巩义网站建设哪家专业,建网站能挣钱吗,青岛网站建设q479185700棒,网站设计方案前言
上一章研究了一些基本的构建神经网络所需的结构#xff1a;层、激活函数、损失函数、优化器之类的#xff0c;这一篇就解决上一章遗留的问题#xff1a;使用CNN构建手写数字识别网络、保存模型参数、单张图片的识别
国际惯例#xff0c;参考博客#xff1a;
tenso…前言
上一章研究了一些基本的构建神经网络所需的结构层、激活函数、损失函数、优化器之类的这一篇就解决上一章遗留的问题使用CNN构建手写数字识别网络、保存模型参数、单张图片的识别
国际惯例参考博客
tensorflow之保存模型与加载模型
【tensorflow】保存模型、再次加载模型等操作
tensorflow_二分类模型之单张图片测试
TensorFlow-Examples
训练数据实现
先贴一下仿真数据集链接https://pan.baidu.com/s/1ugEy85182vjcXQ8VoMJAbg 密码1o83
主要就是把手写数字数据集可视化成png图片保存利用txt文本文档保存路径和标签看上一篇博客就懂了。话不多说折腾起来。
数据集处理
IMG_HEIGHT 28 # 高
IMG_WIDTH 28 # 宽
CHANNELS 3 # 通道数
def read_images(dataset_path, batch_size):imagepaths, labels list(), list()data open(dataset_path, r).read().splitlines()for d in data:imagepaths.append(d.split( )[0])labels.append(int(d.split( )[1])) # 转换为张量imagepaths tf.convert_to_tensor(imagepaths, dtypetf.string)labels tf.convert_to_tensor(labels, dtypetf.int32)# 建立TF队列打乱数据image, label tf.train.slice_input_producer([imagepaths, labels],shuffleTrue)# 读取数据image tf.read_file(image)image tf.image.decode_jpeg(image, channelsCHANNELS)# 将图像resize成规定大小image tf.image.resize_images(image, [IMG_HEIGHT, IMG_WIDTH])# 手动归一化image image * 1.0/127.5 - 1.0# 创建batchinputX, inputY tf.train.batch([image, label], batch_sizebatch_size,capacitybatch_size * 8,num_threads4)return inputX, inputY构建网络
#训练参数
learning_rate 0.001
num_steps 1000
batch_size 128
display_step 10
#网络参数
num_classes 10
dropout 0.75#定义卷积操作
def conv2d(x, W, b, strides1): x tf.nn.conv2d(x, W, strides[1, strides, strides, 1], paddingSAME)x tf.nn.bias_add(x, b)return tf.nn.relu(x)
#定义池化操作
def maxpool2d(x, k2):return tf.nn.max_pool(x, ksize[1, k, k, 1], strides[1, k, k, 1],paddingSAME)
#定义网络结构
def conv_net(x, weights, biases, dropout):#输入数据x tf.reshape(x, shape[-1, IMG_HEIGHT, IMG_WIDTH, CHANNELS])#第一层卷积conv1 conv2d(x, weights[wc1], biases[bc1])conv1 maxpool2d(conv1, k2)#第二层卷积conv2 conv2d(conv1, weights[wc2], biases[bc2])conv2 maxpool2d(conv2, k2)#全连接fc1 tf.reshape(conv2, [-1, weights[wd1].get_shape().as_list()[0]])fc1 tf.add(tf.matmul(fc1, weights[wd1]), biases[bd1])fc1 tf.nn.relu(fc1)fc1 tf.nn.dropout(fc1, dropout)# 输出out tf.add(tf.matmul(fc1, weights[out]), biases[out])return out#初始化权重以后便于保存
weights {wc1: tf.Variable(tf.random_normal([5, 5, CHANNELS, 32])),wc2: tf.Variable(tf.random_normal([5, 5, 32, 64])),wd1: tf.Variable(tf.random_normal([7*7*64, 1024])),out: tf.Variable(tf.random_normal([1024, num_classes]))
}
#初始化偏置以后便于保存
biases {bc1: tf.Variable(tf.random_normal([32])),bc2: tf.Variable(tf.random_normal([64])),bd1: tf.Variable(tf.random_normal([1024])),out: tf.Variable(tf.random_normal([num_classes]))
}定义网络输入输出
注意如果想留个接口以便后续丢入单张图片进行测试一定要留输入接口方法如下
#定义图结构的输入
X tf.placeholder(tf.float32, [None, IMG_HEIGHT, IMG_WIDTH,CHANNELS],nameX)
Y tf.placeholder(tf.float32, [None, num_classes],nameY)
keep_prob tf.placeholder(tf.float32,namekeep_prob)一定要输入name因为后续我们取这个placeholder就是依据这个名字来取的因为这个方法比较常用
定义损失、优化器、训练/评估函数
# 构建模型
logits conv_net(X, weights, biases, keep_prob)
prediction tf.nn.softmax(logits,nameprediction)
#损失函数
loss_op tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logitslogits, labelsY))
optimizer tf.train.AdamOptimizer(learning_ratelearning_rate)
train_op optimizer.minimize(loss_op)
#评估函数
correct_pred tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))
accuracy tf.reduce_mean(tf.cast(correct_pred, tf.float32))训练
记得单热度编码tf.one_hot()
#保存模型
savertf.train.Saver()
# tf.add_to_collection(predict,prediction)
#训练
init tf.global_variables_initializer()#初始化模型
print(读取数据集)
input_img,input_labelread_images(./mnist/train_labels.txt,batch_sizebatch_size)
print(训练模型)
with tf.Session() as sess:coordtf.train.Coordinator()sess.run(init)#初始化参数 tf.train.start_queue_runners(sesssess,coordcoord)for step in range(1, num_steps1): batch_x, batch_y sess.run([input_img,tf.one_hot(input_label,num_classes,1,0)]) sess.run(train_op, feed_dict{X: batch_x, Y: batch_y, keep_prob: 0.8})if step % display_step 0 or step 1:# Calculate batch loss and accuracyloss, acc sess.run([loss_op, accuracy], feed_dict{X: batch_x,Y: batch_y,keep_prob: 1.0})print(Step str(step) , Minibatch Loss \{:.4f}.format(loss) , Training Accuracy \{:.3f}.format(acc))coord.request_stop()coord.join()print(Optimization Finished!)saver.save(sess,./cnn_mnist_model/CNN_Mnist)输出
读取数据集
训练模型
2018-08-03 12:38:05.596648: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2018-08-03 12:38:05.882851: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1392] Found device 0 with properties:
name: GeForce GTX 1060 6GB major: 6 minor: 1 memoryClockRate(GHz): 1.759
pciBusID: 0000:01:00.0
totalMemory: 6.00GiB freeMemory: 4.96GiB
2018-08-03 12:38:05.889153: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1471] Adding visible gpu devices: 0
2018-08-03 12:38:06.600411: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:952] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-08-03 12:38:06.604687: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:958] 0
2018-08-03 12:38:06.606494: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:971] 0: N
2018-08-03 12:38:06.608588: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1084] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4726 MB memory) - physical GPU (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1)
Step 1, Minibatch Loss 206875.5000, Training Accuracy 0.219
Step 10, Minibatch Loss 71490.0000, Training Accuracy 0.164
Step 20, Minibatch Loss 27775.7266, Training Accuracy 0.398
Step 30, Minibatch Loss 15692.2725, Training Accuracy 0.641
Step 40, Minibatch Loss 18211.4141, Training Accuracy 0.625
Step 50, Minibatch Loss 7250.1758, Training Accuracy 0.789
Step 60, Minibatch Loss 10694.9902, Training Accuracy 0.750
Step 70, Minibatch Loss 10783.8535, Training Accuracy 0.766
Step 80, Minibatch Loss 6080.1138, Training Accuracy 0.844
Step 90, Minibatch Loss 6720.9380, Training Accuracy 0.867
Step 100, Minibatch Loss 3673.7524, Training Accuracy 0.922
Step 110, Minibatch Loss 7893.8228, Training Accuracy 0.836
Step 120, Minibatch Loss 6805.0176, Training Accuracy 0.852
Step 130, Minibatch Loss 2863.3728, Training Accuracy 0.906
Step 140, Minibatch Loss 3335.6992, Training Accuracy 0.883
Step 150, Minibatch Loss 3514.4031, Training Accuracy 0.914
Step 160, Minibatch Loss 1842.5328, Training Accuracy 0.945
Step 170, Minibatch Loss 3443.9966, Training Accuracy 0.914
Step 180, Minibatch Loss 1961.7180, Training Accuracy 0.945
Step 190, Minibatch Loss 2919.5215, Training Accuracy 0.898
Step 200, Minibatch Loss 4270.7686, Training Accuracy 0.891
Step 210, Minibatch Loss 3591.2534, Training Accuracy 0.922
Step 220, Minibatch Loss 4692.2163, Training Accuracy 0.867
Step 230, Minibatch Loss 1537.0554, Training Accuracy 0.914
Step 240, Minibatch Loss 3574.1797, Training Accuracy 0.898
Step 250, Minibatch Loss 5143.3276, Training Accuracy 0.898
Step 260, Minibatch Loss 2142.9756, Training Accuracy 0.922
Step 270, Minibatch Loss 1323.6707, Training Accuracy 0.945
Step 280, Minibatch Loss 2004.2051, Training Accuracy 0.961
Step 290, Minibatch Loss 1112.9484, Training Accuracy 0.938
Step 300, Minibatch Loss 1977.6018, Training Accuracy 0.922
Step 310, Minibatch Loss 876.0104, Training Accuracy 0.977
Step 320, Minibatch Loss 3448.3142, Training Accuracy 0.953
Step 330, Minibatch Loss 1173.9749, Training Accuracy 0.961
Step 340, Minibatch Loss 2152.9966, Training Accuracy 0.938
Step 350, Minibatch Loss 3113.6838, Training Accuracy 0.938
Step 360, Minibatch Loss 1779.6680, Training Accuracy 0.922
Step 370, Minibatch Loss 2738.2637, Training Accuracy 0.930
Step 380, Minibatch Loss 1666.9695, Training Accuracy 0.922
Step 390, Minibatch Loss 2076.6716, Training Accuracy 0.914
Step 400, Minibatch Loss 3356.1475, Training Accuracy 0.914
Step 410, Minibatch Loss 1222.7729, Training Accuracy 0.953
Step 420, Minibatch Loss 2422.6355, Training Accuracy 0.898
Step 430, Minibatch Loss 4377.9385, Training Accuracy 0.914
Step 440, Minibatch Loss 1566.1058, Training Accuracy 0.969
Step 450, Minibatch Loss 3540.1555, Training Accuracy 0.875
Step 460, Minibatch Loss 1136.4354, Training Accuracy 0.961
Step 470, Minibatch Loss 2821.9456, Training Accuracy 0.938
Step 480, Minibatch Loss 1804.5267, Training Accuracy 0.945
Step 490, Minibatch Loss 625.0988, Training Accuracy 0.977
Step 500, Minibatch Loss 2406.8958, Training Accuracy 0.930
Step 510, Minibatch Loss 1198.2866, Training Accuracy 0.961
Step 520, Minibatch Loss 680.7784, Training Accuracy 0.953
Step 530, Minibatch Loss 2329.2104, Training Accuracy 0.961
Step 540, Minibatch Loss 848.0190, Training Accuracy 0.945
Step 550, Minibatch Loss 1327.9423, Training Accuracy 0.938
Step 560, Minibatch Loss 1020.9082, Training Accuracy 0.961
Step 570, Minibatch Loss 1885.4563, Training Accuracy 0.922
Step 580, Minibatch Loss 820.5620, Training Accuracy 0.953
Step 590, Minibatch Loss 1448.5205, Training Accuracy 0.938
Step 600, Minibatch Loss 857.7993, Training Accuracy 0.969
Step 610, Minibatch Loss 1193.5856, Training Accuracy 0.930
Step 620, Minibatch Loss 1337.5518, Training Accuracy 0.961
Step 630, Minibatch Loss 2121.9165, Training Accuracy 0.953
Step 640, Minibatch Loss 1516.9609, Training Accuracy 0.938
Step 650, Minibatch Loss 666.7323, Training Accuracy 0.977
Step 660, Minibatch Loss 1004.4291, Training Accuracy 0.953
Step 670, Minibatch Loss 193.3173, Training Accuracy 0.984
Step 680, Minibatch Loss 1339.3765, Training Accuracy 0.945
Step 690, Minibatch Loss 709.9714, Training Accuracy 0.961
Step 700, Minibatch Loss 1380.6301, Training Accuracy 0.953
Step 710, Minibatch Loss 630.5464, Training Accuracy 0.977
Step 720, Minibatch Loss 667.1447, Training Accuracy 0.953
Step 730, Minibatch Loss 1253.6014, Training Accuracy 0.977
Step 740, Minibatch Loss 473.8666, Training Accuracy 0.984
Step 750, Minibatch Loss 809.3101, Training Accuracy 0.961
Step 760, Minibatch Loss 508.8592, Training Accuracy 0.984
Step 770, Minibatch Loss 308.9244, Training Accuracy 0.969
Step 780, Minibatch Loss 1291.0034, Training Accuracy 0.984
Step 790, Minibatch Loss 1884.8574, Training Accuracy 0.938
Step 800, Minibatch Loss 1481.6635, Training Accuracy 0.961
Step 810, Minibatch Loss 463.2684, Training Accuracy 0.969
Step 820, Minibatch Loss 1116.5591, Training Accuracy 0.961
Step 830, Minibatch Loss 2422.9155, Training Accuracy 0.953
Step 840, Minibatch Loss 471.8990, Training Accuracy 0.984
Step 850, Minibatch Loss 1480.4053, Training Accuracy 0.945
Step 860, Minibatch Loss 1062.6339, Training Accuracy 0.938
Step 870, Minibatch Loss 833.3881, Training Accuracy 0.953
Step 880, Minibatch Loss 2153.9014, Training Accuracy 0.953
Step 890, Minibatch Loss 1617.7456, Training Accuracy 0.953
Step 900, Minibatch Loss 347.2119, Training Accuracy 0.969
Step 910, Minibatch Loss 175.5020, Training Accuracy 0.977
Step 920, Minibatch Loss 680.8482, Training Accuracy 0.969
Step 930, Minibatch Loss 240.1681, Training Accuracy 0.977
Step 940, Minibatch Loss 882.4927, Training Accuracy 0.977
Step 950, Minibatch Loss 407.1322, Training Accuracy 0.977
Step 960, Minibatch Loss 300.9460, Training Accuracy 0.969
Step 970, Minibatch Loss 1848.9391, Training Accuracy 0.945
Step 980, Minibatch Loss 496.5137, Training Accuracy 0.969
Step 990, Minibatch Loss 473.6212, Training Accuracy 0.969
Step 1000, Minibatch Loss 124.8958, Training Accuracy 0.992
Optimization Finished!【更新日志】2019-9-2 在tf.train.Saver中还可以提供额外三个参数详细可以参考官方文档
max_to_keep保存多少个最新模型避免模型一直存存到磁盘满了当存到设置的数值时会删掉前面的一个模型保证当前存储的模型数目是设置值keep_checkpoint_every_n_hours:每多少小时保存一次模型
模型载入与测试单张图片
我折腾很久的原因就在于在定义训练网络的输入参数时没有给name值导致get_tensor_by_name一直取不出来对应输入接口测试图片无法丢入到网络中最后发现的时候气出猪叫。
读图片
直接使用opencv的读取函数处理图像记得与训练时图像的处理方法一致最后要reshape成(1,28,28,3)(1,28,28,3)(1,28,28,3)大小其实这里也卡了好久待会后面再说
images []
image cv2.imread(./mnist/test/5/5_9.png)
images.append(image)
images np.array(images, dtypenp.uint8)
images images.astype(float32)
images np.subtract(np.multiply(images, 1.0/127.5) , 1.0)
x_batch images.reshape(1,28,28,3)载入模型
savertf.train.import_meta_graph(./cnn_mnist_model/CNN_Mnist.meta)
saver.restore(sess,./cnn_mnist_model/CNN_Mnist)预测
取出预测函数和测试图片接收器
graphtf.get_default_graph()
predgraph.get_tensor_by_name(prediction:0)
#X graph.get_operation_by_name(X).outputs[0]
Xgraph.get_tensor_by_name(X:0)
keep_probgraph.get_tensor_by_name(keep_prob:0)直接预测
resultsess.run(pred,feed_dict{X:x_batch,keep_prob:1.0})
print(result)结果
2018-08-03 12:46:50.098990: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:958] 0
2018-08-03 12:46:50.101351: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:971] 0: N
2018-08-03 12:46:50.104446: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1084] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4726 MB memory) - physical GPU (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1)
[[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]]我也预测了另外几张图片结果基本没问题。
上面说读取图片卡了很久原因在于我之前使用训练时候读取图片的方法来读图片也就是使用tensorflow的image类来读图片 image tf.read_file(./mnist/test/5/5_9.png)image tf.image.decode_jpeg(image, channels3)# 将图像resize成规定大小image tf.image.resize_images(image, [28, 28])# 手动归一化image image * 1.0/127.5 - 1.0image tf.reshape(image, shape[1, 28, 28, 3])出现如下错误
TypeError: The value of a feed cannot be a tf.Tensor object. Acceptable feed values include Python scalars, strings, lists, numpy ndarrays, or TensorHandles.For reference, the tensor object was Tensor(Reshape:0, shape(1, 28, 28, 3), dtypefloat32) which was passed to the feed with key Tensor(X:0, shape(?, 28, 28, 3), dtypefloat32).这个错误意思是说无法将tf.Tensor类型的变量feed给输入接口能够接受的有Pyhon的巴拉巴拉类型真皮。所以又得把tf.Tensor的值取出来如果看过之前的博客就知道用eval()取值跟Theano一模一样
将原来测试的那句话
resultsess.run(pred,feed_dict{X:image,keep_prob:1.0})改成
resultsess.run(pred,feed_dict{X:image.eval(),keep_prob:1.0})就阔以了所以还是建议测试的时候直接用numpy变量处理不要折腾成tensorflow的变量类型最后还得转回来。
后记
好羡慕TensorLayer和tflearn那么简介的模型构建、保存、载入方法好想脱坑。
博文代码
训练链接https://pan.baidu.com/s/1zZYGgnGj3kttklzZnyJLQA 密码zepl
测试链接https://pan.baidu.com/s/1BygjSatjxtuVIq_HHt9o7A 密码ky87
