利用卷積神經(jīng)網(wǎng)絡(luò)訓(xùn)練圖像數(shù)據(jù)分為以下幾個(gè)步驟
1.讀取圖片文件
2.產(chǎn)生用于訓(xùn)練的批次
3.定義訓(xùn)練的模型（包括初始化參數(shù)，卷積、池化層等參數(shù)、網(wǎng)絡(luò)）
4.訓(xùn)練
1 讀取圖片文件
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
這里文件名作為標(biāo)簽，即類別（其數(shù)據(jù)類型要確定，后面要轉(zhuǎn)為tensor類型數(shù)據(jù)）。
然后將image和label轉(zhuǎn)為list格式數(shù)據(jù)，因?yàn)楹筮呌玫降牡囊恍?a href="/news/dongtai/150833.html">tensorflow函數(shù)接收的是list格式數(shù)據(jù)。
2 產(chǎn)生用于訓(xùn)練的批次
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轉(zhuǎn)化為tensorflow數(shù)據(jù)格式，使用tf.train.slice_input_producer實(shí)現(xiàn)一個(gè)輸入的隊(duì)列。
label不需要處理，image存儲(chǔ)的是路徑，需要讀取為圖片，接下來的幾步就是讀取路徑轉(zhuǎn)為圖片，用于訓(xùn)練。
CNN對(duì)圖像大小是敏感的，第10行圖片resize處理為大小一致，12行將其標(biāo)準(zhǔn)化，即減去所有圖片的均值，方便訓(xùn)練。
接下來使用tf.train.batch函數(shù)產(chǎn)生訓(xùn)練的批次。
最后將產(chǎn)生的批次做數(shù)據(jù)類型的轉(zhuǎn)換和shape的處理即可產(chǎn)生用于訓(xùn)練的批次。
3 定義訓(xùn)練的模型
（1）訓(xùn)練參數(shù)的定義及初始化
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的每層是y=wx+b的決策模型，卷積層產(chǎn)生特征向量，根據(jù)這些特征向量帶入x進(jìn)行計(jì)算，因此，需要定義卷積層的初始化參數(shù)，包括權(quán)重和偏置。其中第8行的參數(shù)形狀后邊再解釋。
（2）定義不同層的操作
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)
這里只定義了三種層，即卷積層、池化層和正則化層
（3）定義訓(xùn)練模型
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
模型比較簡(jiǎn)單，使用三層卷積，第11行使用全連接，需要對(duì)特征向量進(jìn)行reshape，其中l(wèi)6的形狀為[-1，w4的第1維的參數(shù)]，因此，將其按照“w4”reshape的時(shí)候，要使得-1位置的大小為batch_size，這樣，最終再乘以“wo”時(shí)，最終的輸出大小為[batch_size,class_num]
（4）定義評(píng)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 首先定義損失函數(shù)，這是用于訓(xùn)練最小化損失的必需量 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
評(píng)價(jià)分類準(zhǔn)確率的量，訓(xùn)練時(shí)，需要loss值減小，準(zhǔn)確率增加，這樣的訓(xùn)練才是收斂的。
（5）定義訓(xùn)練方式
def training(loss,lr): train_op = tf.train.RMSPropOptimizer(lr,0.9).minimize(loss) return train_op
有很多種訓(xùn)練方式，可以自行去官網(wǎng)查看，但是不同的訓(xùn)練方式可能對(duì)應(yīng)前面的參數(shù)定義不一樣，需要另行處理，否則可能報(bào)錯(cuò)。
4 訓(xùn)練
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()神經(jīng)網(wǎng)絡(luò)訓(xùn)練的時(shí)候，我們需要將模型保存下來，方便后面繼續(xù)訓(xùn)練或者用訓(xùn)練好的模型進(jìn)行測(cè)試。因此，我們需要?jiǎng)?chuàng)建一個(gè)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()訓(xùn)練好的模型信息會(huì)記錄在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"
其余還會(huì)生成一些文件，分別記錄了模型參數(shù)等信息，后邊測(cè)試的時(shí)候程序會(huì)讀取checkpoint文件去加載這些真正的數(shù)據(jù)文件
構(gòu)建好神經(jīng)網(wǎng)絡(luò)進(jìn)行訓(xùn)練完成后，如果用之前的代碼直接進(jìn)行測(cè)試，會(huì)報(bào)shape不符合的錯(cuò)誤，大致是卷積層的輸入與圖像的shape不一致，這是因?yàn)樯掀拇a，將weights和biases定義在了模型的外面，調(diào)用模型的時(shí)候，出現(xiàn)valueError的錯(cuò)誤。
因此，我們需要將參數(shù)定義在模型里面，加載訓(xùn)練好的模型參數(shù)時(shí)，訓(xùn)練好的參數(shù)才能夠真正初始化模型。重寫模型函數(shù)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測(cè)試訓(xùn)練好的模型
首先獲取一張測(cè)試圖像
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
加載模型，計(jì)算測(cè)試結(jié)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)前面主要是將測(cè)試圖片標(biāo)準(zhǔn)化為網(wǎng)絡(luò)的輸入圖像，15-19是加載模型文件，然后將圖像輸入到模型里即可
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