inputs = train_data[:-1].view(1, -1, 1)
时间: 2024-04-26 08:26:55 浏览: 13
这段代码是将 `train_data` 中除最后一个元素外的所有元素作为模型的输入。`.view(1, -1, 1)` 的作用是将输入张量的形状从 `(seq_len,)` 转换为 `(batch_size, seq_len, input_size)`,其中 `seq_len` 是序列长度,`input_size` 是每个时间步输入的特征数,`batch_size` 是每批次输入的序列数。在这里,`batch_size` 被设置为 1,因为我们一次只输入一个序列。`-1` 的意思是将其余的维度自动推断出来,以确保输入张量的总元素数不变。因此,这行代码的作用是将 `train_data` 转换为形状为 `(1, seq_len-1, 1)` 的三维张量,以便与模型的输入要求相匹配。
相关问题
# 对训练集进行预测 train_preds = [] for i in range(len(train_dataset)): inputs, _ = train_dataset[i] inputs = inputs.unsqueeze(0) outputs = model(inputs) _, preds = torch.max(outputs, 1) train_preds.append(preds.item()) # 对验证集进行预测 val_preds = [] for i in range(len(val_dataset)): inputs, _ = val_dataset[i] inputs = inputs.unsqueeze(0) outputs = model(inputs) _, preds = torch.max(outputs, 1) val_preds.append(preds.item()) import numpy as np # 计算训练集准确率 train_labels = np.array([label for _, label in train_dataset]) train_acc = np.mean(np.array(train_preds) == train_labels) print('训练集准确率:{:.2f}%'.format(train_acc * 100)) # 计算验证集准确率 val_labels = np.array([label for _, label in val_dataset]) val_acc = np.mean(np.array(val_preds) == val_labels) print('验证集准确率:{:.2f}%'.format(val_acc * 100)),可以在这里添加一段拟合的过程吗
这段代码是对训练集和验证集使用训练好的模型进行预测,并计算准确率的过程。如果你想添加拟合过程,可以在这段代码之前先进行模型训练,例如:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets
# 加载数据集
train_dataset = datasets.CIFAR10(root='./data', train=True, transform=transforms.ToTensor(), download=True)
val_dataset = datasets.CIFAR10(root='./data', train=False, transform=transforms.ToTensor(), download=True)
# 定义模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(64 * 8 * 8, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.pool(torch.relu(self.conv1(x)))
x = self.pool(torch.relu(self.conv2(x)))
x = x.view(-1, 64 * 8 * 8)
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
# 初始化模型和优化器
model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
# 训练模型
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(train_dataset, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 100 == 99:
print('[Epoch %d, Batch %d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
```
这段代码定义了一个简单的卷积神经网络模型,并使用CIFAR10数据集进行训练。你可以根据需要修改模型结构、训练次数、学习率等参数。在模型训练完成后,你可以使用之前的预测过程对训练集和验证集进行评估,计算准确率并输出。
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。
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