tensorflow 1.0用CNN进行图像分类
tensorflow升级到1.0之后,增加了一些高级模块:如tf.layers,tf.metrics,和tf.losses,使得代码稍微有些简化。
任务:花卉分类
版本:tensorflow1.0
数据:flower-photos
花总共有五类,分别放在5个文件夹下。
闲话不多说,直接上代码,希望大家能看懂:)
复制代码
#-*-coding:utf-8-*- fromskimageimportio,transform importglob importos importtensorflowastf importnumpyasnp importtime path='e:/flower/' #将所有的图片resize成100*100 w=100 h=100 c=3 #读取图片 defread_img(path): cate=[path+xforxinos.listdir(path)ifos.path.isdir(path+x)] imgs=[] labels=[] foridx,folderinenumerate(cate): foriminglob.glob(folder+'/*.jpg'): print('readingtheimages:%s'%(im)) img=io.imread(im) img=transform.resize(img,(w,h)) imgs.append(img) labels.append(idx) returnnp.asarray(imgs,np.float32),np.asarray(labels,np.int32) data,label=read_img(path) #打乱顺序 num_example=data.shape[0] arr=np.arange(num_example) np.random.shuffle(arr) data=data[arr] label=label[arr] #将所有数据分为训练集和验证集 ratio=0.8 s=np.int(num_example*ratio) x_train=data[:s] y_train=label[:s] x_val=data[s:] y_val=label[s:] #-----------------构建网络---------------------- #占位符 x=tf.placeholder(tf.float32,shape=[None,w,h,c],name='x') y_=tf.placeholder(tf.int32,shape=[None,],name='y_') #第一个卷积层(100——>50) conv1=tf.layers.conv2d( inputs=x, filters=32, kernel_size=[5,5], padding="same", activation=tf.nn.relu, kernel_initializer=tf.truncated_normal_initializer(stddev=0.01)) pool1=tf.layers.max_pooling2d(inputs=conv1,pool_size=[2,2],strides=2) #第二个卷积层(50->25) conv2=tf.layers.conv2d( inputs=pool1, filters=64, kernel_size=[5,5], padding="same", activation=tf.nn.relu, kernel_initializer=tf.truncated_normal_initializer(stddev=0.01)) pool2=tf.layers.max_pooling2d(inputs=conv2,pool_size=[2,2],strides=2) #第三个卷积层(25->12) conv3=tf.layers.conv2d( inputs=pool2, filters=128, kernel_size=[3,3], padding="same", activation=tf.nn.relu, kernel_initializer=tf.truncated_normal_initializer(stddev=0.01)) pool3=tf.layers.max_pooling2d(inputs=conv3,pool_size=[2,2],strides=2) #第四个卷积层(12->6) conv4=tf.layers.conv2d( inputs=pool3, filters=128, kernel_size=[3,3], padding="same", activation=tf.nn.relu, kernel_initializer=tf.truncated_normal_initializer(stddev=0.01)) pool4=tf.layers.max_pooling2d(inputs=conv4,pool_size=[2,2],strides=2) re1=tf.reshape(pool4,[-1,6*6*128]) #全连接层 dense1=tf.layers.dense(inputs=re1, units=1024, activation=tf.nn.relu, kernel_initializer=tf.truncated_normal_initializer(stddev=0.01), kernel_regularizer=tf.contrib.layers.l2_regularizer(0.003)) dense2=tf.layers.dense(inputs=dense1, units=512, activation=tf.nn.relu, kernel_initializer=tf.truncated_normal_initializer(stddev=0.01), kernel_regularizer=tf.contrib.layers.l2_regularizer(0.003)) logits=tf.layers.dense(inputs=dense2, units=5, activation=None, kernel_initializer=tf.truncated_normal_initializer(stddev=0.01), kernel_regularizer=tf.contrib.layers.l2_regularizer(0.003)) #---------------------------网络结束--------------------------- loss=tf.losses.sparse_softmax_cross_entropy(labels=y_,logits=logits) train_op=tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss) correct_prediction=tf.equal(tf.cast(tf.argmax(logits,1),tf.int32),y_) acc=tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) #定义一个函数,按批次取数据 defminibatches(inputs=None,targets=None,batch_size=None,shuffle=False): assertlen(inputs)==len(targets) ifshuffle: indices=np.arange(len(inputs)) np.random.shuffle(indices) forstart_idxinrange(0,len(inputs)-batch_size+1,batch_size): ifshuffle: excerpt=indices[start_idx:start_idx+batch_size] else: excerpt=slice(start_idx,start_idx+batch_size) yieldinputs[excerpt],targets[excerpt] #训练和测试数据,可将n_epoch设置更大一些 n_epoch=10 batch_size=64 sess=tf.InteractiveSession() sess.run(tf.global_variables_initializer()) forepochinrange(n_epoch): start_time=time.time() #training train_loss,train_acc,n_batch=0,0,0 forx_train_a,y_train_ainminibatches(x_train,y_train,batch_size,shuffle=True): _,err,ac=sess.run([train_op,loss,acc],feed_dict={x:x_train_a,y_:y_train_a}) train_loss+=err;train_acc+=ac;n_batch+=1 print("trainloss:%f"%(train_loss/n_batch)) print("trainacc:%f"%(train_acc/n_batch)) #validation val_loss,val_acc,n_batch=0,0,0 forx_val_a,y_val_ainminibatches(x_val,y_val,batch_size,shuffle=False): err,ac=sess.run([loss,acc],feed_dict={x:x_val_a,y_:y_val_a}) val_loss+=err;val_acc+=ac;n_batch+=1 print("validationloss:%f"%(val_loss/n_batch)) print("validationacc:%f"%(val_acc/n_batch)) sess.close()
以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持毛票票。