使用t-SNE可视化的代码记录

最新推荐文章于 2025-05-30 11:48:05 发布

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最新推荐文章于 2025-05-30 11:48:05 发布 · 2.7k 阅读

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本文记录了使用t-SNE进行数据可视化的实践过程，通过代码实现将高维数据降维并展示二维图像。结果显示，t-SNE能够有效地揭示数据的内在结构，帮助理解复杂数据分布。

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import os
import keras
from keras.preprocessing import image
from keras.applications.imagenet_utils import decode_predictions, preprocess_input
from keras.models import Model

model = keras.applications.VGG16(weights='imagenet', include_top=True)
feat_extractor = Model(inputs=model.input, outputs=model.get_layer("fc2").output)

import numpy as np
import matplotlib.pyplot as plt

def load_image(path):
    img = image.load_img(path, target_size=model.input_shape[1:3])
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = preprocess_input(x)
    return img, x
    
images_path = 'D:/code/python/data/101_ObjectCategories'
image_extensions = ['.jpg', '.png', '.jpeg']   # case-insensitive (upper/lower doesn't matter)
max_num_images = 10000

import random

images = [os.path.join(dp, f) for dp, dn, filenames in os