详解tensorflow训练自己的数据集实现CNN图像分类_tensorflow1.x训练自己的数据集resnet图像分类

     出处： https://www.jb51.net/article/134623.htm

利用卷积神经网络训练图像数据分为以下几个步骤

1.读取图片文件
2.产生用于训练的批次
3.定义训练的模型（包括初始化参数，卷积、池化层等参数、网络）

4.训练

1 读取图片文件

def get_files(filename):
 class_train = []
 label_train = []
 for train_class in os.listdir(filename):
  for pic in os.listdir(filename+train_class):
   class_train.append(filename+train_class+'/'+pic)
   label_train.append(train_class)
 temp = np.array([class_train,label_train])
 temp = temp.transpose()
 #shuffle the samples
 np.random.shuffle(temp)
 #after transpose, images is in dimension 0 and label in dimension 1
 image_list = list(temp[:,0])
 label_list = list(temp[:,1])
 label_list = [int(i) for i in label_list]
 #print(label_list)
 return image_list,label_list

这里文件名作为标签，即类别（其数据类型要确定，后面要转为tensor类型数据）。

然后将image和label转为list格式数据，因为后边用到的的一些tensorflow函数接收的是list格式数据。

2 产生用于训练的批次

def get_batches(image,label,resize_w,resize_h,batch_size,capacity):
 #convert the list of images and labels to tensor
 image = tf.cast(image,tf.string)
 label = tf.cast(label,tf.int64)
 queue = tf.train.slice_input_producer([image,label])
 label = queue[1]
 image_c = tf.read_file(queue[0])
 image = tf.image.decode_jpeg(image_c,channels = 3)
 #resize
 image = tf.image.resize_image_with_crop_or_pad(image,resize_w,resize_h)
 #(x - mean) / adjusted_stddev
 image = tf.image.per_image_standardization(image)
  
 image_batch,label_batch = tf.train.batch([image,label],
            batch_size = batch_size,
            num_threads = 64,
            capacity = capacity)
 images_batch = tf.cast(image_batch,tf.float32)
 labels_batch = tf.reshape(label_batch,[batch_size])
 return images_batch,labels_batch

首先使用tf.cast转化为tensorflow数据格式，使用tf.train.slice_input_producer实现一个输入的队列。

label不需要处理，image存储的是路径，需要读取为图片，接下来的几步就是读取路径转为图片，用于训练。

CNN对图像大小是敏感的，第10行图片resize处理为大小一致，12行将其标准化，即减去所有图片的均值，方便训练。

接下来使用tf.train.batch函数产生训练的批次。

最后将产生的批次做数据类型的转换和shape的处理即可产生用于训练的批次。

3 定义训练的模型

（1）训练参数的定义及初始化

def init_weights(shape):
 return tf.Variable(tf.random_normal(shape,stddev = 0.01))
#init weights
weights = {
 "w1":init_weights([3,3,3,16]),
 "w2":init_weights([3,3,16,128]),
 "w3":init_weights([3,3,128,256]),
 "w4":init_weights([4096,4096]),
 "wo":init_weights([4096,2])
 }
 
#init biases
biases = {
 "b1":init_weights([16]),
 "b2":init_weights([128]),
 "b3":init_weights([256]),
 "b4":init_weights([4096]),
 "bo":init_weights([2])
 }

CNN的每层是y=wx+b的决策模型，卷积层产生特征向量，根据这些特征向量带入x进行计算，因此，需要定义卷积层的初始化参数，包括权重和偏置。其中第8行的参数形状后边再解释。

（2）定义不同层的操作

def conv2d(x,w,b):
 x = tf.nn.conv2d(x,w,strides = [1,1,1,1],padding = "SAME")
 x = tf.nn.bias_add(x,b)
 return tf.nn.relu(x)
 
def pooling(x):
 return tf.nn.max_pool(x,ksize = [1,2,2,1],strides = [1,2,2,1],padding = "SAME")
 
def norm(x,lsize = 4):
 return tf.nn.lrn(x,depth_radius = lsize,bias = 1,alpha = 0.001/9.0,beta = 0.75)

这里只定义了三种层，即卷积层、池化层和正则化层

（3）定义训练模型

def mmodel(images):
 l1 = conv2d(images,weights["w1"],biases["b1"])
 l2 = pooling(l1)
 l2 = norm(l2)
 l3 = conv2d(l2,weights["w2"],biases["b2"])
 l4 = pooling(l3)
 l4 = norm(l4)
 l5 = conv2d(l4,weights["w3"],biases["b3"])
 #same as the batch size
 l6 = pooling(l5)
 l6 = tf.reshape(l6,[-1,weights["w4"].get_shape().as_list()[0]])
 l7 = tf.nn.relu(tf.matmul(l6,weights["w4"])+biases["b4"])
 soft_max = tf.add(tf.matmul(l7,weights["wo"]),biases["bo"])
 return soft_max

模型比较简单，使用三层卷积，第11行使用全连接，需要对特征向量进行reshape，其中l6的形状为[-1，w4的第1维的参数]，因此，将其按照“w4”reshape的时候，要使得-1位置的大小为batch_size，这样，最终再乘以“wo”时，最终的输出大小为[batch_size,class_num]

（4）定义评估量

def loss(logits,label_batches):
 cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,labels=label_batches)
 cost = tf.reduce_mean(cross_entropy)
 return cost
 
　首先定义损失函数，这是用于训练最小化损失的必需量
def get_accuracy(logits,labels):
 acc = tf.nn.in_top_k(logits,labels,1)
 acc = tf.cast(acc,tf.float32)
 acc = tf.reduce_mean(acc)
 return acc

评价分类准确率的量，训练时，需要loss值减小，准确率增加，这样的训练才是收敛的。

（5）定义训练方式

def training(loss,lr):
 train_op = tf.train.RMSPropOptimizer(lr,0.9).minimize(loss)
 return train_op

有很多种训练方式，可以自行去官网查看，但是不同的训练方式可能对应前面的参数定义不一样，需要另行处理，否则可能报错。

 4 训练

def run_training():
 data_dir = 'C:/Users/wk/Desktop/bky/dataSet/'
 image,label = inputData.get_files(data_dir)
 image_batches,label_batches = inputData.get_batches(image,label,32,32,16,20)
 p = model.mmodel(image_batches)
 cost = model.loss(p,label_batches)
 train_op = model.training(cost,0.001)
 acc = model.get_accuracy(p,label_batches)
  
 sess = tf.Session()
 init = tf.global_variables_initializer()
 sess.run(init)
  
 coord = tf.train.Coordinator()
 threads = tf.train.start_queue_runners(sess = sess,coord = coord)
  
 try:
  for step in np.arange(1000):
   print(step)
   if coord.should_stop():
    break
   _,train_acc,train_loss = sess.run([train_op,acc,cost])
   print("loss:{} accuracy:{}".format(train_loss,train_acc))
 except tf.errors.OutOfRangeError:
  print("Done!!!")
 finally:
  coord.request_stop()
 coord.join(threads)
 sess.close()

神经网络训练的时候，我们需要将模型保存下来，方便后面继续训练或者用训练好的模型进行测试。因此，我们需要创建一个saver保存模型。

def run_training():
 data_dir = 'C:/Users/wk/Desktop/bky/dataSet/'
 log_dir = 'C:/Users/wk/Desktop/bky/log/'
 image,label = inputData.get_files(data_dir)
 image_batches,label_batches = inputData.get_batches(image,label,32,32,16,20)
 print(image_batches.shape)
 p = model.mmodel(image_batches,16)
 cost = model.loss(p,label_batches)
 train_op = model.training(cost,0.001)
 acc = model.get_accuracy(p,label_batches)
  
 sess = tf.Session()
 init = tf.global_variables_initializer()
 sess.run(init)
 saver = tf.train.Saver()
 coord = tf.train.Coordinator()
 threads = tf.train.start_queue_runners(sess = sess,coord = coord)
  
 try:
  for step in np.arange(1000):
   print(step)
   if coord.should_stop():
    break
   _,train_acc,train_loss = sess.run([train_op,acc,cost])
   print("loss:{} accuracy:{}".format(train_loss,train_acc))
   if step % 100 == 0:
    check = os.path.join(log_dir,"model.ckpt")
    saver.save(sess,check,global_step = step)
 except tf.errors.OutOfRangeError:
  print("Done!!!")
 finally:
  coord.request_stop()
 coord.join(threads)
 sess.close()

训练好的模型信息会记录在checkpoint文件中，大致如下： 

model_checkpoint_path: "C:/Users/wk/Desktop/bky/log/model.ckpt-100"
all_model_checkpoint_paths: "C:/Users/wk/Desktop/bky/log/model.ckpt-0"
all_model_checkpoint_paths: "C:/Users/wk/Desktop/bky/log/model.ckpt-100"

其余还会生成一些文件，分别记录了模型参数等信息，后边测试的时候程序会读取checkpoint文件去加载这些真正的数据文件

构建好神经网络进行训练完成后，如果用之前的代码直接进行测试，会报shape不符合的错误，大致是卷积层的输入与图像的shape不一致，这是因为上篇的代码，将weights和biases定义在了模型的外面，调用模型的时候，出现valueError的错误。

因此，我们需要将参数定义在模型里面，加载训练好的模型参数时，训练好的参数才能够真正初始化模型。重写模型函数如下

def mmodel(images,batch_size):
 with tf.variable_scope('conv1') as scope:
  weights = tf.get_variable('weights', 
         shape = [3,3,3, 16],
         dtype = tf.float32, 
         initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32))
  biases = tf.get_variable('biases', 
         shape=[16],
         dtype=tf.float32,
         initializer=tf.constant_initializer(0.1))
  conv = tf.nn.conv2d(images, weights, strides=[1,1,1,1], padding='SAME')
  pre_activation = tf.nn.bias_add(conv, biases)
  conv1 = tf.nn.relu(pre_activation, name= scope.name)
 with tf.variable_scope('pooling1_lrn') as scope:
  pool1 = tf.nn.max_pool(conv1, ksize=[1,2,2,1],strides=[1,2,2,1],
        padding='SAME', name='pooling1')
  norm1 = tf.nn.lrn(pool1, depth_radius=4, bias=1.0, alpha=0.001/9.0,
       beta=0.75,name='norm1')
 with tf.variable_scope('conv2') as scope:
  weights = tf.get_variable('weights',
         shape=[3,3,16,128],
         dtype=tf.float32,
         initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32))
  biases = tf.get_variable('biases',
         shape=[128], 
         dtype=tf.float32,
         initializer=tf.constant_initializer(0.1))
  conv = tf.nn.conv2d(norm1, weights, strides=[1,1,1,1],padding='SAME')
  pre_activation = tf.nn.bias_add(conv, biases)
  conv2 = tf.nn.relu(pre_activation, name='conv2') 
 with tf.variable_scope('pooling2_lrn') as scope:
  norm2 = tf.nn.lrn(conv2, depth_radius=4, bias=1.0, alpha=0.001/9.0,
       beta=0.75,name='norm2')
  pool2 = tf.nn.max_pool(norm2, ksize=[1,2,2,1], strides=[1,1,1,1],
        padding='SAME',name='pooling2')
 with tf.variable_scope('local3') as scope:
  reshape = tf.reshape(pool2, shape=[batch_size, -1])
  dim = reshape.get_shape()[1].value
  weights = tf.get_variable('weights',
         shape=[dim,4096],
         dtype=tf.float32,
         initializer=tf.truncated_normal_initializer(stddev=0.005,dtype=tf.float32))
  biases = tf.get_variable('biases',
         shape=[4096],
         dtype=tf.float32, 
         initializer=tf.constant_initializer(0.1))
  local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name) 
 with tf.variable_scope('softmax_linear') as scope:
  weights = tf.get_variable('softmax_linear',
         shape=[4096, 2],
         dtype=tf.float32,
         initializer=tf.truncated_normal_initializer(stddev=0.005,dtype=tf.float32))
  biases = tf.get_variable('biases', 
         shape=[2],
         dtype=tf.float32, 
         initializer=tf.constant_initializer(0.1))
  softmax_linear = tf.add(tf.matmul(local3, weights), biases, name='softmax_linear')
 return softmax_linear

测试训练好的模型

首先获取一张测试图像

def get_one_image(img_dir):
 image = Image.open(img_dir)
 plt.imshow(image)
 image = image.resize([32, 32])
 image_arr = np.array(image)
 return image_arr

加载模型，计算测试结果

def test(test_file):
 log_dir = 'C:/Users/wk/Desktop/bky/log/'
 image_arr = get_one_image(test_file)
  
 with tf.Graph().as_default():
  image = tf.cast(image_arr, tf.float32)
  image = tf.image.per_image_standardization(image)
  image = tf.reshape(image, [1,32, 32, 3])
  print(image.shape)
  p = model.mmodel(image,1)
  logits = tf.nn.softmax(p)
  x = tf.placeholder(tf.float32,shape = [32,32,3])
  saver = tf.train.Saver()
  with tf.Session() as sess:
   ckpt = tf.train.get_checkpoint_state(log_dir)
   if ckpt and ckpt.model_checkpoint_path:
    global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
    saver.restore(sess, ckpt.model_checkpoint_path)
    print('Loading success)
   else:
    print('No checkpoint')
   prediction = sess.run(logits, feed_dict={x: image_arr})
   max_index = np.argmax(prediction)
   print(max_index)

前面主要是将测试图片标准化为网络的输入图像，15-19是加载模型文件，然后将图像输入到模型里即可