_, preds = torch.max(logits.data, 1)

`_, preds = torch.max(logits.data, 1)` 是PyTorch中常见的用于从神经网络输出中获取预测标签的方法。这里 `_` 是一个占位符，通常表示的是计算结果中不关心的部分（在这种情况下，它是最大值对应的索引），而 `preds` 则是经过 `max()` 操作得到的每个样本的最大值所在位置，也就是模型对每个输入样本的预测类别。

在训练MNIST数据集的场景下，`outputs` 可能是经过softmax激活后的概率分布，`logits` 则可能是未经过softmax处理的原始分数。`torch.max()` 函数返回的是每一维元素中的最大值及其索引。`data` 参数表示我们将对张量的CPU内存数据而不是GPU上的张量进行操作。通过这种方式，我们可以在测试阶段获得每个样本的最可能分类。

代码示例：

# 假设inputs是已经经过前向传播的张量
logits = model(inputs)  # model是神经网络模型

# 使用torch.max找到每个样本的最大得分及其索引
_, predicted_classes = torch.max(logits.data, 1)

# 更新统计信息，如总共有多少个样本
total += predicted_classes.size(0)

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))

9个特征的csv文件怎么使用tranformer模型进行三分类python代码

要使用 transformer 模型对具有 9 个特征的 CSV 文件进行三分类，你可以按照以下步骤编写 Python 代码：

1. 导入所需的库和模块：

import pandas as pd
import torch
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from transformers import BertTokenizer, BertForSequenceClassification, AdamW

2. 加载数据：

# 读取 CSV 文件
data = pd.read_csv('your_file.csv')

# 获取特征列和标签列
features = data['your_features_column'].values
labels = data['your_labels_column'].values

# 将标签转换为整数（0、1、2）
labels = labels.astype(int)

3. 数据预处理：

# 划分训练集和测试集
train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.2, random_state=42)

# 初始化 tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)

# 编码文本数据
train_encodings = tokenizer(list(train_features), truncation=True, padding=True)
test_encodings = tokenizer(list(test_features), truncation=True, padding=True)

# 转换为 PyTorch tensors
train_dataset = torch.utils.data.TensorDataset(torch.tensor(train_encodings['input_ids']),
                                               torch.tensor(train_encodings['attention_mask']),
                                               torch.tensor(train_labels))
test_dataset = torch.utils.data.TensorDataset(torch.tensor(test_encodings['input_ids']),
                                              torch.tensor(test_encodings['attention_mask']),
                                              torch.tensor(test_labels))

4. 初始化并训练模型：

# 初始化分类模型
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=3)

# 设置训练参数
optimizer = AdamW(model.parameters(), lr=1e-5)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=16, shuffle=True)

# 训练模型
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model.to(device)
model.train()

for epoch in range(10):
    for batch in train_loader:
        input_ids, attention_mask, labels = batch
        input_ids = input_ids.to(device)
        attention_mask = attention_mask.to(device)
        labels = labels.to(device)

        optimizer.zero_grad()
        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
        loss = outputs.loss
        loss.backward()
        optimizer.step()

# 保存模型
model.save_pretrained('saved_model')

5. 测试模型：

# 加载保存的模型
model = BertForSequenceClassification.from_pretrained('saved_model')

# 预测测试集
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=16, shuffle=False)
model.eval()

predictions = []
with torch.no_grad():
    for batch in test_loader:
        input_ids, attention_mask, labels = batch
        input_ids = input_ids.to(device)
        attention_mask = attention_mask.to(device)

        outputs = model(input_ids, attention_mask=attention_mask)
        logits = outputs.logits
        _, preds = torch.max(logits, dim=1)
        predictions.extend(preds.tolist())

# 输出分类报告
target_names = ['class_0', 'class_1', 'class_2']
print(classification_report(test_labels, predictions, target_names=target_names))

请确保替换代码中的以下内容：
- 'your_file.csv'：你的 CSV 文件路径
- 'your_features_column'：包含特征的列名称
- 'your_labels_column'：包含标签的列名称

还需要根据你的具体需求调整模型参数、训练参数和保存模型的路径。这个示例使用了预训练的 BERT 模型，你可以根据需要选择其他预训练模型。