Keras多标签分类网络实现

Keras多标签分类网络实现

(adsbygoogle = window.adsbygoogle || []).push({}); 简谈多分类与多标签分类 简单的说,输入一张图片进行分类: * 这张图片里面的物体(通常认为只有一个物体)属于某一个类,各个类别之间的概率
python下mnist数据集转化为图片

python下mnist数据集转化为图片

(adsbygoogle = window.adsbygoogle || []).push({}); 环境:tensorflow 代码如下 from tensorflow.examples.tutorials.mnist import input_data from scipy import misc import numpy as np import os mnist = input_data.read_data_sets('MNIST_data/',one_hot=True) result_path ='mnist_data\\train' def onehot2id(labels): return list(labels).index(1) if not os.path.exists(result_path): os.mkdir(result_path) labels_txt = open('train_labs.txt','w') for i in range(len(mnist.train.images)): img_vec = mnist.train.images[i,:] img_arr = np.reshape(img_vec,[28,28]) img_lab = mnist.train.labels[i,:] img_id = onehot2id(img_lab) labels_txt.write(str(i)+' '+str(img_id)+'\n')
基于Keras图像相似度计算孪生网络

基于Keras图像相似度计算孪生网络

(adsbygoogle = window.adsbygoogle || []).push({}); import keras from keras.layers import Input,Dense,Conv2D from keras.layers import MaxPooling2D,Flatten,Convolution2D from keras.models import Model import os import numpy as np from PIL import Image from keras.optimizers import SGD from scipy import misc root_path = os.getcwd() train_names = ['bear','blackswan','bus','camel','car','cows','dance','dog','hike','hoc','kite','lucia','mallerd','pigs','soapbox','stro','surf','swing','train','walking'] test_names = ['boat','dance-jump','drift-turn','elephant','libby'] def load_data(seq_names,data_number,seq_len): #生成图片对 print('loading data.....') frame_num = 51 train_data1 = [] train_data2 = [] train_lab = [] count =
Keras 猫狗二分类

Keras 猫狗二分类

(adsbygoogle = window.adsbygoogle || []).push({}); import keras from keras.models import Sequential from keras.layers import Dense,MaxPooling2D,Input,Flatten,Convolution2D,Dropout,GlobalAveragePooling2D from keras.optimizers import SGD from keras.callbacks import TensorBoard,ModelCheckpoint from PIL import Image import os import numpy as np from scipy import misc root_path = os.getcwd() def load_data(): tran_imags = [] labels = [] seq_names = ['cat','dog'] for seq_name in seq_names: frames = sorted(os.listdir(os.path.join(root_path,'data','train_data', seq_name))) for frame in frames: imgs = [os.path.join(root_path, 'data', 'train_data', seq_name, frame)]