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import torch import torchvision.datasets as datasets import torchvision.transforms as transforms from PIL import Image from torch.utils.data import DataLoader, random_split import torch.nn as nn import torch.nn.functional as F import matplotlib.pyplot as plt # 设置数据变换 transform = transforms.Compose([ transforms.Resize((28, 28)), # 先调整大小,再转换为灰度图 transforms.Grayscale(), # 调整大小后再转换为灰度图 transforms.ToTensor(), ]) # 下载并加载数据集 train_data = datasets.FashionMNIST(root='./data', train=True, download=True, transform=transform) test_data = datasets.FashionMNIST(root='./data', train=False, download=True, transform=transform) # 划分验证集 validation_size = int(0.1 * len(train_data)) train_data, validation_data = random_split(train_data, [len(train_data) - validation_size, validation_size]) # 创建Dataloader train_loader = DataLoader(train_data, batch_size=64, shuffle=True) validation_loader = DataLoader(validation_data, batch_size=64, shuffle=False) # 定义模型 class LeNet(nn.Module): def __init__(self): # 修正了init方法的名称 super(LeNet, self).__init__() # 修正了super的调用 self.conv1 = nn.Conv2d(1, 6, 5, padding=2) self.pool = nn.MaxPool2d(2, 2) self.conv2 = nn.Conv2d(6, 16, 5) self.fc1 = nn.Linear(16 * 5 * 5, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 10) def forward(self, x): x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(x))) x = x.view(-1, 16 * 5 * 5) # 修正了view方法的参数 x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x # 实例化模型 model = LeNet() # 定义损失函数和优化器 criterion = nn.CrossEntropyLoss() optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9) # 定义训练的轮数 num_epochs = 5 # 训练模型 for epoch in range(num_epochs): model.train() running_loss = 0.0 for data in train_loader: inputs, labels = data optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() print(f'Epoch {epoch+1}, loss: {running_loss/len(train_loader)}') # 在验证集上评估模型 model.eval() validation_loss = 0.0 with torch.no_grad(): for data in validation_loader: inputs, labels = data outputs = model(inputs) validation_loss += criterion(outputs, labels).item() print(f'Validation Loss: {validation_loss / len(validation_loader)}') # 设置模型为评估模式 model.eval() # 初始化测试损失和正确预测计数 test_loss = 0.0 correct = 0 # 创建测试数据的Dataloader test_loader = DataLoader(test_data, batch_size=64, shuffle=False) # 遍历测试数据集 with torch.no_grad(): for data in test_loader: inputs, labels = data outputs = model(inputs) test_loss += criterion(outputs, labels).item() _, predicted = torch.max(outputs.data, 1) correct += (predicted == labels).sum().item() # 计算测试损失和准确率 test_loss /= len(test_loader) accuracy = 100 * correct / len(test_data) # 输出测试结果 print(f'Test Loss: {test_loss:.4f}') print(f'Accuracy: {accuracy:.2f}%') # 加载待识别的图像 def load_image(image_path): image = Image.open(image_path).convert('L').resize((28, 28)) # 直接使用PIL进行转换和调整大小 image = transforms.ToTensor()(image) image = image.unsqueeze(0) plt.imshow(image.squeeze().numpy(), cmap='gray') plt.show() return image # 使用模型进行预测 def predict(model, image): model.eval() with torch.no_grad(): output = model(image) _, predicted = torch.max(output, 1) print(f"Predicted class: {predicted.item()}") return predicted # 加载图像 image = load_image("path_to_your_image.png") # 使用模型进行预测 predicted_class = predict(model, image)帮我修改错误

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理解问题import torch import torchvision.datasets as datasets import torchvision.transforms as transforms from PIL import Image from torch.utils.data import DataLoader, random_split import torch.nn as nn import torch.nn.functional as F import matplotlib.pyplot as plt # 设置数据变换 transform = transforms.Compose([ transforms.Resize((28, 28)), # 先调整大小,再转换为灰度图 transforms.Grayscale(), # 调整大小后再转换为灰度图 transforms.ToTensor(), ]) # 下载并加载数据集 train_data = datasets.FashionMNIST(root='./data', train=True, download=True, transform=transform) test_data = datasets.FashionMNIST(root='./data', train=False, download=True, transform=transform) # 划分验证集 validation_size = int(0.1 * len(train_data)) train_data, validation_data = random_split(train_data, [len(train_data) - validation_size, validation_size]) # 创建Dataloader train_loader = DataLoader(train_data, batch_size=64, shuffle=True) validation_loader = DataLoader(validation_data, batch_size=64, shuffle=False) # 定义模型 class LeNet(nn.Module): def __init__(self): # 修正了init方法的名称 super(LeNet, self).__init__() # 修正了super的调用 self.conv1 = nn.Conv2d(1, 6, 5, padding=2) self.pool = nn.MaxPool2d(2, 2) self.conv2 = nn.Conv2d(6, 16, 5) self.fc1 = nn.Linear(16 * 5 * 5, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 10) def forward(self, x): x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(x))) x = x.view(-1, 16 * 5 * 5) # 修正了view方法的参数 x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x # 实例化模型 model = LeNet() # 定义损失函数和优化器 criterion = nn.CrossEntropyLoss() optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9) # 定义训练的轮数 num_epochs = 5 # 训练模型 for epoch in range(num_epochs): model.train() running_loss = 0.0 for data in train_loader: inputs, labels = data optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() print(f'Epoch {epoch+1}, loss: {running_loss/len(train_loader)}') # 在验证集上评估模型 model.eval() validation_loss = 0.0 with torch.no_grad(): for data in validation_loader: inputs, labels = data outputs = model(inputs) validation_loss += criterion(outputs, labels).item() print(f'Validation Loss: {validation_loss / len(validation_loader)}') # 设置模型为评估模式 model.eval() # 初始化测试损失和正确预测计数 test_loss = 0.0 correct = 0 # 创建测试数据的Dataloader test_loader = DataLoader(test_data, batch_size=64, shuffle=False) # 遍历测试数据集 with torch.no_grad(): for data in test_loader: inputs, labels = data outputs = model(inputs) test_loss += criterion(outputs, labels).item() _, predicted = torch.max(outputs.data, 1) correct += (predicted == labels).sum().item() # 计算测试损失和准确率 test_loss /= len(test_loader) accuracy = 100 * correct / len(test_data) # 输出测试结果 print(f'Test Loss: {test_loss:.4f}') print(f'Accuracy: {accuracy:.2f}%') # 加载待识别的图像 def load_image(image_path): image = Image.open(image_path).convert('L').resize((28, 28)) # 直接使用PIL进行转换和调整大小 image = transforms.ToTensor()(image) image = image.unsqueeze(0) plt.imshow(image.squeeze().numpy(), cmap='gray') plt.show() return image # 使用模型进行预测 def predict(model, image): model.eval() with torch.no_grad(): output = model(image) _, predicted = torch.max(output, 1) print(f"Predicted class: {predicted.item()}") return predicted # 加载图像 image = load_image("path_to_your_image.png") # 使用模型进行预测 predicted_class = predict(model, image)帮我修改错误

已完成理解import torch import torchvision.datasets as datasets import torchvision.transforms as transforms from PIL import Image from torch.utils.data import DataLoader, random_split import torch.nn as nn import torch.nn.functional as F import matplotlib.pyplot as plt # 设置数据变换 transform = transforms.Compose([ transforms.Resize((28, 28)), # 先调整大小,再转换为灰度图 transforms.Grayscale(), # 调整大小后再转换为灰度图 transforms.ToTensor(), ]) # 下载并加载数据集 train_data = datasets.FashionMNIST(root='./data', train=True, download=True, transform=transform) test_data = datasets.FashionMNIST(root='./data', train=False, download=True, transform=transform) # 划分验证集 validation_size = int(0.1 * len(train_data)) train_data, validation_data = random_split(train_data, [len(train_data) - validation_size, validation_size]) # 创建Dataloader train_loader = DataLoader(train_data, batch_size=64, shuffle=True) validation_loader = DataLoader(validation_data, batch_size=64, shuffle=False) # 定义模型 class LeNet(nn.Module): def __init__(self): # 修正了init方法的名称 super(LeNet, self).__init__() # 修正了super的调用 self.conv1 = nn.Conv2d(1, 6, 5, padding=2) self.pool = nn.MaxPool2d(2, 2) self.conv2 = nn.Conv2d(6, 16, 5) self.fc1 = nn.Linear(16 * 5 * 5, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 10) def forward(self, x): x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(x))) x = x.view(-1, 16 * 5 * 5) # 修正了view方法的参数 x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x # 实例化模型 model = LeNet() # 定义损失函数和优化器 criterion = nn.CrossEntropyLoss() optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9) # 定义训练的轮数 num_epochs = 5 # 训练模型 for epoch in range(num_epochs): model.train() running_loss = 0.0 for data in train_loader: inputs, labels = data optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() print(f'Epoch {epoch+1}, loss: {running_loss/len(train_loader)}') # 在验证集上评估模型 model.eval() validation_loss = 0.0 with torch.no_grad(): for data in validation_loader: inputs, labels = data outputs = model(inputs) validation_loss += criterion(outputs, labels).item() print(f'Validation Loss: {validation_loss / len(validation_loader)}') # 设置模型为评估模式 model.eval() # 初始化测试损失和正确预测计数 test_loss = 0.0 correct = 0 # 创建测试数据的Dataloader test_loader = DataLoader(test_data, batch_size=64, shuffle=False) # 遍历测试数据集 with torch.no_grad(): for data in test_loader: inputs, labels = data outputs = model(inputs) test_loss += criterion(outputs, labels).item() _, predicted = torch.max(outputs.data, 1) correct += (predicted == labels).sum().item() # 计算测试损失和准确率 test_loss /= len(test_loader) accuracy = 100 * correct / len(test_data) # 输出测试结果 print(f'Test Loss: {test_loss:.4f}') print(f'Accuracy: {accuracy:.2f}%') # 加载待识别的图像 def load_image(image_path): image = Image.open(image_path).convert('L').resize((28, 28)) # 直接使用PIL进行转换和调整大小 image = transforms.ToTensor()(image) image = image.unsqueeze(0) plt.imshow(image.squeeze().numpy(), cmap='gray') plt.show() return image # 使用模型进行预测 def predict(model, image): model.eval() with torch.no_grad(): output = model(image) _, predicted = torch.max(output, 1) print(f"Predicted class: {predicted.item()}") return predicted # 加载图像 image = load_image("path_to_your_image.png") # 使用模型进行预测 predicted_class = predict(model, image)帮我修改错误

展开阅读网页

在您提供的代码中,有几个地方需要修改以确保代码能够正确运行。以下是修改后的代码和相关解释:

  1. transforms.Resize中,您需要指定三个维度(高度、宽度、通道数),因为FashionMNIST是灰度图像,所以通道数为1。同时,由于FashionMNIST原始图像大小为28x28,不需要调整大小,因此可以删除transforms.Resize

  2. transforms.Grayscale中,由于FashionMNIST已经是灰度图像,这一步也是多余的。

  3. LeNet模型的forward方法中,x.view(-1, 16 * 5 * 5)应该改为x.view(-1, 16 * 5 * 5 * 5),因为输入到self.fc1的维度应该是[batch_size, channels, height, width],而heightwidth都是5。

  4. load_image函数中,您需要确保图像路径是正确的,并且图像已经被转换为灰度图。

以下是修改后的代码:

import torch
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from PIL import Image
from torch.utils.data import DataLoader, random_split
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt

# 设置数据变换
transform = transforms.Compose([
    # transforms.Resize((28, 28)),  # 原始图像已经是28x28,不需要调整大小
    transforms.ToTensor(),  # 直接转换为Tensor
])

# 下载并加载数据集
train_data = datasets.FashionMNIST(root='./data', train=True, download=True, transform=transform)
test_data = datasets.FashionMNIST(root='./data', train=False, download=True, transform=transform)

# 划分验证集
validation_size = int(0.1 * len(train_data))
train_data, validation_data = random_split(train_data, [len(train_data) - validation_size, validation_size])

# 创建Dataloader
train_loader = DataLoader(train_data, batch_size=64, shuffle=True)
validation_loader = DataLoader(validation_data, batch_size=64, shuffle=False)

# 定义模型
class LeNet(nn.Module):
    # ... 省略其他部分 ...

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5 * 5)  # 修改view方法的参数
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# ... 省略其他部分 ...

# 加载待识别的图像
def load_image(image_path):
    image = Image.open(image_path).convert('L').resize((28, 28))  # 确保图像是灰度图并调整大小
    image = transforms.ToTensor()(image)
    image = image.unsqueeze(0)
    plt.imshow(image.squeeze().numpy(), cmap='gray')
    plt.show()
    return image

# ... 省略其他部分 ...

请注意,您需要确保"path_to_your_image.png"是正确的图像路径,并且图像是灰度图。如果图像不是灰度图,您需要在load_image函数中添加转换为灰度图的步骤。1346789101314

更简洁一些
转笔记
简洁
import torch import torchvision.datasets as datasets import torchvision.transforms as transforms from PIL import Image from torch.utils.data import DataLoader, random_split import torch.nn as nn import torch.nn.functional as F import matplotlib.pyplot as plt # 设置数据变换 transform = transforms.Compose([ transforms.Resize((28, 28)), # 先调整大小,再转换为灰度图 transforms.Grayscale(), # 调整大小后再转换为灰度图 transforms.ToTensor(), ]) # 下载并加载数据集 train_data = datasets.FashionMNIST(root='./data', train=True, download=True, transform=transform) test_data = datasets.FashionMNIST(root='./data', train=False, download=True, transform=transform) # 划分验证集 validation_size = int(0.1 * len(train_data)) train_data, validation_data = random_split(train_data, [len(train_data) - validation_size, validation_size]) # 创建Dataloader train_loader = DataLoader(train_data, batch_size=64, shuffle=True) validation_loader = DataLoader(validation_data, batch_size=64, shuffle=False) # 定义模型 class LeNet(nn.Module): def __init__(self): # 修正了init方法的名称 super(LeNet, self).__init__() # 修正了super的调用 self.conv1 = nn.Conv2d(1, 6, 5, padding=2) self.pool = nn.MaxPool2d(2, 2) self.conv2 = nn.Conv2d(6, 16, 5) self.fc1 = nn.Linear(16 * 5 * 5, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 10) def forward(self, x): x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(x))) x = x.view(-1, 16 * 5 * 5) # 修正了view方法的参数 x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x # 实例化模型 model = LeNet() # 定义损失函数和优化器 criterion = nn.CrossEntropyLoss() optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9) # 定义训练的轮数 num_epochs = 5 # 训练模型 for epoch in range(num_epochs): model.train() running_loss = 0.0 for data in train_loader: inputs, labels = data optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() print(f'Epoch {epoch+1}, loss: {running_loss/len(train_loader)}') # 在验证集上评估模型 model.eval() validation_loss = 0.0 with torch.no_grad(): for data in validation_loader: inputs, labels = data outputs = model(inputs) validation_loss += criterion(outputs, labels).item() print(f'Validation Loss: {validation_loss / len(validation_loader)}') # 设置模型为评估模式 model.eval() # 初始化测试损失和正确预测计数 test_loss = 0.0 correct = 0 # 创建测试数据的Dataloader test_loader = DataLoader(test_data, batch_size=64, shuffle=False) # 遍历测试数据集 with torch.no_grad(): for data in test_loader: inputs, labels = data outputs = model(inputs) test_loss += criterion(outputs, labels).item() _, predicted = torch.max(outputs.data, 1) correct += (predicted == labels).sum().item() # 计算测试损失和准确率 test_loss /= len(test_loader) accuracy = 100 * correct / len(test_data) # 输出测试结果 print(f'Test Loss: {test_loss:.4f}') print(f'Accuracy: {accuracy:.2f}%') # 加载待识别的图像 def load_image(image_path): image = Image.open(image_path).convert('L').resize((28, 28)) # 直接使用PIL进行转换和调整大小 image = transforms.ToTensor()(image) image = image.unsqueeze(0) plt.imshow(image.squeeze().numpy(), cmap='gray') plt.show() return image # 使用模型进行预测 def predict(model, image): model.eval() with torch.no_grad(): output = model(image) _, predicted = torch.max(output, 1) print(f"Predicted class: {predicted.item()}") return predicted # 加载图像 image = load_image("path_to_your_image.png") # 使用模型进行预测 predicted_class = predict(model, image)帮我修改错误
相关内容6

数据变换设置1

数据变换 使用transforms.Resize和transforms.Grayscale进行图像预处理。

数据集下载与加载4

数据集加载 使用datasets.FashionMNIST下载并加载FashionMNIST数据集。

数据集划分1

划分验证集 将训练数据划分出10%作为验证集。

创建Dataloader1

创建Dataloader 为训练集和验证集创建Dataloader以批量加载数据。

定义LeNet模型1

模型定义 定义LeNet网络结构,包括卷积层、池化层和全连接层。

训练模型1

模型训练 使用训练集对模型进行训练,并在每个epoch后输出损失。

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