Tensorflow学习教程------实现lenet并且进行二分类

#coding:utf-8import tensorflow as tfimport osdef read_and_decode(filename): ???#根据文件名生成一个队列 ???filename_queue = tf.train.string_input_producer([filename]) ???reader = tf.TFRecordReader() ???_, serialized_example = reader.read(filename_queue) ??#返回文件名和文件 ???features = tf.parse_single_example(serialized_example, ??????????????????????????????????????features={ ??????????????????????????????????????????‘label‘: tf.FixedLenFeature([], tf.int64), ??????????????????????????????????????????‘img_raw‘ : tf.FixedLenFeature([], tf.string), ??????????????????????????????????????}) ???img = tf.decode_raw(features[‘img_raw‘], tf.uint8) ???img = tf.reshape(img, [227, 227, 3]) ???img = (tf.cast(img, tf.float32) * (1. / 255) - 0.5)*2 ???label = tf.cast(features[‘label‘], tf.int32) ???print img,label ???return img, label ???def get_batch(image, label, batch_size,crop_size): ?????#数据扩充变换 ?????distorted_image = tf.random_crop(image, [crop_size, crop_size, 3])#随机裁剪 ?????distorted_image = tf.image.random_flip_up_down(distorted_image)#上下随机翻转 ?????distorted_image = tf.image.random_brightness(distorted_image,max_delta=63)#亮度变化 ?????distorted_image = tf.image.random_contrast(distorted_image,lower=0.2, upper=1.8)#对比度变化 ???????#生成batch ?????#shuffle_batch的参数：capacity用于定义shuttle的范围，如果是对整个训练数据集，获取batch，那么capacity就应该够大 ?????#保证数据打的足够乱 ??????images, label_batch = tf.train.shuffle_batch([distorted_image, label],batch_size=batch_size, ??????????????????????????????????????????????????num_threads=1,capacity=2000,min_after_dequeue=1000) ????return images, label_batch ??????class network(object): ????#构造函数初始化 卷积层 全连接层 ?def lenet(self,images,keep_prob): ???????‘‘‘ ???????根据tensorflow中的conv2d函数，我们先定义几个基本符号 ???????输入矩阵 W×W，这里只考虑输入宽高相等的情况，如果不相等，推导方法一样，不多解释。 ???????filter矩阵 F×F，卷积核 ????????stride值 S，步长 ???????输出宽高为 new_height、new_width ???????在Tensorflow中对padding定义了两种取值：VALID、SAME。下面分别就这两种定义进行解释说明。 ???????VALID ???????new_height = new_width = (W – F + 1) / S ?#结果向上取整 ???????SAME ???????new_height = new_width = W / S ???#结果向上取整 ???????‘‘‘ ???????????????images = tf.reshape(images,shape=[-1,28,28,3]) ???????#images = (tf.cast(images,tf.float32)/255.0-0.5)*2 ???????#第一层，卷积层 39,39,3--->5,5,3,32--->39,39,32 ???????#卷积核大小为5*5 输入层深度为3即三通道图像 卷积核深度为32即卷积核的个数 ???????conv1_weights = tf.get_variable("conv1_weights",[5,5,3,32],initializer = tf.truncated_normal_initializer(stddev=0.1)) ???????conv1_biases = tf.get_variable("conv1_biases",[32],initializer = tf.constant_initializer(0.0)) ???????#移动步长为1 使用全0填充 ???????conv1 = tf.nn.conv2d(images,conv1_weights,strides=[1,1,1,1],padding=‘SAME‘) ???????#激活函数Relu去线性化 ???????relu1 = tf.nn.relu(tf.nn.bias_add(conv1,conv1_biases)) ???????????????#第二层 最大池化层 ?39,39,32--->1,2,2,1--->19,19,32 ???????#池化层过滤器大小为2*2 移动步长为2 使用全0填充 ???????pool1 = tf.nn.max_pool(relu1, ksize=[1,2,2,1],strides=[1,2,2,1],padding=‘SAME‘) ???????????????#第三层 卷积层 ??19,19,32--->5,5,32,64--->19,19,64 ???????#卷积核大小为5*5 当前层深度为32 卷积核的深度为64 ???????conv2_weights = tf.get_variable("conv_weights",[5,5,32,64],initializer = tf.truncated_normal_initializer(stddev=0.1)) ???????conv2_biases = tf.get_variable("conv2_biases",[64],initializer = tf.constant_initializer(0.0)) ???????????????conv2 = tf.nn.conv2d(pool1,conv2_weights,strides=[1,1,1,1],padding=‘SAME‘) #移动步长为1 使用全0填充 ???????relu2 = tf.nn.relu(tf.nn.bias_add(conv2,conv2_biases)) ???????#第四层 最大池化层 19,19,64--->1,2,2,1--->9,9,64 ???????#池化层过滤器大小为2*2 移动步长为2 使用全0填充 ???????pool2 = tf.nn.max_pool(relu2,ksize = [1,2,2,1],strides=[1,2,2,1],padding=‘SAME‘) ???????????????#第五层 全连接层 ????????fc1_weights = tf.get_variable("fc1_weights",[7*7*64,1024],initializer = tf.truncated_normal_initializer(stddev=0.1)) ???????fc1_biases = tf.get_variable("fc1_biases",[1024],initializer = tf.constant_initializer(0.1)) #[1,1024] ???????pool2_vector = tf.reshape(pool2,[-1,7*7*64]) #特征向量扁平化 原始的每一张图变成了一行9×9*64列的向量 ???????fc1 = tf.nn.relu(tf.matmul(pool2_vector,fc1_weights)+fc1_biases) ???????????????#为了减少过拟合 加入dropout层 ???????????????fc1_dropout = tf.nn.dropout(fc1,keep_prob) ???????????????#第六层 全连接层 ???????#神经元节点数为1024 ?分类节点2 ???????fc2_weights = tf.get_variable("fc2_weights",[1024,2],initializer=tf.truncated_normal_initializer(stddev=0.1)) ???????fc2_biases = tf.get_variable("fc2_biases",[2],initializer = tf.constant_initializer(0.1)) ???????fc2 = tf.matmul(fc1_dropout,fc2_weights) + fc2_biases ???????????????return fc2 ???def lenet_loss(self,fc2,y_): ???????????????#第七层 输出层 ???????#softmax ???????y_conv = tf.nn.softmax(fc2) ?????????labels=tf.one_hot(y_,2) ??????????????#定义交叉熵损失函数 ???????#cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv),reduction_indices=[1])) ???????loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = y_conv, labels =labels)) ???????self.cost = loss ???????return self.cost ???def lenet_optimer(self,loss): ???????train_optimizer = tf.train.GradientDescentOptimizer(lr).minimize(loss) ?????????return train_optimizer ???#计算softmax交叉熵损失函数 ?????def softmax_loss(self,predicts,labels): ?????????predicts=tf.nn.softmax(predicts) ?????????labels=tf.one_hot(labels,self.weights[‘fc2‘].get_shape().as_list()[1]) ?????????loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = predicts, labels =labels)) ???????self.cost= loss ?????????return self.cost ?????#梯度下降 ?????def optimer(self,loss,lr=0.01): ?????????train_optimizer = tf.train.GradientDescentOptimizer(lr).minimize(loss) ???????????return train_optimizer ?????def train(): ?????image,label=read_and_decode("./train.tfrecords") ???batch_image,batch_label=get_batch(image,label,batch_size=30,crop_size=28) ???#建立网络,训练所用 ?????x = tf.placeholder("float",shape=[None,28,28,3],name=‘x-input‘) ???y_ = tf.placeholder("int32",shape=[None]) ???keep_prob = tf.placeholder(tf.float32) ???net=network() ?????inf = net.lenet(x,keep_prob) ???loss=net.lenet_loss(inf,y_) ?#计算loss ???opti=net.optimer(loss) ?#梯度下降 ???????correct_prediction = tf.equal(tf.cast(tf.argmax(inf,1),tf.int32),batch_label) ???accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) ???????init=tf.global_variables_initializer() ???with tf.Session() as session: ?????????with tf.device("/gpu:0"): ???????????session.run(init) ?????????????coord = tf.train.Coordinator() ?????????????threads = tf.train.start_queue_runners(coord=coord) ?????????????max_iter=10000 ?????????????iter=0 ?????????????if os.path.exists(os.path.join("model",‘model.ckpt‘)) is True: ?????????????????tf.train.Saver(max_to_keep=None).restore(session, os.path.join("model",‘model.ckpt‘)) ?????????????while iter<max_iter: ?????????????????#loss_np,_,label_np,image_np,inf_np=session.run([loss,opti,batch_image,batch_label,inf]) ????????????????b_batch_image,b_batch_label = session.run([batch_image,batch_label]) ?????????????????loss_np,_=session.run([loss,opti],feed_dict={x:b_batch_image,y_:b_batch_label,keep_prob:0.6}) ??????????????????if iter%50==0: ?????????????????????print ‘trainloss:‘,loss_np ??????????????????if iter%500==0: ???????????????????#accuracy_np = session.run([accuracy]) ???????????????????accuracy_np = session.run([accuracy],feed_dict={x:b_batch_image,y_:b_batch_label,keep_prob:1.0}) ???????????????????print ‘xxxxxxxxxxxxxxxxxxxxxx‘,accuracy_np ???????????????iter+=1 ?????????????coord.request_stop()#queue需要关闭，否则报错 ?????????????coord.join(threads) ??????????if __name__ == ‘__main__‘: ???train()