CNN图像分类（实际项目，特殊训练集，95%准确率，数据代码百度云） |
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CNN图像分类（实际项目，特殊训练集，95%准确率，数据代码百度云）
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最近基于VGG-16缩进了网络做了一个CNN模型用于处理图像分类，实际项目，训练对象是448×32的长条试纸图片。 目录 项目源码百度云 tensorboard可视化展示 源代码 项目源码百度云 项目源码百度云链接：https://pan.baidu.com/s/1aWLeh4Kaft7NPlB0GxBZMg 提取码：vjhu 里面项目名字没改，VGG16因为是改造的，名字也没好好取，能用就行。。 数据文件格式如上右图，test1和train2为原始图片，test和train为处理后图片，统一为448×32大小，用于网络训练。每个下有lh1、lh2等为类别，每个类别下分别存放了图片 注：test/lh1,test1/lh1,train/lh1,train2/lh1下各有一张图片供参考，数据就不大量泄漏了，其余文件为空 VGG16_RAW.pyVGG16模型源文件VGG16_mini2.py改造的小型CNN模型，训练出来模型大概7M左右tf009_predition.py预测文件tf009.py训练文件image_pre_deal.py图像预处理文件，将原始图片转为统一大小格式的图片calculate_mean.py计算图片平均值的文件，用于后续减均值处理 tensorboard可视化展示tensorboard:（不要在意波折的细节，只要看清准确率，损失值数值就行了，采用的Mini-batch的迭代方式，这次训练有点乱）
模型：
tf009.py源码，训练主文件：（写的迁移学习不要介意，懒得改了。。） ''' VGG16迁移学习训练主函数 tensorboard --logdir=D:\python\vgg16\logs ''' import tensorflow as tf import os os.environ["CUDA_VISIBLE_DEVICES"]="-1" # 由于出现显卡内存不足问题，所以。。。 import numpy as np from time import time import vgg16.VGG16_mini2 as model def get_batch(image_list,label_list,img_width,img_height,batch_size,capacity):#通过读取列表来载入批量图片及标签 image = tf.cast(image_list,tf.string) label = tf.cast(label_list,tf.int32) input_queue = tf.train.slice_input_producer([image,label],shuffle=True) label = input_queue[1] image_contents = tf.read_file(input_queue[0]) image = tf.image.decode_jpeg(image_contents,channels=3) image = tf.cast(image,tf.float32) image -= [42.79902,42.79902,42.79902] # 减均值 # image = preprocess_for_train(image,img_height,img_width) image.set_shape((img_height,img_width,3)) image_batch,label_batch = tf.train.batch([image,label],batch_size=batch_size,num_threads=64,capacity=capacity) label_batch = tf.reshape(label_batch,[batch_size]) return image_batch,label_batch def get_file(file_dir): images = [] for root,sub_folders,files in os.walk(file_dir): for name in files: images.append(os.path.join(root,name)) labels = [] for label_name in images: letter = label_name.split("\\")[-2] if letter =="lh1":labels.append(0) elif letter =="lh2":labels.append(1) elif letter == "lh3":labels.append(2) elif letter == "lh4":labels.append(3) elif letter == "lh5":labels.append(4) elif letter == "lh6":labels.append(5) elif letter == "lh7": labels.append(6) print("check for get_file:",images[0],"label is ",labels[0]) #shuffle temp = np.array([images,labels]) temp = temp.transpose() np.random.shuffle(temp) image_list = list(temp[:,0]) label_list = list(temp[:,1]) label_list = [int(float(i)) for i in label_list] return image_list,label_list #标签格式重构 def onehot(labels): n_sample = len(labels) n_class = 7 # max(labels) + 1 onehot_labels = np.zeros((n_sample,n_class)) onehot_labels[np.arange(n_sample),labels] = 1 return onehot_labels if __name__ == '__main__': startTime =time() batch_size = 8 record_epoch = 70000/batch_size small_loop = int(7000/batch_size) capacity = 256 # 内存中存储的最大数据容量 pic_height,pic_width = 32,448 # 修改图片大小参数，应当为32的倍数！不然会导致错误 xs,ys = get_file('./data/train')#获取图像列表与标签列表 image_batch,label_batch = get_batch(xs,ys,img_width=pic_width,img_height=pic_height,batch_size=batch_size,capacity=capacity) # 验证集 xs_val,ys_val = get_file('./data/test')#获取图像列表与标签列表 image_val_batch,label_val_batch = get_batch(xs_val,ys_val,img_width=pic_width,img_height=pic_height,batch_size=455,capacity=capacity) x = tf.placeholder(tf.float32,[None,pic_height,pic_width,3]) y = tf.placeholder(tf.int32,[None,7])#7分类 vgg = model.vgg16(x) fc8_fineuining = vgg.probs #即softmax(fc8) prediction_out = tf.argmax(fc8_fineuining,1) real_out = tf.argmax(y,1) correct_prediction = tf.equal(prediction_out,real_out)#检查预测类与实际类别是否匹配 accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))#准确率 loss_function = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=fc8_fineuining,labels=y))#损失函数 optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(loss_function) sess = tf.Session() sess.run(tf.global_variables_initializer()) # vgg.load_weights('vgg16_weights.npz',sess) saver = tf.train.Saver() # # 断点续训 # ckpt_dir = "./model/" # ckpt = tf.train.latest_checkpoint(ckpt_dir) # if ckpt != None: # saver.restore(sess, ckpt) # print('saver restore finish') # else: # print("training from scratch") #启动线程 coord = tf.train.Coordinator()#使用协调器管理线程 threads = tf.train.start_queue_runners(coord=coord,sess=sess) # 日志记录 summary_writer = tf.summary.FileWriter('./logs/', graph=sess.graph, flush_secs=15) summary_writer2 = tf.summary.FileWriter('./logs/plot2/', flush_secs=15) tf.summary.scalar(name='loss_func', tensor=loss_function) tf.summary.scalar(name='accuracy', tensor=accuracy) merged_summary_op = tf.summary.merge_all() epoch_start_time = time() # 采用Mini-batch迭代 step = 0 epoch = 10000 for i in range(epoch): for j in range(small_loop): images,labels = sess.run([image_batch,label_batch]) labels = onehot(labels) # # 可视化 # plt.subplot(221) # plt.imshow(images[0, :, :, 0]) # plt.show() # print(1) sess.run(optimizer,feed_dict={x:images,y:labels}) merged_summary,loss,real_train_out = sess.run([merged_summary_op,loss_function,real_out],feed_dict={x:images,y:labels}) summary_writer.add_summary(merged_summary, global_step=step) # print(i,j,"to see train data:",real_train_out[:10]) step += 1 images_val, labels_val = sess.run([image_val_batch, label_val_batch]) labels_val = onehot(labels_val) merged_summary_val, loss_val,accuracy_val,prediction_val_out,real_val_out = sess.run([merged_summary_op, loss_function,accuracy,prediction_out,real_out], feed_dict={x: images_val, y: labels_val}) summary_writer2.add_summary(merged_summary_val, global_step=step) # 输出每个类别正确率 lh1_right, lh2_right, lh3_right, lh4_right, lh5_right, lh6_right, lh7_right = 0, 0, 0, 0, 0, 0, 0 lh1_wrong, lh2_wrong, lh3_wrong, lh4_wrong, lh5_wrong, lh6_wrong, lh7_wrong = 0, 0, 0, 0, 0, 0, 0 for ii in range(len(prediction_val_out)): if prediction_val_out[ii] == real_val_out[ii]: if real_val_out[ii] == 0:lh1_right+=1 elif real_val_out[ii] == 1:lh2_right+=1 elif real_val_out[ii] == 2:lh3_right += 1 elif real_val_out[ii] == 3:lh4_right += 1 elif real_val_out[ii] == 4:lh5_right += 1 elif real_val_out[ii] == 5:lh6_right += 1 elif real_val_out[ii] == 6:lh7_right += 1 else: if real_val_out[ii] == 0:lh1_wrong+=1 elif real_val_out[ii] == 1:lh2_wrong+=1 elif real_val_out[ii] == 2:lh3_wrong += 1 elif real_val_out[ii] == 3:lh4_wrong += 1 elif real_val_out[ii] == 4:lh5_wrong += 1 elif real_val_out[ii] == 5:lh6_wrong += 1 elif real_val_out[ii] == 6:lh7_wrong += 1 print(i,"correct rate :",((lh1_right)/(lh1_right+lh1_wrong)),((lh2_right)/(lh2_right+lh2_wrong)),((lh3_right)/(lh3_right+lh3_wrong)),((lh4_right)/(lh4_right+lh4_wrong)),((lh5_right)/(lh5_right+lh5_wrong)),((lh6_right)/(lh6_right+lh6_wrong)),((lh7_right)/(lh7_right+lh7_wrong))) # print(i,"nums:",((lh1_right+lh1_wrong)),(lh2_right+lh2_wrong),((lh3_right+lh3_wrong)),((lh4_right+lh4_wrong)),(lh5_right+lh5_wrong),((lh6_right+lh6_wrong)),((lh7_right+lh7_wrong))) print(i,"epoch's accuracy:",accuracy_val) print(i," loss is %f"%loss,"val loss is %f"%loss_val) epoch_end_time =time() print(i," epoch takes:",(epoch_end_time-epoch_start_time)) epoch_start_time = epoch_end_time if i % 1 == 0 and i != 0: saver.save(sess,os.path.join("./model/",'epoch{:06d}.ckpt'.format(i))) print("------------model saved") # print("-------------Epoch %d is finished"%i) summary_writer.close() saver.save(sess,"./model/") print("optimization finished") duration = time() - startTime print("train takes:","{:.2f}".format(duration)) coord.request_stop()#通知线程关闭 coord.join(threads)#等其他线程关闭这一函数才返回
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