920. 最优乘车【根据题意建图求最短路】

在这里插入图片描述
https://www.acwing.com/problem/content/description/922/
根据每一条线,建边,注意是有向边。求最短路,最短路的结果是最少的坐车次数,减1即为最少的换乘次数。

#include<bits/stdc++.h>
using namespace std;
const int N=1e3+10;
int g[N][N],dist[N],st[N];
int n,m;
string s,a;
void Dijkstra()
{
    memset(dist,0x3f,sizeof dist);
    dist[1]=0;
    for(int i=0;i<n;i++)
    {
        int t=-1;
        for(int j=1;j<=n;j++) if(!st[j]&&(t==-1 || dist[j]<dist[t])) t=j;
        st[t]=1;
        for(int j=1;j<=n;j++) dist[j]=min(dist[j],dist[t]+g[t][j]);
    }
}
int main(void)
{
    cin>>m>>n;
    memset(g,0x3f,sizeof g);
    getline(cin,s);
    while(m--)
    {
       getline(cin,s);
       vector<int>ve;
       stringstream l(s);
       while(l>>a) ve.push_back(stoi(a));
       for(int i=0;i<ve.size();i++) 
        for(int j=i+1;j<ve.size();j++) 
        {
            int a=ve[i],b=ve[j],c=1;
            g[a][b]=min(g[a][b],c);
        }
    }
    Dijkstra();
    if(dist[n]==0x3f3f3f3f) puts("NO");
    else cout<<dist[n]-1;
    return 0;
}
根据提供的代码和错误信息,主要问题是 `ImageDataGenerator` 的 `flow` 方法不能直接处理字符串类型的像路径。我们需要使用 `flow_from_directory` 方法来从目录中加载像,或者手动将像路径转换为像数组。以下是修正后的完整代码: ```python import json import os import pandas from future.standard_library import install_aliases install_aliases() from pygame.examples.sprite_texture import load_img # 读取 train_label.json 和 val_label.json with open(r'C:\Users\24067\Desktop\train_label.json', 'r', encoding='utf-8') as f: train_labels = json.load(f) with open(r'C:\Users\24067\Desktop\val_label.json', 'r', encoding='utf-8') as f: val_labels = json.load(f) # 创文件名到标签的映射 train_labels_dict = {item['文件名']: item['标签'] for item in train_labels} val_labels_dict = {item['文件名']: item['标签'] for item in val_labels} # 标签映射到类别索引 label_mapping = {"特级": 0, "一级": 1, "二级": 2, "三级": 3} train_image_paths = [] train_image_labels = [] val_image_paths = [] val_image_labels = [] # 获取训练集的片路径和标签 train_folder = r'C:\Users\24067\Desktop\peach_split\train' for filename in os.listdir(train_folder): if filename in train_labels_dict: train_image_paths.append(os.path.join(train_folder, filename)) train_image_labels.append(label_mapping[train_labels_dict[filename]]) # 获取验证集的片路径和标签 val_folder = r'C:\Users\24067\Desktop\peach_split\val' for filename in os.listdir(val_folder): if filename in val_labels_dict: val_image_paths.append(os.path.join(val_folder, filename)) val_image_labels.append(label_mapping[val_labels_dict[filename]]) import numpy as np from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array # 将文件路径列表转换为像数组 def load_images_from_paths(image_paths, target_size=(224, 224)): images = [] for path in image_paths: # 加载片并调整大小 img = load_img(path, target_size=target_size) img_array = img_to_array(img) images.append(img_array) return np.array(images) # 加载训练集片并转换为数组 train_images = load_images_from_paths(train_image_paths) val_images = load_images_from_paths(val_image_paths) # 将标签转换为 NumPy 数组 train_labels = np.array(train_image_labels) val_labels = np.array(val_image_labels) # 定义数据增强器 train_datagen = ImageDataGenerator( rescale=1./255, rotation_range=20, width_shift_range=0.2, height_shift_range=0.2, horizontal_flip=True ) val_datagen = ImageDataGenerator(rescale=1./255) # 训练数据生成器 train_generator = train_datagen.flow( x=train_images, y=train_labels, batch_size=32 ) # 验证数据生成器 val_generator = val_datagen.flow( x=val_images, y=val_labels, batch_size=32 ) # 测试数据生成器 (注意:测试集没有类别标签) test_datagen = ImageDataGenerator(rescale=1./255) test_generator = test_datagen.flow_from_directory( directory=r'C:\Users\24067\Desktop\peach_split\test', target_size=(224, 224), batch_size=32, class_mode=None, # 因为测试集没有标签 shuffle=False ) from tensorflow.keras.applications import ResNet50 from tensorflow.keras.layers import Dense, GlobalAveragePooling2D from tensorflow.keras.models import Model # 加载预训练的 ResNet50 模型,不包含顶层 base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3)) # 构自定义分类层 x = base_model.output x = GlobalAveragePooling2D()(x) x = Dense(1024, activation='relu')(x) predictions = Dense(4, activation='softmax')(x) # 4类 # 构模型 model = Model(inputs=base_model.input, outputs=predictions) # 冻结预训练模型的卷积层 for layer in base_model.layers: layer.trainable = False # 编译模型 model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) from tensorflow.keras.callbacks import EarlyStopping # 提前停止回调,当验证损失在 3 个 epoch 内没有改善时停止训练 early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True) # 训练模型 history = model.fit( train_generator, epochs=3, # 设置为你需要的迭代次数 validation_data=val_generator, callbacks=[early_stopping] ) ``` ### 主要修改点: 1. **使用 `load_images_from_paths` 函数**:将像路径列表转换为像数组,这样可以直接传递给 `ImageDataGenerator` 的 `flow` 方法。 2. **删除重复的数据生成器创**:避免了重复创 `train_generator` 和 `val_generator`。 3. **修复导入语句**:确保所有必要的模块都正确导入。 希望这些修改能解决你的问题。如果有任何其他问题或需要进一步的帮助,请告诉我。
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