from utils import sigmoid,d_sigmoid,d_mse_loss什么意思
时间: 2024-06-01 16:11:36 浏览: 17
utils是一个自定义的Python模块,其中包含了一些函数和工具类。
sigmoid函数是一种常用的激活函数,用于将输入值映射到0到1之间的概率值。其公式为:
$$sigmoid(x) = \frac{1}{1+e^{-x}}$$
d_sigmoid函数是sigmoid函数的导数,用于反向传播时计算梯度。其公式为:
$$d\_sigmoid(x) = sigmoid(x) * (1-sigmoid(x))$$
d_mse_loss函数是均方误差损失函数的导数,用于反向传播时计算梯度。均方误差损失函数用于衡量预测值与真实值之间的差距。其公式为:
$$d\_mse\_loss(\hat{y},y) = \frac{1}{n} * \sum_{i=1}^{n} 2 * (\hat{y_i} - y_i)$$
其中,$\hat{y}$表示模型的预测值,$y$表示真实值,$n$表示样本数。
相关问题
将loss='categorical_crossentropy'改为均方误差的写法
将loss='categorical_crossentropy'改为均方误差的写法,需要将模型的输出和标签都改为数值型数据,同时将损失函数改为均方误差(MSE)。
下面是示例代码:
```python
from keras.models import Sequential
from keras.layers import Dense
model = Sequential()
model.add(Dense(10, input_dim=8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
```
在这个例子中,我们将模型的输出改为了单个数值,因此我们将损失函数改为均方误差(MSE),并且在编译模型时使用了'mean_squared_error'参数。
同时,我们还需要将标签数据转换为数值型数据,可以使用以下代码:
```python
import numpy as np
from keras.utils import to_categorical
# 生成随机数据作为示例
data = np.random.random((100, 8))
labels = np.random.randint(2, size=(100, 1))
# 将标签数据转换为数值型数据
labels = to_categorical(labels)
# 训练模型
model.fit(data, labels, epochs=10, batch_size=32)
```
在这个例子中,我们使用了`to_categorical`函数将标签数据转换为数值型数据,这样就可以使用均方误差作为损失函数进行训练了。
用eca_resnet50进行图像去噪,包含train.py、val,py、test.py,并在test.py中导出去噪后的图片
好的,首先需要安装相关的库,包括PyTorch、torchvision、Pillow、numpy等。
接着,我们可以定义一个 `ECA_ResNet50` 类,实现图像去噪的功能。具体实现代码如下:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class ECABlock(nn.Module):
def __init__(self, channels, kernel_size, gamma=2, b=1):
super(ECABlock, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv = nn.Conv1d(1, 1, kernel_size, padding=(kernel_size - 1) // 2, bias=False)
self.sigmoid = nn.Sigmoid()
self.fc = nn.Sequential(
nn.Linear(channels, channels // gamma),
nn.ReLU(inplace=True),
nn.Linear(channels // gamma, channels)
)
self.bn = nn.BatchNorm2d(channels, affine=False)
self.b = b
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x)
y = self.conv(y.squeeze(-1).transpose(-1, -2))
y = y.transpose(-1, -2).unsqueeze(-1)
y = self.sigmoid(y)
y = x * y.expand_as(x)
z = self.fc(y.view(b, c)).view(b, c, 1, 1)
out = self.bn(z) * self.b + x
return out
class ECA_ResNet50(nn.Module):
def __init__(self, num_classes=10):
super(ECA_ResNet50, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = nn.Sequential(
nn.Conv2d(64, 256, kernel_size=1, stride=1, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
ECABlock(256, 3),
ECABlock(256, 3),
ECABlock(256, 3)
)
self.layer2 = nn.Sequential(
nn.Conv2d(256, 512, kernel_size=1, stride=2, bias=False),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
ECABlock(512, 3),
ECABlock(512, 3),
ECABlock(512, 3),
ECABlock(512, 3)
)
self.layer3 = nn.Sequential(
nn.Conv2d(512, 1024, kernel_size=1, stride=2, bias=False),
nn.BatchNorm2d(1024),
nn.ReLU(inplace=True),
ECABlock(1024, 3),
ECABlock(1024, 3),
ECABlock(1024, 3),
ECABlock(1024, 3),
ECABlock(1024, 3),
ECABlock(1024, 3)
)
self.layer4 = nn.Sequential(
nn.Conv2d(1024, 2048, kernel_size=1, stride=2, bias=False),
nn.BatchNorm2d(2048),
nn.ReLU(inplace=True),
ECABlock(2048, 3),
ECABlock(2048, 3),
ECABlock(2048, 3)
)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(2048, num_classes)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
```
接下来,我们需要定义训练、验证和测试函数。训练函数中,我们使用 `nn.MSELoss()` 作为损失函数,使用 `torch.optim.Adam()` 作为优化器,设置学习率为 0.001,训练 50 个 epoch,每个 epoch 中,我们先将模型设置为训练模式,然后遍历训练集中的每一个 batch,将输入的图像加上噪声,将加噪后的图像送入网络中,计算输出和目标图像的均方误差,并更新网络参数。每个 epoch 完成后,我们调用验证函数,计算模型在验证集上的准确率。测试函数中,我们遍历测试集中的每一个样本,将其送入网络中,得到去噪后的图像,并保存到指定的文件夹中。
具体实现代码如下:
```python
import os
import numpy as np
import argparse
from PIL import Image
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision.utils import save_image
import torchvision.transforms as transforms
def train(net, trainloader, criterion, optimizer, device):
net.train()
train_loss = 0
for i, (inputs, targets) in enumerate(trainloader):
inputs = inputs.to(device)
targets = targets.to(device)
inputs_noisy = inputs + 0.1 * torch.randn(inputs.size()).to(device)
optimizer.zero_grad()
outputs = net(inputs_noisy)
loss = criterion(outputs, inputs)
loss.backward()
optimizer.step()
train_loss += loss.item()
return train_loss / len(trainloader)
def val(net, valloader, criterion, device):
net.eval()
total = 0
correct = 0
val_loss = 0
with torch.no_grad():
for i, (inputs, targets) in enumerate(valloader):
inputs = inputs.to(device)
targets = targets.to(device)
inputs_noisy = inputs + 0.1 * torch.randn(inputs.size()).to(device)
outputs = net(inputs_noisy)
loss = criterion(outputs, inputs)
val_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
return val_loss / len(valloader), correct / total
def test(net, testloader, device, output_dir):
net.eval()
if not os.path.exists(output_dir):
os.makedirs(output_dir)
for i, (inputs, filename) in enumerate(testloader):
inputs = inputs.to(device)
inputs_noisy = inputs + 0.1 * torch.randn(inputs.size()).to(device)
outputs = net(inputs_noisy)
denoised_img = outputs.detach().cpu()
save_image(denoised_img, os.path.join(output_dir, filename[0]))
def main():
parser = argparse.ArgumentParser(description="Image Denoising with ECA-ResNet50")
parser.add_argument('--train-data', type=str, default='./train', help='path to the train data')
parser.add_argument('--val-data', type=str, default='./val', help='path to the validation data')
parser.add_argument('--test-data', type=str, default='./test', help='path to the test data')
parser.add_argument('--output-dir', type=str, default='./output/', help='output directory')
parser.add_argument('--num-epochs', type=int, default=50, help='number of epochs to train')
parser.add_argument('--batch-size', type=int, default=32, help='batch size')
parser.add_argument('--lr', type=float, default=0.001, help='learning rate')
parser.add_argument('--num-workers', type=int, default=4, help='number of workers for data loading')
parser.add_argument('--cuda', action='store_true', help='use cuda')
args = parser.parse_args()
train_transform = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor()
])
val_transform = transforms.Compose([
transforms.ToTensor()
])
test_transform = transforms.Compose([
transforms.ToTensor()
])
trainset = ImageFolderWithFilename(args.train_data, transform=train_transform)
trainloader = DataLoader(trainset, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers)
valset = ImageFolderWithFilename(args.val_data, transform=val_transform)
valloader = DataLoader(valset, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers)
testset = ImageFolderWithFilename(args.test_data, transform=test_transform)
testloader = DataLoader(testset, batch_size=1, shuffle=False, num_workers=args.num_workers)
device = torch.device('cuda' if args.cuda and torch.cuda.is_available() else 'cpu')
net = ECA_ResNet50().to(device)
criterion = nn.MSELoss()
optimizer = optim.Adam(net.parameters(), lr=args.lr)
for epoch in range(args.num_epochs):
train_loss = train(net, trainloader, criterion, optimizer, device)
val_loss, val_acc = val(net, valloader, criterion, device)
print('Epoch [{}/{}], Train Loss: {:.4f}, Val Loss: {:.4f}, Val Acc: {:.4f}'.format(
epoch+1, args.num_epochs, train_loss, val_loss, val_acc))
test(net, testloader, device, args.output_dir)
class ImageFolderWithFilename(torchvision.datasets.ImageFolder):
def __getitem__(self, index):
original_tuple = super().__getitem__(index)
path = self.imgs[index][0]
tuple_with_path = (original_tuple + (path,))
return tuple_with_path
if __name__ == '__main__':
main()
```
最后,我们可以通过以下命令运行代码:
```
python train.py --train-data <path_to_train_data> --val-data <path_to_validation_data> --test-data <path_to_test_data> --output-dir <path_to_output_directory> --num-epochs 50 --batch-size 32 --lr 0.001 --num-workers 4 --cuda
```
其中,`<path_to_train_data>`、`<path_to_validation_data>` 和 `<path_to_test_data>` 分别为训练集、验证集和测试集的路径,`<path_to_output_directory>` 为输出文件夹的路径。`--num-epochs`、`--batch-size`、`--lr` 和 `--num-workers` 分别表示训练的 epoch 数、batch size、学习率和数据加载的线程数。`--cuda` 表示使用 CUDA 进行训练(如果可用)。
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