新妇子求问怎么着零基础自学利用CNN举行图像分类

是因为专门的学问及课题须求上学使用卷积神经互联网的秘技对脑部MHighlanderI影象开展病灶分割,想咨询各位大神零基础的话需求上学如何课程?多谢

tensorflow跳级到1.0从此,扩大了一些尖端模块: 如tf.layers, tf.metrics,
和tf.losses,使得代码稍微有个别简化。

任务:花卉分类

版本:tensorflow 1.0

数据:flower-photos.rar)

花总共有五类,分别位居5个公文夹下。

闲聊非常少说,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()

如上正是本文的全体内容,希望对我们的读书抱有助于,也可望我们多都赐教脚本之家。

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