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。