详解tensorflow训练自己的数据集实现CNN图像分类

技术  /  管理员 发布于 7年前   139

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 weightsweights = { "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 biasesbiases = { "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

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