{"id":1131725,"date":"2025-01-08T20:51:03","date_gmt":"2025-01-08T12:51:03","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1131725.html"},"modified":"2025-01-08T20:51:05","modified_gmt":"2025-01-08T12:51:05","slug":"%e5%a6%82%e4%bd%95%e5%b0%86%e5%9b%be%e7%89%87%e5%88%b6%e4%bd%9c%e6%88%90%e6%95%b0%e6%8d%ae%e9%9b%86python","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1131725.html","title":{"rendered":"\u5982\u4f55\u5c06\u56fe\u7247\u5236\u4f5c\u6210\u6570\u636e\u96c6python"},"content":{"rendered":"

\"\u5982\u4f55\u5c06\u56fe\u7247\u5236\u4f5c\u6210\u6570\u636e\u96c6python\"<\/p>\n

\u8981\u5c06\u56fe\u7247\u5236\u4f5c\u6210\u6570\u636e\u96c6\u5e76\u5728Python\u4e2d\u4f7f\u7528\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u51e0\u4e2a\u6838\u5fc3\u6b65\u9aa4\uff1a\u51c6\u5907\u56fe\u7247\u3001\u6807\u7b7e\u6570\u636e\u3001\u56fe\u50cf\u9884\u5904\u7406\u3001\u6570\u636e\u96c6\u5212\u5206\u3001\u6570\u636e\u589e\u5f3a<\/strong>\u3002\u5176\u4e2d\uff0c\u56fe\u50cf\u9884\u5904\u7406\u662f\u6700\u5173\u952e\u7684\u4e00\u6b65\uff0c\u56e0\u4e3a\u826f\u597d\u7684\u9884\u5904\u7406\u4e0d\u4ec5\u53ef\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u51c6\u786e\u6027\uff0c\u8fd8\u80fd\u5927\u5927\u51cf\u5c11\u8bad\u7ec3\u65f6\u95f4\u548c\u8d44\u6e90\u6d88\u8017\u3002\u8be6\u7ec6\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n

\u4e00\u3001\u51c6\u5907\u56fe\u7247<\/h3>\n<\/p>\n

\u5728\u5236\u4f5c\u6570\u636e\u96c6\u7684\u521d\u59cb\u9636\u6bb5\uff0c\u9996\u5148\u9700\u8981\u6536\u96c6\u6216\u751f\u6210\u7528\u4e8e\u8bad\u7ec3\u548c\u6d4b\u8bd5\u7684\u56fe\u7247\u3002\u56fe\u7247\u53ef\u4ee5\u6765\u6e90\u4e8e\u4e92\u8054\u7f51\u3001\u76f8\u673a\u62cd\u6444\u3001\u6216\u662f\u73b0\u6709\u7684\u6570\u636e\u96c6\u3002\u786e\u4fdd\u56fe\u7247\u8d28\u91cf\u826f\u597d\uff0c\u5e76\u4e14\u4e0d\u540c\u5206\u7c7b\u7684\u56fe\u7247\u6570\u91cf\u5e73\u8861\uff0c\u4ee5\u514d\u9020\u6210\u6a21\u578b\u504f\u5dee\u3002<\/p>\n<\/p>\n

1.1 \u56fe\u7247\u6536\u96c6<\/h4>\n<\/p>\n

\u56fe\u7247\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u6536\u96c6\uff0c\u5305\u62ec\u4ece\u7f51\u7edc\u4e0a\u4e0b\u8f7d\u3001\u4f7f\u7528\u76f8\u673a\u62cd\u6444\u3001\u6216\u76f4\u63a5\u4f7f\u7528\u73b0\u6210\u7684\u6570\u636e\u96c6\u3002\u4f8b\u5982\uff0c\u5f88\u591a\u5f00\u6e90\u6570\u636e\u96c6\u5982ImageNet\u3001CIFAR-10\u3001MNIST\u7b49\u90fd\u53ef\u4ee5\u4e0b\u8f7d\u5e76\u4f7f\u7528\u3002<\/p>\n<\/p>\n

1.2 \u56fe\u7247\u5b58\u50a8<\/h4>\n<\/p>\n

\u4e3a\u65b9\u4fbf\u7ba1\u7406\u548c\u4f7f\u7528\uff0c\u5efa\u8bae\u5c06\u56fe\u7247\u6309\u7167\u7c7b\u522b\u5b58\u50a8\u5728\u4e0d\u540c\u7684\u6587\u4ef6\u5939\u4e2d\u3002\u4f8b\u5982\uff0c\u5982\u679c\u8981\u8fdb\u884c\u732b\u548c\u72d7\u7684\u5206\u7c7b\uff0c\u53ef\u4ee5\u521b\u5efa\u4e24\u4e2a\u6587\u4ef6\u5939\u5206\u522b\u547d\u540d\u4e3a\u201ccats\u201d\u548c\u201cdogs\u201d\uff0c\u7136\u540e\u5c06\u5bf9\u5e94\u7684\u56fe\u7247\u653e\u5165\u76f8\u5e94\u7684\u6587\u4ef6\u5939\u4e2d\u3002<\/p>\n<\/p>\n

\u4e8c\u3001\u6807\u7b7e\u6570\u636e<\/h3>\n<\/p>\n

\u6807\u7b7e\u6570\u636e\u662f\u6307\u6bcf\u5f20\u56fe\u7247\u5bf9\u5e94\u7684\u5206\u7c7b\u6807\u7b7e\uff0c\u7528\u4e8e\u76d1\u7763\u5b66\u4e60\u3002\u6807\u7b7e\u6570\u636e\u53ef\u4ee5\u901a\u8fc7\u624b\u52a8\u6807\u6ce8\u6216\u81ea\u52a8\u751f\u6210\u3002<\/p>\n<\/p>\n

2.1 \u624b\u52a8\u6807\u6ce8<\/h4>\n<\/p>\n

\u624b\u52a8\u6807\u6ce8\u662f\u6700\u5e38\u89c1\u7684\u65b9\u5f0f\uff0c\u7279\u522b\u662f\u5728\u6570\u636e\u91cf\u4e0d\u5927\u7684\u60c5\u51b5\u4e0b\u3002\u53ef\u4ee5\u4f7f\u7528Excel\u8868\u683c\u6216\u6587\u672c\u6587\u4ef6\u6765\u8bb0\u5f55\u6bcf\u5f20\u56fe\u7247\u7684\u8def\u5f84\u53ca\u5176\u5bf9\u5e94\u7684\u6807\u7b7e\u3002<\/p>\n<\/p>\n

2.2 \u81ea\u52a8\u751f\u6210<\/h4>\n<\/p>\n

\u5728\u6570\u636e\u91cf\u8f83\u5927\u65f6\uff0c\u53ef\u4ee5\u7f16\u5199\u811a\u672c\u81ea\u52a8\u751f\u6210\u6807\u7b7e\u6570\u636e\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u904d\u5386\u6bcf\u4e2a\u5206\u7c7b\u6587\u4ef6\u5939\u4e2d\u7684\u56fe\u7247\uff0c\u81ea\u52a8\u751f\u6210\u5305\u542b\u56fe\u7247\u8def\u5f84\u548c\u6807\u7b7e\u7684CSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n

\u4e09\u3001\u56fe\u50cf\u9884\u5904\u7406<\/h3>\n<\/p>\n

\u56fe\u50cf\u9884\u5904\u7406\u662f\u5c06\u539f\u59cb\u56fe\u7247\u6570\u636e\u8f6c\u6362\u4e3a\u9002\u5408\u6a21\u578b\u8bad\u7ec3\u7684\u683c\u5f0f\u3002\u8fd9\u4e00\u6b65\u975e\u5e38\u91cd\u8981\uff0c\u56e0\u4e3a\u826f\u597d\u7684\u9884\u5904\u7406\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n

3.1 \u56fe\u50cf\u7f29\u653e<\/h4>\n<\/p>\n

\u4e0d\u540c\u7684\u6a21\u578b\u5bf9\u8f93\u5165\u56fe\u50cf\u7684\u5c3a\u5bf8\u6709\u4e0d\u540c\u7684\u8981\u6c42\u3002\u5e38\u89c1\u7684\u5c3a\u5bf8\u6709224×224\u3001256×256\u7b49\u3002\u53ef\u4ee5\u4f7f\u7528PIL\u5e93\u6216OpenCV\u5e93\u8fdb\u884c\u56fe\u50cf\u7f29\u653e\u3002<\/p>\n<\/p>\n

from PIL import Image<\/p>\n
image = Image.open('path_to_image.jpg')<\/p>\n
image = image.resize((224, 224))<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.2 \u56fe\u50cf\u5f52\u4e00\u5316<\/h4>\n<\/p>\n
\u5f52\u4e00\u5316\u662f\u5c06\u56fe\u50cf\u50cf\u7d20\u503c\u7f29\u653e\u52300-1\u4e4b\u95f4\uff0c\u5e38\u7528\u7684\u65b9\u5f0f\u662f\u5c06\u50cf\u7d20\u503c\u9664\u4ee5255\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
image_array = np.array(image) \/ 255.0<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.3 \u56fe\u50cf\u589e\u5f3a<\/h4>\n<\/p>\n
\u56fe\u50cf\u589e\u5f3a\u662f\u901a\u8fc7\u5bf9\u539f\u59cb\u56fe\u50cf\u8fdb\u884c\u5404\u79cd\u53d8\u6362\uff08\u5982\u65cb\u8f6c\u3001\u7ffb\u8f6c\u3001\u88c1\u526a\u7b49\uff09\u6765\u751f\u6210\u65b0\u7684\u8bad\u7ec3\u6837\u672c\uff0c\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u3002<\/p>\n<\/p>\n
from tensorflow.keras.preprocessing.image import ImageDataGenerator<\/p>\n
datagen = ImageDataGenerator(rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u6570\u636e\u96c6\u5212\u5206<\/h3>\n<\/p>\n
\u5c06\u6570\u636e\u96c6\u5212\u5206\u4e3a\u8bad\u7ec3\u96c6\u3001\u9a8c\u8bc1\u96c6\u548c\u6d4b\u8bd5\u96c6\u662f\u673a\u5668\u5b66\u4e60<\/a>\u4efb\u52a1\u4e2d\u7684\u5e38\u89c1\u6b65\u9aa4\u3002\u901a\u5e38\u7684\u5212\u5206\u6bd4\u4f8b\u662f70%\u7528\u4e8e\u8bad\u7ec3\uff0c20%\u7528\u4e8e\u9a8c\u8bc1\uff0c10%\u7528\u4e8e\u6d4b\u8bd5\u3002<\/p>\n<\/p>\n
4.1 \u5212\u5206\u6570\u636e\u96c6<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528scikit-learn\u5e93\u4e2d\u7684trAI<\/a>n_test_split<\/code>\u51fd\u6570\u6765\u5212\u5206\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n
from sklearn.model_selection import train_test_split<\/p>\n
train_images, test_images, train_labels, test_labels = train_test_split(images, labels, test_size=0.1, random_state=42)<\/p>\n
train_images, val_images, train_labels, val_labels = train_test_split(train_images, train_labels, test_size=0.2, random_state=42)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.2 \u786e\u4fdd\u6570\u636e\u5e73\u8861<\/h4>\n<\/p>\n
\u5728\u5212\u5206\u6570\u636e\u96c6\u65f6\uff0c\u9700\u8981\u786e\u4fdd\u6bcf\u4e2a\u7c7b\u522b\u7684\u56fe\u7247\u6570\u91cf\u5747\u8861\uff0c\u4ee5\u9632\u6b62\u6a21\u578b\u5728\u67d0\u4e00\u7c7b\u522b\u4e0a\u8868\u73b0\u4e0d\u4f73\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u6570\u636e\u589e\u5f3a<\/h3>\n<\/p>\n
\u6570\u636e\u589e\u5f3a\u662f\u901a\u8fc7\u5bf9\u8bad\u7ec3\u6570\u636e\u8fdb\u884c\u5404\u79cd\u53d8\u6362\uff08\u5982\u65cb\u8f6c\u3001\u7f29\u653e\u3001\u88c1\u526a\u7b49\uff09\u6765\u589e\u52a0\u6570\u636e\u91cf\uff0c\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u3002<\/p>\n<\/p>\n
5.1 \u4f7f\u7528ImageDataGenerator<\/h4>\n<\/p>\n
TensorFlow\u548cKeras\u63d0\u4f9b\u4e86ImageDataGenerator<\/code>\u7c7b\u6765\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u589e\u5f3a\u3002<\/p>\n<\/p>\n
datagen = ImageDataGenerator(rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest')<\/p>\n
datagen.fit(train_images)<\/p>\n
<\/code><\/pre>\n<\/p>\n
5.2 \u5e94\u7528\u6570\u636e\u589e\u5f3a<\/h4>\n<\/p>\n
\u5728\u8bad\u7ec3\u6a21\u578b\u65f6\uff0c\u53ef\u4ee5\u5c06\u589e\u5f3a\u540e\u7684\u6570\u636e\u8f93\u5165\u5230\u6a21\u578b\u4e2d\u3002<\/p>\n<\/p>\n
model.fit(datagen.flow(train_images, train_labels, batch_size=32), epochs=50, validation_data=(val_images, val_labels))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u4fdd\u5b58\u548c\u52a0\u8f7d\u6570\u636e\u96c6<\/h3>\n<\/p>\n
\u5728\u5b8c\u6210\u6570\u636e\u9884\u5904\u7406\u548c\u5212\u5206\u540e\uff0c\u53ef\u4ee5\u5c06\u6570\u636e\u96c6\u4fdd\u5b58\u4e3a\u6587\u4ef6\uff0c\u4ee5\u4fbf\u540e\u7eed\u4f7f\u7528\u3002<\/p>\n<\/p>\n
6.1 \u4fdd\u5b58\u6570\u636e\u96c6<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528NumPy\u6216Pandas\u5e93\u5c06\u5904\u7406\u540e\u7684\u6570\u636e\u4fdd\u5b58\u4e3a\u6587\u4ef6\u3002<\/p>\n<\/p>\n
np.save('train_images.npy', train_images)<\/p>\n
np.save('train_labels.npy', train_labels)<\/p>\n
np.save('val_images.npy', val_images)<\/p>\n
np.save('val_labels.npy', val_labels)<\/p>\n
np.save('test_images.npy', test_images)<\/p>\n
np.save('test_labels.npy', test_labels)<\/p>\n
<\/code><\/pre>\n<\/p>\n
6.2 \u52a0\u8f7d\u6570\u636e\u96c6<\/h4>\n<\/p>\n
\u5728\u9700\u8981\u4f7f\u7528\u6570\u636e\u96c6\u65f6\uff0c\u53ef\u4ee5\u76f4\u63a5\u4ece\u6587\u4ef6\u4e2d\u52a0\u8f7d\u3002<\/p>\n<\/p>\n
train_images = np.load('train_images.npy')<\/p>\n
train_labels = np.load('train_labels.npy')<\/p>\n
val_images = np.load('val_images.npy')<\/p>\n
val_labels = np.load('val_labels.npy')<\/p>\n
test_images = np.load('test_images.npy')<\/p>\n
test_labels = np.load('test_labels.npy')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u5c06\u56fe\u7247\u5236\u4f5c\u6210\u6570\u636e\u96c6\u5e76\u5728Python\u4e2d\u4f7f\u7528<\/strong>\uff0c\u9700\u8981\u7ecf\u8fc7\u51c6\u5907\u56fe\u7247\u3001\u6807\u7b7e\u6570\u636e\u3001\u56fe\u50cf\u9884\u5904\u7406\u3001\u6570\u636e\u96c6\u5212\u5206\u3001\u6570\u636e\u589e\u5f3a\u7b49\u6b65\u9aa4\u3002\u6bcf\u4e00\u6b65\u90fd\u81f3\u5173\u91cd\u8981\uff0c\u7279\u522b\u662f\u56fe\u50cf\u9884\u5904\u7406\uff0c\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\u548c\u8bad\u7ec3\u6548\u7387\u3002\u901a\u8fc7\u5408\u7406\u7684\u9884\u5904\u7406\u548c\u6570\u636e\u589e\u5f3a\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u63d0\u9ad8\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u4ece\u800c\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u53d6\u5f97\u66f4\u597d\u7684\u6548\u679c\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u9009\u62e9\u9002\u5408\u7684\u56fe\u7247\u683c\u5f0f\u6765\u5236\u4f5c\u6570\u636e\u96c6\uff1f<\/strong>
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