网站及微站建设合同,软件工程专业考研科目,企业开通网站的费用怎么做分录,wordpress与dede哪个好用有大量二维矩阵作为样本#xff0c;为连续数据。数据具有空间连续性#xff0c;因此用卷积网络#xff0c;通过dcgan生成二维矩阵。因为是连续变量#xff0c;因此损失采用nn.MSELoss()。
import torch
import torch.nn as nn
import torch.optim as optim
import numpy a…有大量二维矩阵作为样本为连续数据。数据具有空间连续性因此用卷积网络通过dcgan生成二维矩阵。因为是连续变量因此损失采用nn.MSELoss()。
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from DemDataset import create_netCDF_Dem_trainLoader
import torchvision
from torch.utils.tensorboard import SummaryWriterbatch_size16
#load data
dataloader create_netCDF_Dem_trainLoader(batch_size)# Generator with Conv2D structure
class Generator(nn.Module):def __init__(self):super(Generator, self).__init__()self.model nn.Sequential(nn.ConvTranspose2d(100, 512, kernel_size4, stride2, padding1),nn.BatchNorm2d(512),nn.ReLU(),nn.ConvTranspose2d(512, 512, kernel_size4, stride2, padding1),nn.BatchNorm2d(512),nn.ReLU(),nn.ConvTranspose2d(512, 256, kernel_size4, stride2, padding1),nn.BatchNorm2d(256),nn.ReLU(),nn.ConvTranspose2d(256, 128, kernel_size4, stride2, padding1),nn.BatchNorm2d(128),nn.ReLU(),nn.ConvTranspose2d(128, 64, kernel_size4, stride2, padding1),nn.BatchNorm2d(64),nn.ReLU(),nn.ConvTranspose2d(64, 32, kernel_size4, stride2, padding1),nn.BatchNorm2d(32),nn.ReLU(),nn.ConvTranspose2d(32, 1, kernel_size4, stride2, padding1),nn.Tanh())def forward(self, z):img self.model(z)return img# Discriminator with Conv2D structure
class Discriminator(nn.Module):def __init__(self):super(Discriminator, self).__init__()self.model nn.Sequential(nn.Conv2d(1, 32, kernel_size4, stride2, padding1),nn.LeakyReLU(0.2),nn.Conv2d(32, 64, kernel_size4, stride2, padding1),nn.LeakyReLU(0.2),nn.Conv2d(64, 128, kernel_size4, stride2, padding1),nn.LeakyReLU(0.2),nn.Conv2d(128, 256, kernel_size4, stride2, padding1),nn.LeakyReLU(0.2),nn.Conv2d(256, 512, kernel_size4, stride2, padding1),nn.LeakyReLU(0.2),nn.Conv2d(512, 512, kernel_size4, stride2, padding1),nn.LeakyReLU(0.2),nn.Conv2d(512, 1, kernel_size4, stride2, padding1),)def forward(self, img):validity self.model(img)return validity# Initialize GAN components
generator Generator()
discriminator Discriminator()# Define loss function and optimizers
criterion nn.MSELoss()
optimizer_G optim.Adam(generator.parameters(), lr0.0002, betas(0.5, 0.999))
optimizer_D optim.Adam(discriminator.parameters(), lr0.0002, betas(0.5, 0.999))device torch.device(cuda if torch.cuda.is_available() else cpu)
generator.to(device)
discriminator.to(device)writer_real SummaryWriter(flogs/real)
writer_fake SummaryWriter(flogs/fake)
step 0# Training loop
num_epochs 200
for epoch in range(num_epochs):for batch_idx, real_data in enumerate(dataloader):real_data real_data.to(device)# Train Discriminatoroptimizer_D.zero_grad()real_labels torch.ones(real_data.size(0), 1).to(device)fake_labels torch.zeros(real_data.size(0), 1).to(device)z torch.randn(real_data.size(0), 100, 1, 1).to(device)fake_data generator(z)real_pred discriminator(real_data)fake_pred discriminator(fake_data.detach())d_loss_real criterion(real_pred, real_labels)d_loss_fake criterion(fake_pred, fake_labels)d_loss d_loss_real d_loss_faked_loss.backward()optimizer_D.step()# Train Generatoroptimizer_G.zero_grad()z torch.randn(real_data.size(0), 100, 1, 1).to(device)fake_data generator(z)fake_pred discriminator(fake_data)g_loss criterion(fake_pred, real_labels)g_loss.backward()optimizer_G.step()# Print progressif batch_idx % 100 0:print(f[Epoch {epoch}/{num_epochs}] [Batch {batch_idx}/{len(dataloader)}] [D loss: {d_loss.item():.4f}] [G loss: {g_loss.item():.4f}])with torch.no_grad():img_grid_real torchvision.utils.make_grid(fake_data#, normalizeTrue,)img_grid_fake torchvision.utils.make_grid(real_data#, normalizeTrue)writer_fake.add_image(fake_img, img_grid_fake, global_stepstep)writer_real.add_image(real_img, img_grid_real, global_stepstep)step 1# After training, you can generate a 2D array by sampling from the generator
z torch.randn(1, 100, 1, 1).to(device)
generated_array generator(z)
