详解tensorflow训练自己的数据集实现CNN图像分类

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

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

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])  }

 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)

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

 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

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

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()

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()

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

 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

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)