d_loss = discriminator_loss(real_images, fake_images) d_optimizer.zero_grad() d_loss.backward() d_optimizer.step()
时间: 2024-01-18 21:03:04 浏览: 26
这是一个生成对抗网络(GAN)中的鉴别器(Discriminator)的训练步骤,用于计算并更新鉴别器的参数。其中,d_loss是通过调用discriminator_loss函数计算出的鉴别器的损失值,该函数根据真实图像和伪图像之间的相似度来计算损失。d_optimizer是一个优化器对象,用于更新鉴别器的参数。在这个步骤中,首先将优化器的梯度清零,然后通过调用backward()函数计算损失的梯度,最后通过调用step()函数来更新鉴别器的参数。这个步骤的目的是让鉴别器能够更好地区分真实图像和伪图像,从而提高GAN的训练效果。
相关问题
def train_step(real_ecg, dim): noise = tf.random.normal(dim) for i in range(disc_steps): with tf.GradientTape() as disc_tape: generated_ecg = generator(noise, training=True) real_output = discriminator(real_ecg, training=True) fake_output = discriminator(generated_ecg, training=True) disc_loss = discriminator_loss(real_output, fake_output) gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables) discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables)) ### for tensorboard ### disc_losses.update_state(disc_loss) fake_disc_accuracy.update_state(tf.zeros_like(fake_output), fake_output) real_disc_accuracy.update_state(tf.ones_like(real_output), real_output) ####################### with tf.GradientTape() as gen_tape: generated_ecg = generator(noise, training=True) fake_output = discriminator(generated_ecg, training=True) gen_loss = generator_loss(fake_output) gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables) generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables)) ### for tensorboard ### gen_losses.update_state(gen_loss) ####################### def train(dataset, epochs, dim): for epoch in tqdm(range(epochs)): for batch in dataset: train_step(batch, dim) disc_losses_list.append(disc_losses.result().numpy()) gen_losses_list.append(gen_losses.result().numpy()) fake_disc_accuracy_list.append(fake_disc_accuracy.result().numpy()) real_disc_accuracy_list.append(real_disc_accuracy.result().numpy()) ### for tensorboard ### # with disc_summary_writer.as_default(): # tf.summary.scalar('loss', disc_losses.result(), step=epoch) # tf.summary.scalar('fake_accuracy', fake_disc_accuracy.result(), step=epoch) # tf.summary.scalar('real_accuracy', real_disc_accuracy.result(), step=epoch) # with gen_summary_writer.as_default(): # tf.summary.scalar('loss', gen_losses.result(), step=epoch) disc_losses.reset_states() gen_losses.reset_states() fake_disc_accuracy.reset_states() real_disc_accuracy.reset_states() ####################### # Save the model every 5 epochs # if (epoch + 1) % 5 == 0: # generate_and_save_ecg(generator, epochs, seed, False) # checkpoint.save(file_prefix = checkpoint_prefix) # Generate after the final epoch display.clear_output(wait=True) generate_and_save_ecg(generator, epochs, seed, False)
下面是将上述代码转换为PyTorch代码的示例:
```python
def train_step(real_ecg, dim):
noise = torch.randn(dim)
for i in range(disc_steps):
generated_ecg = generator(noise, training=True)
real_output = discriminator(real_ecg, training=True)
fake_output = discriminator(generated_ecg, training=True)
disc_loss = discriminator_loss(real_output, fake_output)
discriminator.zero_grad()
disc_loss.backward()
discriminator_optimizer.step()
### for tensorboard ###
disc_losses.update(disc_loss)
fake_disc_accuracy.update(torch.zeros_like(fake_output), fake_output)
real_disc_accuracy.update(torch.ones_like(real_output), real_output)
#######################
for i in range(gen_steps):
generated_ecg = generator(noise, training=True)
fake_output = discriminator(generated_ecg, training=True)
gen_loss = generator_loss(fake_output)
generator.zero_grad()
gen_loss.backward()
generator_optimizer.step()
### for tensorboard ###
gen_losses.update(gen_loss)
#######################
def train(dataset, epochs, dim):
for epoch in tqdm(range(epochs)):
for batch in dataset:
train_step(batch, dim)
disc_losses_list.append(disc_losses.avg)
gen_losses_list.append(gen_losses.avg)
fake_disc_accuracy_list.append(fake_disc_accuracy.avg)
real_disc_accuracy_list.append(real_disc_accuracy.avg)
### for tensorboard ###
# with disc_summary_writer.as_default():
# tf.summary.scalar('loss', disc_losses.result(), step=epoch)
# tf.summary.scalar('fake_accuracy', fake_disc_accuracy.result(), step=epoch)
# tf.summary.scalar('real_accuracy', real_disc_accuracy.result(), step=epoch)
# with gen_summary_writer.as_default():
# tf.summary.scalar('loss', gen_losses.result(), step=epoch)
#######################
disc_losses.reset()
gen_losses.reset()
fake_disc_accuracy.reset()
real_disc_accuracy.reset()
#######################
# Save the model every 5 epochs
# if (epoch + 1) % 5 == 0:
# generate_and_save_ecg(generator, epochs, seed, False)
# checkpoint.save(file_prefix = checkpoint_prefix)
# Generate after the final epoch
# display.clear_output(wait=True)
# generate_and_save_ecg(generator, epochs, seed, False)
```
注意:上述代码仅作为示例,可能需要根据实际情况进行调整和修改。
运行以下Python代码:import torchimport torch.nn as nnimport torch.optim as optimfrom torchvision import datasets, transformsfrom torch.utils.data import DataLoaderfrom torch.autograd import Variableclass Generator(nn.Module): def __init__(self, input_dim, output_dim, num_filters): super(Generator, self).__init__() self.input_dim = input_dim self.output_dim = output_dim self.num_filters = num_filters self.net = nn.Sequential( nn.Linear(input_dim, num_filters), nn.ReLU(), nn.Linear(num_filters, num_filters*2), nn.ReLU(), nn.Linear(num_filters*2, num_filters*4), nn.ReLU(), nn.Linear(num_filters*4, output_dim), nn.Tanh() ) def forward(self, x): x = self.net(x) return xclass Discriminator(nn.Module): def __init__(self, input_dim, num_filters): super(Discriminator, self).__init__() self.input_dim = input_dim self.num_filters = num_filters self.net = nn.Sequential( nn.Linear(input_dim, num_filters*4), nn.LeakyReLU(0.2), nn.Linear(num_filters*4, num_filters*2), nn.LeakyReLU(0.2), nn.Linear(num_filters*2, num_filters), nn.LeakyReLU(0.2), nn.Linear(num_filters, 1), nn.Sigmoid() ) def forward(self, x): x = self.net(x) return xclass ConditionalGAN(object): def __init__(self, input_dim, output_dim, num_filters, learning_rate): self.generator = Generator(input_dim, output_dim, num_filters) self.discriminator = Discriminator(input_dim+1, num_filters) self.optimizer_G = optim.Adam(self.generator.parameters(), lr=learning_rate) self.optimizer_D = optim.Adam(self.discriminator.parameters(), lr=learning_rate) def train(self, data_loader, num_epochs): for epoch in range(num_epochs): for i, (inputs, labels) in enumerate(data_loader): # Train discriminator with real data real_inputs = Variable(inputs) real_labels = Variable(labels) real_labels = real_labels.view(real_labels.size(0), 1) real_inputs = torch.cat((real_inputs, real_labels), 1) real_outputs = self.discriminator(real_inputs) real_loss = nn.BCELoss()(real_outputs, torch.ones(real_outputs.size())) # Train discriminator with fake data noise = Variable(torch.randn(inputs.size(0), self.generator.input_dim)) fake_labels = Variable(torch.LongTensor(inputs.size(0)).random_(0, 10)) fake_labels = fake_labels.view(fake_labels.size(0), 1) fake_inputs = self.generator(torch.cat((noise, fake_labels.float()), 1)) fake_inputs = torch.cat((fake_inputs, fake_labels), 1) fake_outputs = self.discriminator(fake_inputs) fake_loss = nn.BCELoss()(fake_outputs, torch.zeros(fake_outputs.size())) # Backpropagate and update weights for discriminator discriminator_loss = real_loss + fake_loss self.discriminator.zero_grad() discriminator_loss.backward() self.optimizer_D.step() # Train generator noise = Variable(torch.randn(inputs.size(0), self.generator.input_dim)) fake_labels = Variable(torch.LongTensor(inputs.size(0)).random_(0,
这是一个用 PyTorch 实现的条件 GAN,以下是代码的简要解释:
首先引入 PyTorch 相关的库和模块:
```
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
from torch.autograd import Variable
```
接下来定义生成器(Generator)和判别器(Discriminator)的类:
```
class Generator(nn.Module):
def __init__(self, input_dim, output_dim, num_filters):
super(Generator, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.num_filters = num_filters
self.net = nn.Sequential(
nn.Linear(input_dim, num_filters),
nn.ReLU(),
nn.Linear(num_filters, num_filters*2),
nn.ReLU(),
nn.Linear(num_filters*2, num_filters*4),
nn.ReLU(),
nn.Linear(num_filters*4, output_dim),
nn.Tanh()
)
def forward(self, x):
x = self.net(x)
return x
class Discriminator(nn.Module):
def __init__(self, input_dim, num_filters):
super(Discriminator, self).__init__()
self.input_dim = input_dim
self.num_filters = num_filters
self.net = nn.Sequential(
nn.Linear(input_dim, num_filters*4),
nn.LeakyReLU(0.2),
nn.Linear(num_filters*4, num_filters*2),
nn.LeakyReLU(0.2),
nn.Linear(num_filters*2, num_filters),
nn.LeakyReLU(0.2),
nn.Linear(num_filters, 1),
nn.Sigmoid()
)
def forward(self, x):
x = self.net(x)
return x
```
其中,生成器接受输入维度 input_dim、输出维度 output_dim 和 num_filters 个特征,采用线性层和激活函数构建多层神经网络。判别器接受输入维度 input_dim 和 num_filters 个特征,同样采用线性层和激活函数构建多层神经网络。
最后定义条件 GAN 的类 ConditionalGAN,该类包括生成器、判别器和优化器,以及 train 方法进行训练:
```
class ConditionalGAN(object):
def __init__(self, input_dim, output_dim, num_filters, learning_rate):
self.generator = Generator(input_dim, output_dim, num_filters)
self.discriminator = Discriminator(input_dim+1, num_filters)
self.optimizer_G = optim.Adam(self.generator.parameters(), lr=learning_rate)
self.optimizer_D = optim.Adam(self.discriminator.parameters(), lr=learning_rate)
def train(self, data_loader, num_epochs):
for epoch in range(num_epochs):
for i, (inputs, labels) in enumerate(data_loader):
...
```
其中,ConditionalGAN 类接受输入维度 input_dim、输出维度 output_dim、特征数 num_filters 和学习率 learning_rate。train 方法则接受数据加载器 data_loader 和训练轮数 num_epochs,用于训练模型。