# Start epoch for adversarial training _C.TRAIN.START_EPOCH_ADV = 50
时间: 2024-04-07 21:31:19 浏览: 13
这个问题是关于对抗训练的起始 epoch 设置,参数名为 `_C.TRAIN.START_EPOCH_ADV`,其值为 50。这个参数是指模型在训练时从第 50 个 epoch 开始进行对抗训练。对抗训练是一种提高模型鲁棒性的方法,通过向输入数据中添加扰动来使模型对不同的输入具有相似的输出。通常情况下,对抗训练需要更长的训练时间和更大的计算资源,因此需要根据具体情况来进行调整。
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。
修改一下这段代码在pycharm中的实现,import pandas as pd import numpy as np from sklearn.model_selection import train_test_split import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim #from torchvision import datasets,transforms import torch.utils.data as data #from torch .nn:utils import weight_norm import matplotlib.pyplot as plt from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import f1_score from sklearn.metrics import cohen_kappa_score data_ = pd.read_csv(open(r"C:\Users\zhangjinyue\Desktop\rice.csv"),header=None) data_ = np.array(data_).astype('float64') train_data =data_[:,:520] train_Data =np.array(train_data).astype('float64') train_labels=data_[:,520] train_labels=np.array(train_data).astype('float64') train_data,train_data,train_labels,train_labels=train_test_split(train_data,train_labels,test_size=0.33333) train_data=torch.Tensor(train_data) train_data=torch.LongTensor(train_labels) train_data=train_data.reshape(-1,1,20,26) train_data=torch.Tensor(train_data) train_data=torch.LongTensor(train_labels) train_data=train_data.reshape(-1,1,20,26) start_epoch=1 num_epoch=1 BATCH_SIZE=70 Ir=0.001 classes=('0','1','2','3','4','5') device=torch.device("cuda"if torch.cuda.is_available()else"cpu") torch.backends.cudnn.benchmark=True best_acc=0.0 train_dataset=data.TensorDataset(train_data,train_labels) test_dataset=data.TensorDataset(train_data,train_labels) train_loader=torch.utills.data.DataLoader(dtaset=train_dataset,batch_size=BATCH_SIZE,shuffle=True) test_loader=torch.utills.data.DataLoader(dtaset=train_dataset,batch_size=BATCH_SIZE,shuffle=True)
修改后的代码如下所示:
```python
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as data
import matplotlib.pyplot as plt
from sklearn.metrics import precision_score, recall_score, f1_score, cohen_kappa_score
data_ = pd.read_csv(r"C:\Users\zhangjinyue\Desktop\rice.csv", header=None)
data_ = np.array(data_).astype('float64')
train_data = data_[:, :520]
train_labels = data_[:, 520]
train_data, _, train_labels, _ = train_test_split(train_data, train_labels, test_size=0.33333)
train_data = torch.Tensor(train_data)
train_labels = torch.LongTensor(train_labels)
train_data = train_data.reshape(-1, 1, 20, 26)
start_epoch = 1
num_epoch = 1
BATCH_SIZE = 70
Ir = 0.001
classes = ('0', '1', '2', '3', '4', '5')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.backends.cudnn.benchmark = True
best_acc = 0.0
train_dataset = data.TensorDataset(train_data, train_labels)
test_dataset = data.TensorDataset(train_data, train_labels)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True)
```
请注意,我做了以下修改:
1. 移除了不需要的导入语句。
2. 修复了变量名拼写错误。
3. 移除了重复的代码行。
4. 修正了 `torch.utils.data.DataLoader` 的拼写错误。
5. 修正了数据集分割时的变量名错误。
请根据你的实际需求进一步调整代码。