像这样的事情应该做的工作

当你在做一些新的事情时，很可能会出错。 使用风险自负，或者在样本上尝试并在单独的目录中完全尝试

import pandas as pd
import os
import numpy as np
import shutil

# source is the current directory
# Open dataset file
dataset = pd.read_csv('dataset.csv')
file_names = list(dataset['filenames'].values)
img_labels = list(dataset['labels'].values)

folders_to_be_created = np.unique(list(dataset['labels'])).values

source = os.getcwd()

for new_path in folders_to_be_created:
    if not os.path.exists(".//" + new_path):
        os.makedirs(new_path)

## Be sure that there is nothing else in your directory except the data, csv and the code file, IT's Better to only have your data in that directory and reference the CSV file from a different Directory...

folders = folders_to_be_created.copy()

for f in range(len(file_names)):

  current_img = file_names[f]
  current_label = img_labels[f]

   ## **Check this Line Accordingly** 

  shutil.move("path//to//current//file", "path//to//new//destination//folder//current_label//")

• Shutil 的链接

• 另一个链接

使用flow_from_dataframe

import pandas as pd
data = pd.read_csv("filename.csv") 
df["category"] = df["category"].replace({0: 'cat', 1: 'dog'}) 
train_df, validate_df = train_test_split(df, test_size=0.20, random_state=42)
train_df = train_df.reset_index(drop=True)
validate_df = validate_df.reset_index(drop=True)

train_datagen = ImageDataGenerator(
    rotation_range=15,
    rescale=1./255,
    shear_range=0.1,
    zoom_range=0.2,
    horizontal_flip=True,
    width_shift_range=0.1,
    height_shift_range=0.1
)

train_generator = train_datagen.flow_from_dataframe(
    train_df, 
    "../input/train/train/", 
    x_col='filename',
    y_col='category',
    target_size=IMAGE_SIZE,
    class_mode='categorical',
    batch_size=batch_size
)

试试这个：

training_set = train_datagen.flow_from_directory(training_path,
                                             target_size = (64, 64),
                                             batch_size = 32,
                                             class_mode = 'binary')


imgs, labels = next(training_set)