@torch.no_grad() def val(dataset): # Validation step data_loader = DataLoader(dataset, batch_size=config['batch_size'], shuffle=False, num_workers=config['data_threads'], pin_memory=True )是什么意思

这段代码定义了一个名为`val`的函数，用于进行验证。

函数接受一个名为`dataset`的参数，表示用于验证的数据集。

在函数内部，使用`@torch.no_grad()`装饰器来标记验证过程，表示在验证过程中不需要计算梯度，以减少内存消耗和加快计算速度。

然后，使用`DataLoader`类创建一个数据加载器`data_loader`，用于加载验证数据集。与训练时的数据加载器相似，这里也设置了批处理大小、洗牌和并行加载等参数。

创建完数据加载器后，可以在验证过程中使用`data_loader`来迭代获取小批量的验证样本。

这段代码的作用是设置验证数据集的批处理大小、洗牌和并行加载等参数，并创建一个数据加载器，以便在验证过程中使用。同时，通过`@torch.no_grad()`装饰器来标记验证过程中不需要计算梯度。

相关问题

帮我把这段代码从tensorflow框架改成pytorch框架： import tensorflow as tf import os import numpy as np import matplotlib.pyplot as plt os.environ["CUDA_VISIBLE_DEVICES"] = "0" base_dir = 'E:/direction/datasetsall/' train_dir = os.path.join(base_dir, 'train_img/') validation_dir = os.path.join(base_dir, 'val_img/') train_cats_dir = os.path.join(train_dir, 'down') train_dogs_dir = os.path.join(train_dir, 'up') validation_cats_dir = os.path.join(validation_dir, 'down') validation_dogs_dir = os.path.join(validation_dir, 'up') batch_size = 64 epochs = 50 IMG_HEIGHT = 128 IMG_WIDTH = 128 num_cats_tr = len(os.listdir(train_cats_dir)) num_dogs_tr = len(os.listdir(train_dogs_dir)) num_cats_val = len(os.listdir(validation_cats_dir)) num_dogs_val = len(os.listdir(validation_dogs_dir)) total_train = num_cats_tr + num_dogs_tr total_val = num_cats_val + num_dogs_val train_image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1. / 255) validation_image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1. / 255) train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size, directory=train_dir, shuffle=True, target_size=(IMG_HEIGHT, IMG_WIDTH), class_mode='categorical') val_data_gen = validation_image_generator.flow_from_directory(batch_size=batch_size, directory=validation_dir, target_size=(IMG_HEIGHT, IMG_WIDTH), class_mode='categorical') sample_training_images, _ = next(train_data_gen) model = tf.keras.models.Sequential([ tf.keras.layers.Conv2D(16, 3, padding='same', activation='relu', input_shape=(IMG_HEIGHT, IMG_WIDTH, 3)), tf.keras.layers.MaxPooling2D(), tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'), tf.keras.layers.MaxPooling2D(), tf.keras.layers.Conv2D(64, 3, padding='same', activation='relu'), tf.keras.layers.MaxPooling2D(), tf.keras.layers.Flatten(), tf.keras.layers.Dense(256, activation='relu'), tf.keras.layers.Dense(2, activation='softmax') ]) model.compile(optimizer='adam', loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), metrics=['accuracy']) model.summary() history = model.fit_generator( train_data_gen, steps_per_epoch=total_train // batch_size, epochs=epochs, validation_data=val_data_gen, validation_steps=total_val // batch_size ) # 可视化训练结果 acc = history.history['accuracy'] val_acc = history.history['val_accuracy'] loss = history.history['loss'] val_loss = history.history['val_loss'] epochs_range = range(epochs) model.save("./model/timo_classification_128_maxPool2D_dense256.h5")

import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader from torchvision import datasets, transforms import os BATCH_SIZE = 64 EPOCHS = 50 IMG_HEIGHT = 128 IMG_WIDTH = 128 train_transforms = transforms.Compose([ transforms.Resize((IMG_HEIGHT,IMG_WIDTH)), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.5,0.5,0.5], [0.5,0.5,0.5])]) test_transforms = transforms.Compose([ transforms.Resize((IMG_HEIGHT,IMG_WIDTH)), transforms.ToTensor(), transforms.Normalize([0.5,0.5,0.5], [0.5,0.5,0.5])]) base_dir = 'E:/direction/datasetsall/' train_dir = os.path.join(base_dir, 'train_img/') validation_dir = os.path.join(base_dir, 'val_img/') train_cats_dir = os.path.join(train_dir, 'down') train_dogs_dir = os.path.join(train_dir, 'up') validation_cats_dir = os.path.join(validation_dir, 'down') validation_dogs_dir = os.path.join(validation_dir, 'up') train_dataset = datasets.ImageFolder(train_dir, transform=train_transforms) train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True) test_dataset = datasets.ImageFolder(validation_dir, transform=test_transforms) test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model = nn.Sequential( nn.Conv2d(3, 16, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2), nn.Conv2d(16, 32, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2), nn.Conv2d(32, 64, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2), nn.Flatten(), nn.Linear(64 * (IMG_HEIGHT // 8) * (IMG_WIDTH // 8), 256), nn.ReLU(), nn.Linear(256, 2), nn.Softmax(dim=1) ) model.to(device) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) for epoch in range(EPOCHS): train_loss = 0.0 train_acc = 0.0 model.train() for images, labels in train_loader: images = images.to(device) labels = labels.to(device) optimizer.zero_grad() outputs = model(images) loss = criterion(outputs, labels) loss.backward() optimizer.step() train_loss += loss.item() * images.size(0) _, preds = torch.max(outputs, 1) train_acc += torch.sum(preds == labels.data) train_loss = train_loss / len(train_loader.dataset) train_acc = train_acc / len(train_loader.dataset) print('Epoch: {} \tTraining Loss: {:.6f} \tTraining Accuracy: {:.6f}'.format(epoch+1, train_loss,train_acc)) with torch.no_grad(): test_loss = 0.0 test_acc = 0.0 model.eval() for images, labels in test_loader: images = images.to(device) labels = labels.to(device) outputs = model(images) loss = criterion(outputs, labels) test_loss += loss.item() * images.size(0) _, preds = torch.max(outputs, 1) test_acc += torch.sum(preds == labels.data) test_loss = test_loss / len(test_loader.dataset) test_acc = test_acc / len(test_loader.dataset) print('Test Loss: {:.6f} \tTest Accuracy: {:.6f}'.format(test_loss,test_acc))

1.使用PyTorch实现LeNet网络 2.使用实验7的 Fashion MNIST 对LeNet进行训练和测试，优化算法采用SGD或Adam，torch.optim.SGD 或 torch.optim.Adam 3.分别绘制训练和测试的损失函数曲线和分类正确率曲线 4.调节BatchSize、学习率，并依据测试损失曲线的拐点确定最佳模型，保存该模型 5.使用测试集测试所保存模型的性能，以混淆矩阵展示

好的，这是一个深度学习的任务，需要使用PyTorch实现LeNet网络并对Fashion MNIST数据集进行训练和测试，我来帮您一步一步完成这个任务。

首先，我们需要导入必要的包：torch、torchvision、numpy和matplotlib。

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import numpy as np
import matplotlib.pyplot as plt

接下来，我们定义LeNet网络的结构。

class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 5)
        self.pool1 = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.pool2 = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(16 * 4 * 4, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool1(torch.relu(self.conv1(x)))
        x = self.pool2(torch.relu(self.conv2(x)))
        x = x.view(-1, 16 * 4 * 4)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

这里我们定义了一个包含2个卷积层和3个全连接层的LeNet网络，其中第一个卷积层有6个5x5的卷积核，第二个卷积层有16个5x5的卷积核。每个卷积层后面都跟了一个2x2的最大池化层，然后是3个全连接层，分别有120、84和10个神经元。

接下来，我们加载Fashion MNIST数据集，并将其划分为训练集和验证集。

transform = torchvision.transforms.Compose([
    torchvision.transforms.ToTensor(),
    torchvision.transforms.Normalize((0.1307,), (0.3081,))
])

train_dataset = torchvision.datasets.FashionMNIST(root='./data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)

val_dataset = torchvision.datasets.FashionMNIST(root='./data', train=False, download=True, transform=transform)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=64, shuffle=False)

这里我们使用了PyTorch内置的Fashion MNIST数据集，并使用了一个Compose对象将ToTensor和Normalize变换组合起来。我们将训练集和验证集分别放入DataLoader中，batch_size设置为64，shuffle设置为True和False，表示训练集需要打乱，而验证集不需要。

接下来，我们定义优化算法和损失函数。

net = LeNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01)

这里我们使用了SGD优化算法和交叉熵损失函数，学习率设置为0.01。

接下来，我们开始训练模型。

train_losses = []
train_accs = []
val_losses = []
val_accs = []

for epoch in range(10):
    train_loss = 0.0
    train_acc = 0.0
    val_loss = 0.0
    val_acc = 0.0

    net.train()
    for i, (inputs, labels) in enumerate(train_loader):
        optimizer.zero_grad()

        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        train_loss += loss.item()
        _, predicted = torch.max(outputs.data, 1)
        train_acc += (predicted == labels).sum().item()

    net.eval()
    with torch.no_grad():
        for inputs, labels in val_loader:
            outputs = net(inputs)
            loss = criterion(outputs, labels)

            val_loss += loss.item()
            _, predicted = torch.max(outputs.data, 1)
            val_acc += (predicted == labels).sum().item()

    train_loss /= len(train_loader)
    train_acc /= len(train_dataset)
    val_loss /= len(val_loader)
    val_acc /= len(val_dataset)

    train_losses.append(train_loss)
    train_accs.append(train_acc)
    val_losses.append(val_loss)
    val_accs.append(val_acc)

    print('Epoch %d: train_loss=%.4f train_acc=%.4f val_loss=%.4f val_acc=%.4f' % (
        epoch+1, train_loss, train_acc, val_loss, val_acc))

这里我们训练了10个epoch，每个epoch分别对训练集进行一次迭代，同时在验证集上计算loss和accuracy。在每个epoch结束时，我们将训练集和验证集的loss和accuracy记录下来。

最后，我们绘制训练和验证的损失函数曲线和分类正确率曲线。

fig, ax = plt.subplots(1, 2, figsize=(12, 6))

ax[0].plot(train_losses, label='train')
ax[0].plot(val_losses, label='val')
ax[0].set_xlabel('epoch')
ax[0].set_ylabel('loss')
ax[0].set_title('Training and validation loss')
ax[0].legend()

ax[1].plot(train_accs, label='train')
ax[1].plot(val_accs, label='val')
ax[1].set_xlabel('epoch')
ax[1].set_ylabel('accuracy')
ax[1].set_title('Training and validation accuracy')
ax[1].legend()

plt.show()

这里我们使用了matplotlib库来绘制图形，包括训练和验证的损失函数曲线和分类正确率曲线。

接下来，我们调节BatchSize和学习率，并依据测试损失曲线的拐点确定最佳模型，并保存该模型。

train_losses = []
train_accs = []
val_losses = []
val_accs = []

best_val_loss = float('inf')
best_model = None

batch_sizes = [16, 32, 64, 128, 256]
learning_rates = [0.001, 0.01, 0.1, 1]

for batch_size in batch_sizes:
    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
    val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=False)

    for learning_rate in learning_rates:
        net = LeNet()
        criterion = nn.CrossEntropyLoss()
        optimizer = optim.SGD(net.parameters(), lr=learning_rate)

        for epoch in range(10):
            train_loss = 0.0
            train_acc = 0.0
            val_loss = 0.0
            val_acc = 0.0

            net.train()
            for i, (inputs, labels) in enumerate(train_loader):
                optimizer.zero_grad()

                outputs = net(inputs)
                loss = criterion(outputs, labels)
                loss.backward()
                optimizer.step()

                train_loss += loss.item()
                _, predicted = torch.max(outputs.data, 1)
                train_acc += (predicted == labels).sum().item()

            net.eval()
            with torch.no_grad():
                for inputs, labels in val_loader:
                    outputs = net(inputs)
                    loss = criterion(outputs, labels)

                    val_loss += loss.item()
                    _, predicted = torch.max(outputs.data, 1)
                    val_acc += (predicted == labels).sum().item()

            train_loss /= len(train_loader)
            train_acc /= len(train_dataset)
            val_loss /= len(val_loader)
            val_acc /= len(val_dataset)

            train_losses.append(train_loss)
            train_accs.append(train_acc)
            val_losses.append(val_loss)
            val_accs.append(val_acc)

            if val_loss < best_val_loss:
                best_val_loss = val_loss
                best_model = net.state_dict()

            print('BatchSize=%d LearningRate=%.3f Epoch %d: train_loss=%.4f train_acc=%.4f val_loss=%.4f val_acc=%.4f' % (
                batch_size, learning_rate, epoch+1, train_loss, train_acc, val_loss, val_acc))

print('Best validation loss:', best_val_loss)

torch.save(best_model, 'best_model.pth')

这里我们使用了两个for循环，分别对BatchSize和学习率进行调节，并在每个epoch结束时记录train_loss、train_acc、val_loss和val_acc。在每次更新最佳模型时，我们将模型的参数保存下来。

最后，我们使用测试集测试所保存模型的性能，并以混淆矩阵展示。

test_dataset = torchvision.datasets.FashionMNIST(root='./data', train=False, download=True, transform=transform)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=False)

net = LeNet()
net.load_state_dict(torch.load('best_model.pth'))

net.eval()
with torch.no_grad():
    all_predicted = []
    all_labels = []

    for inputs, labels in test_loader:
        outputs = net(inputs)
        _, predicted = torch.max(outputs.data, 1)

        all_predicted.extend(predicted.numpy())
        all_labels.extend(labels.numpy())

    all_predicted = np.array(all_predicted)
    all_labels = np.array(all_labels)

    confusion_matrix = np.zeros((10, 10))
    for i in range(len(all_predicted)):
        confusion_matrix[all_labels[i], all_predicted[i]] += 1

    print(confusion_matrix)

这里我们加载保存的最佳模型，并使用测试集进行测试。使用numpy库创建混淆矩阵，将模型预测结果与真实标签进行比对，并将结果输出。