图像分类实验的可视化热力图该怎么制作呀？

# "cuda" only when GPUs are available.
device = "cuda" if torch.cuda.is_available() else "cpu"

_exp_name = "sample"

# The number of training epochs and patience.
n_epochs = 4
patience = 300 # If no improvement in 'patience' epochs, early stop

# Initialize a model, and put it on the device specified.
model = Classifier().to(device)

# For the classification task, we use cross-entropy as the measurement of performance.
criterion = nn.CrossEntropyLoss()

# Initialize optimizer, you may fine-tune some hyperparameters such as learning rate on your own.
optimizer = torch.optim.Adam(model.parameters(), lr=0.0003, weight_decay=1e-5) 

# Initialize trackers, these are not parameters and should not be changed
stale = 0
best_acc = 0

for epoch in range(n_epochs):

    # ---------- Training ----------
    # Make sure the model is in train mode before training.
    model.train()

    # These are used to record information in training.
    train_loss = []
    train_accs = []

    for batch in tqdm(train_loader):

        # A batch consists of image data and corresponding labels.
        imgs, labels = batch
        #imgs = imgs.half()
        #print(imgs.shape,labels.shape)

        # Forward the data. (Make sure data and model are on the same device.)
        logits = model(imgs.to(device))

        # Calculate the cross-entropy loss.
        # We don't need to apply softmax before computing cross-entropy as it is done automatically.
        loss = criterion(logits, labels.to(device))

        # Gradients stored in the parameters in the previous step should be cleared out first.
        optimizer.zero_grad()

        # Compute the gradients for parameters.
        loss.backward()

        # Clip the gradient norms for stable training.
        grad_norm = nn.utils.clip_grad_norm_(model.parameters(), max_norm=10)

        # Update the parameters with computed gradients.
        optimizer.step()

        # Compute the accuracy for current batch.
        # 取全连接层最后输出的概率最大值作为预测的类别
        acc = (logits.argmax(dim=-1) == labels.to(device)).float().mean()

        # Record the loss and accuracy.
        train_loss.append(loss.item())
        train_accs.append(acc)
        
    train_loss = sum(train_loss) / len(train_loss)
    train_acc = sum(train_accs) / len(train_accs)

    # Print the information.
    print(f"[ Train | {epoch + 1:03d}/{n_epochs:03d} ] loss = {train_loss:.5f}, acc = {train_acc:.5f}")

    # ---------- Validation ----------
    # Make sure the model is in eval mode so that some modules like dropout are disabled and work normally.
    model.eval()

    # These are used to record information in validation.
    valid_loss = []
    valid_accs = []

    # Iterate the validation set by batches.
    for batch in tqdm(valid_loader):

        # A batch consists of image data and corresponding labels.
        imgs, labels = batch
        #imgs = imgs.half()

        # We don't need gradient in validation.
        # Using torch.no_grad() accelerates the forward process.
        with torch.no_grad():
            logits = model(imgs.to(device))

        # We can still compute the loss (but not the gradient).
        loss = criterion(logits, labels.to(device))

        # Compute the accuracy for current batch.
        acc = (logits.argmax(dim=-1) == labels.to(device)).float().mean()

        # Record the loss and accuracy.
        valid_loss.append(loss.item())
        valid_accs.append(acc)
        #break

    # The average loss and accuracy for entire validation set is the average of the recorded values.
    valid_loss = sum(valid_loss) / len(valid_loss)
    valid_acc = sum(valid_accs) / len(valid_accs)

    # Print the information.
    print(f"[ Valid | {epoch + 1:03d}/{n_epochs:03d} ] loss = {valid_loss:.5f}, acc = {valid_acc:.5f}")


    # update logs
    if valid_acc > best_acc:
        with open(f"./{_exp_name}_log.txt","a"):
            print(f"[ Valid | {epoch + 1:03d}/{n_epochs:03d} ] loss = {valid_loss:.5f}, acc = {valid_acc:.5f} -> best")
    else:
        with open(f"./{_exp_name}_log.txt","a"):
            print(f"[ Valid | {epoch + 1:03d}/{n_epochs:03d} ] loss = {valid_loss:.5f}, acc = {valid_acc:.5f}")


    # save models
    if valid_acc > best_acc:
        print(f"Best model found at epoch {epoch}, saving model")
        torch.save(model.state_dict(), f"{_exp_name}_best.ckpt") # only save best to prevent output memory exceed error
        best_acc = valid_acc
        stale = 0
    else:
        stale += 1
        if stale > patience:
            print(f"No improvment {patience} consecutive epochs, early stopping")
            break

针对如何制作图像分类实验结果的可视化热力图，以下是解决方案：

1. 首先，需要通过训练一个图像分类模型来获取分类结果。可以使用参考资料中提供的代码段落1来构建一个基本的卷积神经网络分类器，也可以根据自己的需要进行修改和优化。
2. 在训练过程中，需要统计每个图像被分类到每个类别的概率，可以在代码训练过程中记录这些概率。在PyTorch中，可以使用softmax函数将线性输出转换为概率分布。例如，可以在forward函数中添加以下代码来获取分类概率分布：

def forward(self, x):
    out = self.cnn(x)
    out = out.view(out.size()[0], -1)
    logits = self.fc(out)
    probs = nn.functional.softmax(logits, dim=1)
    return probs

1. 在得到每个图像的分类概率后，可以将这些概率绘制成热力图来直观地展示分类结果。可以使用Python中的matplotlib包来生成热力图。
2. 对于每一张图片，将它分类到每个类别的概率按照类别顺序排成一行，并以该行作为热力图中的一个横坐标。可以使用以下代码实现将每个图像的分类结果存储在一个DataFrame中：

import pandas as pd
import os
from PIL import Image
from torchvision import transforms

def get_image_probs(model, image_path):
    transform = transforms.Compose([
        transforms.Resize(size=(128, 128)),
        transforms.ToTensor(),
    ])
    image = Image.open(image_path)
    inputs = transform(image).unsqueeze(0)
    probs = model(inputs)
    return probs.detach().numpy()[0]

def get_classification_results(model, data_directory):
    results = []
    for class_name in sorted(os.listdir(data_directory)):
        class_directory = os.path.join(data_directory, class_name)
        for image_name in os.listdir(class_directory):
            image_path = os.path.join(class_directory, image_name)
            probs = get_image_probs(model, image_path)
            result = {"ImageName": image_name, "ClassName": class_name}
            for class_idx, class_prob in enumerate(probs):
                result[f"Class{class_idx}Prob"] = class_prob
            results.append(result)
    return pd.DataFrame(results)

model = Classifier()
result_df = get_classification_results(model, data_directory)

上述代码将每个图像的分类概率存储在一个DataFrame中，每一行表示一个图像，包含ImageName（图像文件名）、ClassName（类别名）和每个类别的概率。结果可以按照需要进行处理和分析。 5. 使用pandas的pivot_table函数将分类结果转换为热力图的输入格式。以下代码示例将结果转换为以Class为列、ImageName为行、ClassProb为值的DataFrame，然后使用matplotlib的imshow函数生成热力图。

result_pivot = result_df.pivot_table(
    index="ImageName",
    columns="ClassName",
    values=[f"Class{i}Prob" for i in range(11)],
)
result_pivot = result_pivot.reindex(sorted(result_pivot.columns), axis=1)

import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(10, 10))
ax.imshow(result_pivot, aspect="auto", cmap="jet")
ax.set_yticks(range(len(result_pivot)))
ax.set_xticks(range(len(result_pivot.columns)))
ax.set_yticklabels(result_pivot.index)
ax.set_xticklabels(result_pivot.columns, rotation=90)
ax.set_xlabel("Class")
ax.set_ylabel("ImageName")
plt.show()

上述代码中，cmap参数指定使用jet颜色映射，aspect参数指定热力图的宽高比例为1:1，这样热力图就不会因为图片名称太长而变形。set_yticks和set_xticks方法用于设置刻度位置，set_yticklabels和set_xticklabels方法用于设置刻度标签。最终用show方法显示热力图。

综上所述，以上是制作图像分类实验结果的可视化热力图的具体解决方案。