详解tensorflow训练自己的数据集实现CNN图像分类

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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

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