Keras数据集加载小结

Keras数据集加载小结

对于keras加载训练数据,官方上没有详说。然而网上查各种资料,写法太多,通过自己跑代码测试总结以下几条,方便自己以后使用。 总的来说kera

python下mnist数据集转化为图片

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') img_path = os.path.join(result_path,str(i)+'.png') misc.imsave(img_path,img_arr) 以
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

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 = 0 while count < data_number:
Keras 猫狗二分类

Keras 猫狗二分类

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)] imgs = np.array(Image.open(imgs[0])) tran_imags.append(imgs) if