D = Discriminator() mnist = MnistDataSet('data/mnist_train.csv') for label, img, target in mnist: D.train(img, torch.FloatTensor([1.0])) D.train(generate_random(784), torch.FloatTensor([0.0])) D.plot_progress() D.save('model/classifier.pth')
时间: 2024-03-29 08:42:15 浏览: 19
这段代码的作用是使用GAN中的判别器模型对MNIST数据集进行训练,最终保存训练好的模型。具体操作如下:
1. 创建一个名为"D"的判别器模型对象;
2. 从指定路径下的"mnist_train.csv"文件中读取MNIST数据集,并将其存储在名为"mnist"的变量中;
3. 对MNIST数据集进行遍历,每次迭代获取一个样本的标签、图像数据和目标值;
4. 调用判别器模型的"train"方法,对当前样本的图像数据进行训练,同时传入目标值作为标签;
5. 调用判别器模型的"train"方法,对一个随机生成的图像数据进行训练,同时传入目标值作为标签;
6. 调用判别器模型的"plot_progress"方法,将训练过程中的损失曲线绘制出来;
7. 调用判别器模型的"save"方法,将训练好的模型保存到指定路径下的"classifier.pth"文件中。
这段代码的目的是训练判别器模型,使其能够对真实图像和生成图像进行区分,并输出对应的概率值。训练过程中,判别器模型会不断地调整自身的参数,以最小化真实图像和生成图像之间的差异。最终,训练好的模型可以用于判别任意图像的真伪。
相关问题
cgan_model.h5怎么生成
生成 cgan_model.h5 的步骤如下:
1. 准备数据集,包括真实图片和对应的标签。
2. 定义生成器和判别器网络结构。生成器用于生成假图片,判别器用于判断真假图片。
3. 定义损失函数和优化器。损失函数可以是二元交叉熵或 Wasserstein 损失等。优化器可以选择 Adam 优化器或其他优化器。
4. 训练模型。使用真实图片和标签训练判别器,使用生成器生成假图片并与标签一起训练判别器。最后,将生成器和判别器合并为一个 cgan_model 模型。
5. 保存 cgan_model 模型为 cgan_model.h5。
下面是一个简单的 Keras 代码示例:
```python
from keras.models import Model
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, Embedding, multiply
from keras.layers import Conv2DTranspose, Conv2D, Lambda
from keras.optimizers import Adam
from keras.datasets import mnist
import keras.backend as K
# 定义生成器
def build_generator(z_dim, img_shape, num_classes):
z = Input(shape=(z_dim,))
label = Input(shape=(1,), dtype='int32')
emb = Flatten()(Embedding(num_classes, z_dim)(label))
x = multiply([z, emb])
x = Dense(256, activation='relu')(x)
x = Reshape((1, 1, 256))(x)
x = Conv2DTranspose(128, 4, strides=1, padding='valid')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2DTranspose(64, 4, strides=2, padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2DTranspose(1, 4, strides=2, padding='same')(x)
img = Activation('tanh')(x)
return Model([z, label], img)
# 定义判别器
def build_discriminator(img_shape, num_classes):
img = Input(shape=img_shape)
label = Input(shape=(1,), dtype='int32')
emb = Flatten()(Embedding(num_classes, np.prod(img_shape))(label))
emb = Reshape(img_shape)(emb)
x = multiply([img, emb])
x = Conv2D(64, 4, strides=2, padding='same')(x)
x = Activation('relu')(x)
x = Conv2D(128, 4, strides=2, padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Flatten()(x)
x = Dense(1, activation='sigmoid')(x)
return Model([img, label], x)
# 定义损失函数和优化器
def wasserstein_loss(y_true, y_pred):
return K.mean(y_true * y_pred)
def build_cgan(generator, discriminator):
z = Input(shape=(z_dim,))
label = Input(shape=(1,), dtype='int32')
img = generator([z, label])
discriminator.trainable = False
validity = discriminator([img, label])
return Model([z, label], validity)
generator = build_generator(z_dim, img_shape, num_classes)
discriminator = build_discriminator(img_shape, num_classes)
cgan = build_cgan(generator, discriminator)
optimizer = Adam(lr=0.0002, beta_1=0.5, beta_2=0.999)
discriminator.compile(loss=wasserstein_loss, optimizer=optimizer, metrics=['accuracy'])
cgan.compile(loss=wasserstein_loss, optimizer=optimizer)
# 训练模型
for epoch in range(num_epochs):
for i, (imgs, labels) in enumerate(data_loader):
z = np.random.normal(0, 1, (batch_size, z_dim))
gen_imgs = generator.predict([z, labels])
d_loss_real = discriminator.train_on_batch([imgs, labels], -np.ones((batch_size, 1)))
d_loss_fake = discriminator.train_on_batch([gen_imgs, labels], np.ones((batch_size, 1)))
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
z = np.random.normal(0, 1, (batch_size, z_dim))
labels = np.random.randint(0, num_classes, batch_size).reshape(-1, 1)
g_loss = cgan.train_on_batch([z, labels], -np.ones((batch_size, 1)))
# 保存模型
if epoch % save_interval == 0:
generator.save_weights('cgan_model.h5')
```
在训练完成后,可以使用以下代码将生成器的权重保存为 cgan_model.h5 文件:
```python
generator.save_weights('cgan_model.h5')
```
gan生成mnist
生成 MNIST 数据集是深度学习中的一个经典问题,可以使用 GAN(Generative Adversarial Networks)来实现。GAN 是一种无监督学习的方法,它由两个神经网络组成:生成器和判别器。生成器负责生成假的图像,判别器负责判断图像是真实的还是假的。两个网络相互对抗,最终生成器可以生成越来越逼真的图像。
以下是使用 PyTorch 实现 GAN 生成 MNIST 数据集的示例代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
# 定义生成器
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.fc1 = nn.Linear(100, 256)
self.fc2 = nn.Linear(256, 512)
self.fc3 = nn.Linear(512, 784)
self.relu = nn.ReLU()
self.tanh = nn.Tanh()
def forward(self, x):
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
x = self.tanh(self.fc3(x))
return x
# 定义判别器
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.fc1 = nn.Linear(784, 512)
self.fc2 = nn.Linear(512, 256)
self.fc3 = nn.Linear(256, 1)
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
def forward(self, x):
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
x = self.sigmoid(self.fc3(x))
return x
# 定义训练函数
def train(generator, discriminator, dataloader, optimizer_g, optimizer_d, criterion):
for epoch in range(100):
for i, (real_images, _) in enumerate(dataloader):
# 训练判别器
real_labels = torch.ones(real_images.size(0), 1)
fake_labels = torch.zeros(real_images.size(0), 1)
real_images = real_images.view(real_images.size(0), -1)
real_images = real_images.to(device)
real_labels = real_labels.to(device)
fake_labels = fake_labels.to(device)
optimizer_d.zero_grad()
real_outputs = discriminator(real_images)
real_loss = criterion(real_outputs, real_labels)
noise = torch.randn(real_images.size(0), 100)
noise = noise.to(device)
fake_images = generator(noise)
fake_outputs = discriminator(fake_images.detach())
fake_loss = criterion(fake_outputs, fake_labels)
d_loss = real_loss + fake_loss
d_loss.backward()
optimizer_d.step()
# 训练生成器
optimizer_g.zero_grad()
noise = torch.randn(real_images.size(0), 100)
noise = noise.to(device)
fake_images = generator(noise)
fake_outputs = discriminator(fake_images)
g_loss = criterion(fake_outputs, real_labels)
g_loss.backward()
optimizer_g.step()
print('Epoch [{}/{}], d_loss: {:.4f}, g_loss: {:.4f}'.format(epoch+1, 100, d_loss.item(), g_loss.item()))
# 加载数据集
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5], std=[0.5])
])
train_dataset = datasets.MNIST(root='./data', train=True, transform=transform, download=True)
train_dataloader = DataLoader(train_dataset, batch_size=128, shuffle=True)
# 定义超参数
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
lr = 0.0002
betas = (0.5, 0.999)
criterion = nn.BCELoss()
# 初始化生成器和判别器
generator = Generator().to(device)
discriminator = Discriminator().to(device)
# 定义优化器
optimizer_g = optim.Adam(generator.parameters(), lr=lr, betas=betas)
optimizer_d = optim.Adam(discriminator.parameters(), lr=lr, betas=betas)
# 训练模型
train(generator, discriminator, train_dataloader, optimizer_g, optimizer_d, criterion)
```
在训练完成后,可以使用生成器生成假的 MNIST 图像。以下是生成图像的示例代码:
```python
import matplotlib.pyplot as plt
# 生成假的图像
noise = torch.randn(64, 100)
noise = noise.to(device)
fake_images = generator(noise).detach().cpu()
# 显示图像
fig, ax = plt.subplots(nrows=8, ncols=8, figsize=(10, 10))
for i in range(8):
for j in range(8):
ax[i][j].imshow(fake_images[i*8+j].view(28, 28), cmap='gray')
ax[i][j].axis('off')
plt.show()
```