tensorflow 1.0用CNN进行图像分类

转载  更新时间:2018年04月15日 09:27:42   作者:denny402   我要评论

这篇文章主要为大家详细介绍了tensorflow 1.0用CNN进行图像分类,具有一定的参考价值,感兴趣的小伙伴们可以参考一下

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:tensorflow 1.0

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 -*-

from skimage import io,transform
import glob
import os
import tensorflow as tf
import numpy as np
import time

path='e:/flower/'

#将所有的图片resize成100*100
w=100
h=100
c=3


#读取图片
def read_img(path):
 cate=[path+x for x in os.listdir(path) if os.path.isdir(path+x)]
 imgs=[]
 labels=[]
 for idx,folder in enumerate(cate):
  for im in glob.glob(folder+'/*.jpg'):
   print('reading the images:%s'%(im))
   img=io.imread(im)
   img=transform.resize(img,(w,h))
   imgs.append(img)
   labels.append(idx)
 return np.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
def minibatches(inputs=None, targets=None, batch_size=None, shuffle=False):
 assert len(inputs) == len(targets)
 if shuffle:
  indices = np.arange(len(inputs))
  np.random.shuffle(indices)
 for start_idx in range(0, len(inputs) - batch_size + 1, batch_size):
  if shuffle:
   excerpt = indices[start_idx:start_idx + batch_size]
  else:
   excerpt = slice(start_idx, start_idx + batch_size)
  yield inputs[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())
for epoch in range(n_epoch):
 start_time = time.time()
 
 #training
 train_loss, train_acc, n_batch = 0, 0, 0
 for x_train_a, y_train_a in minibatches(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(" train loss: %f" % (train_loss/ n_batch))
 print(" train acc: %f" % (train_acc/ n_batch))
 
 #validation
 val_loss, val_acc, n_batch = 0, 0, 0
 for x_val_a, y_val_a in minibatches(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(" validation loss: %f" % (val_loss/ n_batch))
 print(" validation acc: %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脚本之家。

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