使⽤t-SNE可视化的代码记录
import os
import keras
from keras.preprocessing import image
from keras.applications.imagenet_utils import decode_predictions, preprocess_input
dels import Model
model = keras.applications.VGG16(weights='imagenet', include_top=True)
feat_extractor = Model(inputs=model.input, _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.walk(images_path)for f in filenames if os.path.splitext(f)[1].lower()in image_extensions] if max_num_images <len(images):
images =[images[i]for i in sorted(random.sample(xrange(len(images)), max_num_images))]
print("keeping %d images to analyze"%len(images))
import time
tic = time.clock()
features =[]
for i, image_path in enumerate(images):
if i %500==0:
toc = time.clock()
elap = toc-tic;
print("analyzing image %d / %d. Time: %4.4f seconds."%(i,len(images), elap))
import pickletic = time.clock()
img, x = load_image(image_path);
feat = feat_extractor.predict(x)[0]
features.append(feat)
print('finished extracting features for %d images'%len(images))
from sklearn.decomposition import PCA
features = np.array(features)
pca = PCA(n_components=300)
pca.fit(features)
pca_features = ansform(features)
import pickle
pickle.dump([images, pca_features, pca],open('./features_caltech101.p','wb'))
images, pca_features, pca = pickle.load(open('./features_caltech101.p','rb'))
for img, f in list(zip(images, pca_features))[0:5]:
print("image: %s, features: %0.2f,%0.2f,%0.2f,% "%(img, f[0], f[1], f[2], f[3]))
num_images_to_plot =1000
if len(images)> num_images_to_plot:
sort_order =sorted(random.sample(range(len(images)), num_images_to_plot))
images =[images[i]for i in sort_order]
pca_features =[pca_features[i]for i in sort_order]
X = np.array(pca_features)
tsne = TSNE(n_components=2, learning_rate=150, perplexity=30, angle=0.2, verbose=2).fit_transform(X) tx, ty = tsne[:,0], tsne[:,1]
tx =(tx-np.min(tx))/(np.max(tx)- np.min(tx))
ty =(ty-np.min(ty))/(np.max(ty)- np.min(ty))
width =4000
height =3000
max_dim =100
full_image = w('RGBA',(width, height))
for img, x, y in zip(images, tx, ty):
tile = Image.open(img)
rs =max(1, tile.width/max_dim, tile.height/max_dim)
tile = size((int(tile.width/rs),int(tile.height/rs)), Image.ANTIALIAS)
full_image.paste(tile,(int((width-max_dim)*x),int((height-max_dim)*y)), vert('RGBA'))
matplotlib.pyplot.figure(figsize =(16,12))
imshow(full_image)
matplotlib.pyplot.show()
full_image.save("./example-tSNE-caltech101.png")
效果如下图：

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