel_size=3, strides=2, padding="same", activation="tanh")(x)
generator = Model(noise, x)
return generator
# 构建判别器
def build_discriminator():
img = Input(shape=img_shape)
x = Conv2D(32, kernel_size=3, strides=2, padding="same")(img)
x = Conv2D(64, kernel_size=3, strides=2, padding="same")(x)
x = Conv2D(128, kernel_size=3, strides=2, padding="same")(x)
x = Flatten()(x)
x = Dense(1, activation="sigmoid")(x)
discriminator = Model(img, x)
discriminator.compile(loss="binary_crossentropy", optimizer=Adam(), metrics=["accuracy"])
return discriminator
# 构建GAN模型
def build_gan(generator, discriminator):
discriminator.trainable = False
noise = Input(shape=(latent_dim,))
img = generator(noise)
valid = discriminator(img)
gan = Model(noise, valid)
gan.compile(loss="binary_crossentropy", optimizer=Adam())
return gan
# 训练GAN模型
def train(epochs, batch_size=128, save_interval=50):
# 加载数据集
(X_train, _), (_, _) = mnist.load_data()
# 归一化数据
X_train = X_train / 127.5 - 1.
X_train = np.expand_dims(X_train, axis=3)
# 初始化生成器和判别器
generator = build_generator()
discriminator = build_discriminator()
gan = build_gan(generator, discriminator)
# 开始训练
for epoch in range(epochs):
# 训练判别器
idx = np.random.randint(0, X_train.shape[0], batch_size)
imgs = X_train[idx]
noise = np.random.normal(0, 1, (batch_size, latent_dim))
gen_imgs = generator.predict(noise)
d_loss_real = discriminator.train_on_batch(imgs, np.ones((batch_size, 1)))
d_loss_fake = discriminator.train_on_batch(gen_imgs, np.zeros((batch_size, 1)))
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
# 训练生成器
noise = np.random.normal(0, 1, (batch_size, latent_dim))
g_loss = gan.train_on_batch(noise, np.ones((batch_size, 1)))
# 每个epoch保存一次生成的图像
if epoch % save_interval == 0:
print("Epoch %d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100 * d_loss[1], g_loss))
save_images(epoch, generator)
# 生成图像并保存
def save_images(epoch, generator):
r, c = 5, 5
noise = np.random.normal(0, 1, (r * c, latent_dim))
gen_imgs = generator.predict(noise)
gen_imgs = 0.5 * gen_imgs + 0.5
fig, axs = plt.subplots(r, c, figsize=(10,10))
cnt = 0
for i in range(r):
for j in range(c):
axs[i,j].imshow(gen_imgs[cnt, :,:,0], cmap='gray')
axs[i,j].axis('off')
cnt += 1
fig.savefig("images/mnist_%d.png" % epoch)
plt.close()
# 训练模型
train(epochs=20000, batch_size=32, save_interval=100)
```
上述代码使用了MNIST数据集进行训练,并生成了一系列压缩后的图像。你可以根据自己的需求修改代码以适应不同的数据集和压缩比例。
基于GAN的深度图像重建算法代码
以下是一个基于GAN的深度图像重建算法的Python代码示例,使用的是PyTorch框架:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import torchvision.transforms as transforms
import torchvision.datasets as dset
from torchvision.utils import save_image
import os
# 定义超参数
batch_size = 128
lr = 0.0002
train_epoch = 100
beta1 = 0.5
nz = 100
ngf = 64
ndf = 64
# 定义Generator模型
class generator(nn.Module):
def __init__(self):
super(generator, self).__init__()
self.main = nn.Sequential(
nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False),
nn.BatchNorm2d(ngf * 8),
nn.ReLU(True),
nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 4),
nn.ReLU(True),
nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 2),
nn.ReLU(True),
nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf),
nn.ReLU(True),
nn.ConvTranspose2d(ngf, 3, 4, 2, 1, bias=False),
nn.Tanh()
)
def forward(self, input):
output = self.main(input)
return output
# 定义Discriminator模型
class discriminator(nn.Module):
def __init__(self):
super(discriminator, self).__init__()
self.main = nn.Sequential(
nn.Conv2d(3, ndf, 4, 2, 1, bias=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 2),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 4),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 8),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),
nn.Sigmoid()
)
def forward(self, input):
output = self.main(input)
return output.view(-1, 1)
# 初始化模型和优化器
G = generator()
D = discriminator()
G.cuda()
D.cuda()
G.apply(weights_init)
D.apply(weights_init)
criterion = nn.BCELoss()
optimizerD = optim.Adam(D.parameters(), lr=lr, betas=(beta1, 0.999))
optimizerG = optim.Adam(G.parameters(), lr=lr, betas=(beta1, 0.999))
# 定义训练过程
for epoch in range(train_epoch):
for i, data in enumerate(dataloader, 0):
real_cpu, _ = data
batch_size = real_cpu.size(0)
input = Variable(real_cpu.cuda())
target = Variable(torch.ones(batch_size).cuda())
noise = Variable(torch.randn(batch_size, nz, 1, 1).cuda())
fake = G(noise)
target_fake = Variable(torch.zeros(batch_size).cuda())
# 训练Discriminator
D.zero_grad()
output = D(input)
errD_real = criterion(output, target)
errD_real.backward()
output_fake = D(fake.detach())
errD_fake = criterion(output_fake, target_fake)
errD_fake.backward()
errD = errD_real + errD_fake
optimizerD.step()
# 训练Generator
G.zero_grad()
output_fake = D(fake)
errG = criterion(output_fake, target)
errG.backward()
optimizerG.step()
# 保存生成的图像
fake = G(fixed_noise)
save_image(fake.data, 'output/fake_samples_epoch_%03d.png' % epoch, normalize=True)
# 保存模型
torch.save(G.state_dict(), 'generator.pth')
torch.save(D.state_dict(), 'discriminator.pth')
```