CNN图像识别
2021/5/12 18:27:56
本文主要是介绍CNN图像识别,对大家解决编程问题具有一定的参考价值,需要的程序猿们随着小编来一起学习吧!
#数据加载 %matplotlib inline import matplotlib.pyplot as plt import tensorflow as tf import numpy as np import sklearn def load_batch(name): with open(name,'rb') as f: data_dict = np.load(f,encoding='bytes') images = data_dict[b'data'] labels = data_dict[b'labels'] images = images.reshape(10000,3,32,32) images = images.transpose(0,2,3,1) labels = np.array(labels) return images,labels def load_data(): images_train = [] labels_train = [] for i in range(5): f = 'data_batch_%d'%(i+1) image_batch,label_batch=load_batch(f) images_train.append(image_batch) labels_train.append(label_batch) Xtrain = np.concatenate(images_train) Ytrain = np.concatenate(labels_train) del image_batch,label_batch Xtest,Ytest = load_batch('test_batch') print('finished loadding~~~') return Xtrain,Ytrain,Xtest,Ytest Xtrain,Ytrain,Xtest,Ytest = load_data() plt.imshow(Xtrain[6])
finished loadding~~~ <matplotlib.image.AxesImage at 0xf2e8be0>
[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-tk372Jlk-1620811936085)(output_1_2.png)]
!pip install sklearn
Collecting sklearn Downloading sklearn-0.0.tar.gz (1.1 kB) Collecting scikit-learn Downloading scikit_learn-0.22.2.post1-cp35-cp35m-win_amd64.whl (6.2 MB) Requirement already satisfied: numpy>=1.11.0 in c:\programdata\anaconda3\envs\tensorflow\lib\site-packages (from scikit-learn->sklearn) (1.18.5) Collecting joblib>=0.11 Downloading joblib-0.14.1-py2.py3-none-any.whl (294 kB) Collecting scipy>=0.17.0 Downloading scipy-1.4.1-cp35-cp35m-win_amd64.whl (30.8 MB) Building wheels for collected packages: sklearn Building wheel for sklearn (setup.py): started Building wheel for sklearn (setup.py): finished with status 'done' Created wheel for sklearn: filename=sklearn-0.0-py2.py3-none-any.whl size=1316 sha256=fcaf738561a52706f6b1d4541d249d0d2c8170280ea59d76a4f236c6cdb4d98d Stored in directory: c:\users\st\appdata\local\pip\cache\wheels\9e\ec\a6\33cdb5605b0b150074213e154792654a1006e6e6807dc7ca6f Successfully built sklearn Installing collected packages: scipy, joblib, scikit-learn, sklearn Successfully installed joblib-0.14.1 scikit-learn-0.22.2.post1 scipy-1.4.1 sklearn-0.0 DEPRECATION: Python 3.5 reached the end of its life on September 13th, 2020. Please upgrade your Python as Python 3.5 is no longer maintained. pip 21.0 will drop support for Python 3.5 in January 2021. pip 21.0 will remove support for this functionality. WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000000004793588>: Failed to establish a new connection: [Errno 11004] getaddrinfo failed',)': /packages/1e/7a/dbb3be0ce9bd5c8b7e3d87328e79063f8b263b2b1bfa4774cb1147bfcd3f/sklearn-0.0.tar.gz WARNING: Retrying (Retry(total=3, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000000004793780>: Failed to establish a new connection: [Errno 11004] getaddrinfo failed',)': /packages/1e/7a/dbb3be0ce9bd5c8b7e3d87328e79063f8b263b2b1bfa4774cb1147bfcd3f/sklearn-0.0.tar.gz
#定义标签字典,每一个数字所代表的图像类别名称 label_dict = {0:"airplane",1:"automobile",2:"bird",3:"cat",4:"deer",5:"dog",6:"frog",7:"horse",8:"ship",9:"trunk"} #定义显示图像数据及其对应标签的函数 def plot_images_labels_prediction(images,labels,prediction,idx,num=10): fig = plt.gcf() fig.set_size_inches(12,6) if num > 10: num = 10 for i in range(0,num): ax = plt.subplot(2,5,1+i) ax.imshow(images[idx],cmap="binary") title = str(i)+','+label_dict[labels[idx]] if len(prediction)>0: title+='=>'+label_dict[prediction[idx]] ax.set_title(title,fontsize=10) idx += 1 plt.show() plot_images_labels_prediction(Xtest,Ytest,[],1,10)
[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-G3PVdaX7-1620811936098)(output_3_0.png)]
#数据预处理 #显示第一个图像的第一个像素点 Xtrain[0][0][0]
array([59, 62, 63], dtype=uint8)
#将图像数字标准化Xtrain_normalize = Xtrain.astype('float32')/255.0Xtest_normalize = Xtest.astype('float32')/255.0
#查看预处理后图像数据信息Xtrain_normalize[0][0][0]
array([ 0.23137255, 0.24313726, 0.24705882], dtype=float32)
# 独热编码from sklearn.preprocessing import OneHotEncoderencoder = OneHotEncoder(sparse=False)yy = [[0],[1],[2],[3],[4],[5],[6],[7],[8],[9]]encoder.fit(yy)Ytrain_reshape =Ytrain.reshape(-1,1)Ytrain_onehot = encoder.transform(Ytrain_reshape)Ytest_reshape = Ytest.reshape(-1,1)Ytest_onehot = encoder.transform(Ytest_reshape)Ytrain[:10]
array([6, 9, 9, 4, 1, 1, 2, 7, 8, 3])
Ytrain_onehot.shape
(50000, 10)
Ytrain[:5]
array([6, 9, 9, 4, 1])
Ytrain_onehot[:5]
array([[ 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.], [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.], [ 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.], [ 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.]])
#定义共享函数#定义权值 def weight(shape): return tf.Variable(tf.truncated_normal(shape,stddev=0.1),name='W') #定义偏值 def bias(shape): return tf.Variable(tf.constant(0.1,shape=shape),name='b')#定义卷积操作#步长为2,即原尺寸长和宽各除2 def conv2d(x,W): return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME') def max_pool_2x2(x): return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
#输入层 with tf.name_scope('input_layer'): x = tf.placeholder('float',shape=[None,32,32,3],name='x')
#第一个卷基层 with tf.name_scope('conv_1'): W1 = weight([3,3,3,32]) b1 = bias([32]) conv_1 = conv2d(x,W1) + b1 conv_1 = tf.nn.relu(conv_1)
#第一个池化层 with tf.name_scope('pool_1'): pool_1 = max_pool_2x2(conv_1)
#第二个卷积层 with tf.name_scope('conv_2'): W2 = weight([3,3,32,64]) b2 = bias([64]) conv_2 = conv2d(pool_1,W2) + b2 conv_2 = tf.nn.relu(conv_2)
#第二个池化层 with tf.name_scope('pool_2'): pool_2 = max_pool_2x2(conv_2)
#全连层 with tf.name_scope('fc'): W3 = weight([4096,128]) b3 = bias([128]) flat = tf.reshape(pool_2,[-1,4096]) h = tf.nn.relu(tf.matmul(flat,W3)+b3) h_dropout = tf.nn.dropout(h,keep_prob=0.8)
#输出层 with tf.name_scope('output_layer'): W4 = weight([128,10]) b4 = bias([10]) pred = tf.nn.softmax(tf.matmul(h_dropout,W4)+b4)
#构建模型 with tf.name_scope('optimizer'): #定义占位符 y = tf.placeholder('float',shape=[None,10],name='label') #定义损失函数 loss_function = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred,labels=y)) #选择优化器 optimizer = tf.train.AdamOptimizer(learning_rate = 0.0001).minimize(loss_function)
#定义准确率 with tf.name_scope('evaluation'): correct_prediction = tf.equal(tf.argmax(pred,1),tf.argmax(y,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction,'float'))
#启动回话import osfrom time import timetrain_epochs = 25batch_size = 50total_batch = int(len(Xtrain)/batch_size)epoch_list = []accuracy_list = []loss_list = []epoch = tf.Variable(0,name='epoch',trainable=False)startTime = time()sess = tf.Session()init = tf.global_variables_initializer()sess.run(init)
#设置检查点存储目录ckpt_dir = "CIFAR10_log/"if not os.path.exists(ckpt_dir): os.makedirs(ckpt_dir)#生成saversaver = tf.train.Saver(max_to_keep = 1)#如果有检查点文件,读取最新的检查点文件,恢复各种变量值ckpt = tf.train.latest_checkpoint(ckpt_dir)if ckpt != None: saver.restore(sess,ckpt)else: print("Traing frome scrath")#获取续训参数start = sess.run(epoch)print("Training starts from {} epoch".format(start+1))
Traing frome scrathTraining starts from 1 epoch
#迭代训练 def get_train_batch(number, batch_size): return Xtrain_normalize[number*batch_size:(number+1)*batch_size], Ytrain_onehot[number*batch_size:(number+1)*batch_size] for ep in range(start, train_epochs): for i in range(total_batch): batch_x, batch_y = get_train_batch(i,batch_size) sess.run(optimizer,feed_dict={x: batch_x, y: batch_y}) if i %100 == 0: print("Step {}".format(i), "finished") loss,acc = sess.run([loss_function,accuracy],feed_dict={x: batch_x, y: batch_y}) epoch_list.append(ep+1) loss_list.append(loss) accuracy_list.append(acc) print("Train epoch:" ,"%02d"%(sess.run(epoch)+1),"Loss=","{:.6f}".format(loss)," Accuracy=",acc)#保存检查点 saver.save(sess,ckpt_dir+"CIFAR10_cnn_model.cpkt",global_step=ep+1) sess.run(epoch.assign(ep+1)) duration =time()-startTime print("Train finished takes:" ,duration)
Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 01 Loss= 2.163664 Accuracy= 0.3 Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 02 Loss= 2.159031 Accuracy= 0.3 Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 03 Loss= 2.152160 Accuracy= 0.3 Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 04 Loss= 2.146002 Accuracy= 0.32 Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 05 Loss= 2.118356 Accuracy= 0.36 Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 06 Loss= 2.115577 Accuracy= 0.36 Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 07 Loss= 2.079697 Accuracy= 0.38 Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 08 Loss= 2.072792 Accuracy= 0.4 Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 09 Loss= 2.049045 Accuracy= 0.42 Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 10 Loss= 2.041191 Accuracy= 0.42 Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 11 Loss= 2.026438 Accuracy= 0.42 Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 12 Loss= 2.034380 Accuracy= 0.46 Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 13 Loss= 2.002859 Accuracy= 0.46 Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 14 Loss= 1.988719 Accuracy= 0.46 Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 15 Loss= 1.987236 Accuracy= 0.5 Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 16 Loss= 1.974100 Accuracy= 0.48 Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 17 Loss= 2.021529 Accuracy= 0.42 Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 18 Loss= 1.996164 Accuracy= 0.44 Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 19 Loss= 1.993273 Accuracy= 0.46 Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 20 Loss= 2.001982 Accuracy= 0.46 Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 21 Loss= 1.966713 Accuracy= 0.5 Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 22 Loss= 1.938897 Accuracy= 0.52 Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 23 Loss= 1.928690 Accuracy= 0.54 Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 24 Loss= 1.972906 Accuracy= 0.48 Step 0 finished Step 100 finished Step 200 finished Step 300 finished Step 400 finished Step 500 finished Step 600 finished Step 700 finished Step 800 finished Step 900 finished Train epoch: 25 Loss= 1.931121 Accuracy= 0.54 Train finished takes: 4361.418999910355
%matplotlib inline
fig = plt.gcf() fig.set_size_inches(4,2) plt.plot(epoch_list,loss_list,label='loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['loss'],loc='upper right')
<matplotlib.legend.Legend at 0xefe2160>
[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-oRkAUTzN-1620811936106)(output_25_1.png)]
#可视化准确率 plt.plot(epoch_list,accuracy_list,label="accuracy") fig = plt.gcf() fig.set_size_inches(4,2) plt.ylim(0.1,1) plt.ylabel('accuracy') plt.xlabel('epoch') plt.legend() plt.show()
[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-91jfDBEv-1620811936114)(output_26_0.png)]
#评估模型及预测 test_total_batch = int(len(Xtest_normalize)/batch_size) test_acc_sum = 0.0 for i in range(test_total_batch): test_image_batch = Xtest_normalize[i*batch_size:(i+1)*batch_size] test_label_batch = Ytest_onehot[i*batch_size:(i+1)*batch_size] test_batch_acc = sess.run(accuracy,feed_dict = {x:test_image_batch,y:test_label_batch}) test_acc_sum += test_batch_acc test_acc = float(test_acc_sum/test_total_batch) print("Test accuracy:{:.6f}".format(test_acc))
Test accuracy:0.613900
test_pred = sess.run(pred,feed_dict={x:Xtest_normalize[:10]}) prediction_result = sess.run(tf.argmax(test_pred,1))
plot_images_labels_prediction(Xtest,Ytest,prediction_result,0,10)
这篇关于CNN图像识别的文章就介绍到这儿,希望我们推荐的文章对大家有所帮助,也希望大家多多支持为之网!
- 2024-11-23增量更新怎么做?-icode9专业技术文章分享
- 2024-11-23压缩包加密方案有哪些?-icode9专业技术文章分享
- 2024-11-23用shell怎么写一个开机时自动同步远程仓库的代码?-icode9专业技术文章分享
- 2024-11-23webman可以同步自己的仓库吗?-icode9专业技术文章分享
- 2024-11-23在 Webman 中怎么判断是否有某命令进程正在运行?-icode9专业技术文章分享
- 2024-11-23如何重置new Swiper?-icode9专业技术文章分享
- 2024-11-23oss直传有什么好处?-icode9专业技术文章分享
- 2024-11-23如何将oss直传封装成一个组件在其他页面调用时都可以使用?-icode9专业技术文章分享
- 2024-11-23怎么使用laravel 11在代码里获取路由列表?-icode9专业技术文章分享
- 2024-11-22怎么实现ansible playbook 备份代码中命名包含时间戳功能?-icode9专业技术文章分享