win 10 系统下利用tensorflow+python实现花朵识别 |
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win 10 系统下利用tensorflow+python实现花朵识别
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windows系统下利用tensorflow+python实现花朵识别
本文主要通过CNN进行花卉的分类，训练结束保存模型，最后通过调用模型，输入花卉的图片通过模型来进行类别的预测。 目的：识别不同的花朵 环境：win10 +TensorFlow 1.12.0+pycharm 数据集：http://download.tensorflow.org/example_images/flower_photos.tgz
首先肯定是要搭建tensorflow和python的环境了，可以参考我之前的博客：在windows 10 64位系统下安装TensorFlow 和 pycharm安装教程 其他环境也可以百度。 二、下载花朵图片数据集下载地址：http://download.tensorflow.org/example_images/flower_photos.tgz 现在我们要训练花朵的识别模型，这是 Google 在TensorFlow里面提供的一个例子，其中包含了5类花朵的训练图片。可以新建个文件夹，用于存放数据和训练的模型。 如下图：flower_photos,用于放置训练图片 flower_model用于放置产生的模型，具体程序里有路径，灵活应用。 我用的pycharm编译的程序，新建一个py文件 代码如下，复制进去 #程序功能：用tensorflow训练5种花朵的模型 #作者：Mao #时间：2018.12.6 #参考博文 #作者：Enchanted_ZhouH #来源：CSDN #原文：https://blog.csdn.net/Enchanted_ZhouH/article/details/74116823 import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' from skimage import io,transform import glob import os import tensorflow as tf import numpy as np import time #数据集地址 path= 'E:/TensorFlow_project/18_12_5_flower/flower_photos/' #模型保存地址 model_path='E:/TensorFlow_project/18_12_5_flower/flower_model/model.ckpt' #将所有的图片resize成100*100 w=100 h=100 c=3 #读取图片 def read_img(path): cate=[path+x for x in os.listdir(path) if os.path.isdir(path+x)] imgs=[] labels=[] for idx,folder in enumerate(cate): for im in glob.glob(folder+'/*.jpg'): print('reading the images:%s'%(im)) img=io.imread(im) img=transform.resize(img,(w,h)) imgs.append(img) labels.append(idx) return np.asarray(imgs,np.float32),np.asarray(labels,np.int32) data,label=read_img(path) #打乱顺序 num_example=data.shape[0] arr=np.arange(num_example) np.random.shuffle(arr) data=data[arr] label=label[arr] #将所有数据分为训练集和验证集 ratio=0.8 s=np.int(num_example*ratio) x_train=data[:s] y_train=label[:s] x_val=data[s:] y_val=label[s:] #-----------------构建网络---------------------- #占位符 x=tf.placeholder(tf.float32,shape=[None,w,h,c],name='x') y_=tf.placeholder(tf.int32,shape=[None,],name='y_') def inference(input_tensor, train, regularizer): with tf.variable_scope('layer1-conv1'): conv1_weights = tf.get_variable("weight",[5,5,3,32],initializer=tf.truncated_normal_initializer(stddev=0.1)) conv1_biases = tf.get_variable("bias", [32], initializer=tf.constant_initializer(0.0)) conv1 = tf.nn.conv2d(input_tensor, conv1_weights, strides=[1, 1, 1, 1], padding='SAME') relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases)) with tf.name_scope("layer2-pool1"): pool1 = tf.nn.max_pool(relu1, ksize = [1,2,2,1],strides=[1,2,2,1],padding="VALID") with tf.variable_scope("layer3-conv2"): conv2_weights = tf.get_variable("weight",[5,5,32,64],initializer=tf.truncated_normal_initializer(stddev=0.1)) conv2_biases = tf.get_variable("bias", [64], initializer=tf.constant_initializer(0.0)) conv2 = tf.nn.conv2d(pool1, conv2_weights, strides=[1, 1, 1, 1], padding='SAME') relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases)) with tf.name_scope("layer4-pool2"): pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') with tf.variable_scope("layer5-conv3"): conv3_weights = tf.get_variable("weight",[3,3,64,128],initializer=tf.truncated_normal_initializer(stddev=0.1)) conv3_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0)) conv3 = tf.nn.conv2d(pool2, conv3_weights, strides=[1, 1, 1, 1], padding='SAME') relu3 = tf.nn.relu(tf.nn.bias_add(conv3, conv3_biases)) with tf.name_scope("layer6-pool3"): pool3 = tf.nn.max_pool(relu3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') with tf.variable_scope("layer7-conv4"): conv4_weights = tf.get_variable("weight",[3,3,128,128],initializer=tf.truncated_normal_initializer(stddev=0.1)) conv4_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0)) conv4 = tf.nn.conv2d(pool3, conv4_weights, strides=[1, 1, 1, 1], padding='SAME') relu4 = tf.nn.relu(tf.nn.bias_add(conv4, conv4_biases)) with tf.name_scope("layer8-pool4"): pool4 = tf.nn.max_pool(relu4, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') nodes = 6*6*128 reshaped = tf.reshape(pool4,[-1,nodes]) with tf.variable_scope('layer9-fc1'): fc1_weights = tf.get_variable("weight", [nodes, 1024], initializer=tf.truncated_normal_initializer(stddev=0.1)) if regularizer != None: tf.add_to_collection('losses', regularizer(fc1_weights)) fc1_biases = tf.get_variable("bias", [1024], initializer=tf.constant_initializer(0.1)) fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_weights) + fc1_biases) if train: fc1 = tf.nn.dropout(fc1, 0.5) with tf.variable_scope('layer10-fc2'): fc2_weights = tf.get_variable("weight", [1024, 512], initializer=tf.truncated_normal_initializer(stddev=0.1)) if regularizer != None: tf.add_to_collection('losses', regularizer(fc2_weights)) fc2_biases = tf.get_variable("bias", [512], initializer=tf.constant_initializer(0.1)) fc2 = tf.nn.relu(tf.matmul(fc1, fc2_weights) + fc2_biases) if train: fc2 = tf.nn.dropout(fc2, 0.5) with tf.variable_scope('layer11-fc3'): fc3_weights = tf.get_variable("weight", [512, 5], initializer=tf.truncated_normal_initializer(stddev=0.1)) if regularizer != None: tf.add_to_collection('losses', regularizer(fc3_weights)) fc3_biases = tf.get_variable("bias", [5], initializer=tf.constant_initializer(0.1)) logit = tf.matmul(fc2, fc3_weights) + fc3_biases return logit #---------------------------网络结束--------------------------- regularizer = tf.contrib.layers.l2_regularizer(0.0001) logits = inference(x,False,regularizer) #(小处理)将logits乘以1赋值给logits_eval，定义name，方便在后续调用模型时通过tensor名字调用输出tensor b = tf.constant(value=1,dtype=tf.float32) logits_eval = tf.multiply(logits,b,name='logits_eval') loss=tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y_) train_op=tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss) correct_prediction = tf.equal(tf.cast(tf.argmax(logits,1),tf.int32), y_) acc= tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) #定义一个函数，按批次取数据 def minibatches(inputs=None, targets=None, batch_size=None, shuffle=False): assert len(inputs) == len(targets) if shuffle: indices = np.arange(len(inputs)) np.random.shuffle(indices) for start_idx in range(0, len(inputs) - batch_size + 1, batch_size): if shuffle: excerpt = indices[start_idx:start_idx + batch_size] else: excerpt = slice(start_idx, start_idx + batch_size) yield inputs[excerpt], targets[excerpt] #训练和测试数据，可将n_epoch设置更大一些 n_epoch=10 batch_size=64 saver=tf.train.Saver() sess=tf.Session() sess.run(tf.global_variables_initializer()) for epoch in range(n_epoch): start_time = time.time() #training train_loss, train_acc, n_batch = 0, 0, 0 for x_train_a, y_train_a in minibatches(x_train, y_train, batch_size, shuffle=True): _,err,ac=sess.run([train_op,loss,acc], feed_dict={x: x_train_a, y_: y_train_a}) train_loss += err; train_acc += ac; n_batch += 1 print(" train loss: %f" % (np.sum(train_loss)/ n_batch)) print(" train acc: %f" % (np.sum(train_acc)/ n_batch)) #validation val_loss, val_acc, n_batch = 0, 0, 0 for x_val_a, y_val_a in minibatches(x_val, y_val, batch_size, shuffle=False): err, ac = sess.run([loss,acc], feed_dict={x: x_val_a, y_: y_val_a}) val_loss += err; val_acc += ac; n_batch += 1 print(" validation loss: %f" % (np.sum(val_loss)/ n_batch)) print(" validation acc: %f" % (np.sum(val_acc)/ n_batch)) saver.save(sess,model_path) sess.close()运行这个py文件，会不断的读图，最后生成模型，运行图如下： 运行成功后的界面 代码如下： #程序功能：用tensorflow训练5种花朵的模型后，调用模型，识别花朵 #作者：mao #时间：2018.12.6 #参考博文 #作者：Enchanted_ZhouH #来源：CSDN #原文：https://blog.csdn.net/Enchanted_ZhouH/article/details/74116823 import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' from skimage import io,transform import tensorflow as tf import numpy as np path1 = "E:/TensorFlow_project/18_12_5_flower/flower_photos/daisy/5547758_eea9edfd54_n.jpg" path2 = "E:/TensorFlow_project/18_12_5_flower/flower_photos/dandelion/7355522_b66e5d3078_m.jpg" path3 = "E:/TensorFlow_project/18_12_5_flower/flower_photos/roses/394990940_7af082cf8d_n.jpg" path4 = "E:/TensorFlow_project/18_12_5_flower/flower_photos/sunflowers/6953297_8576bf4ea3.jpg" path5 = "E:/TensorFlow_project/18_12_5_flower/flower_photos/tulips/10791227_7168491604.jpg" flower_dict = {0:'dasiy',1:'dandelion',2:'roses',3:'sunflowers',4:'tulips'} w=100 h=100 c=3 def read_one_image(path): img = io.imread(path) img = transform.resize(img,(w,h)) return np.asarray(img) with tf.Session() as sess: data = [] data1 = read_one_image(path1) data2 = read_one_image(path2) data3 = read_one_image(path3) data4 = read_one_image(path4) data5 = read_one_image(path5) data.append(data1) data.append(data2) data.append(data3) data.append(data4) data.append(data5) saver = tf.train.import_meta_graph('E:/TensorFlow_project/18_12_5_flower/flower_model/model.ckpta') saver.restore(sess,tf.train.latest_checkpoint('E:/TensorFlow_project/18_12_5_flower/flower_model/')) graph = tf.get_default_graph() x = graph.get_tensor_by_name("x:0") feed_dict = {x:data} logits = graph.get_tensor_by_name("logits_eval:0") classification_result = sess.run(logits,feed_dict) #打印出预测矩阵 print(classification_result) #打印出预测矩阵每一行最大值的索引 print(tf.argmax(classification_result,1).eval()) #根据索引通过字典对应花的分类 output = [] output = tf.argmax(classification_result,1).eval() for i in range(len(output)): print("第",i+1,"朵花预测:"+flower_dict[output[i]])运行结果 本文的CNN模型完全可以通过增大模型复杂度或者改参数调试以及对图像进行预处理来提高准确率， 注意：一些图片读取或者模型保存及读取的路径要正确，需要在程序里修改一下。 参考：TensorFlow之CNN图像分类及模型保存与调用：https://blog.csdn.net/Enchanted_ZhouH/article/details/74116823 |
并且在相应文件夹下生成模型文件 
说明我们运行成功了，预测结果和调用模型代码中的五个路径相比较是完全准确的。 本文的模型对于花卉的分类准确率大概在70%左右，采用迁移学习调用Inception-v3模型对本文中的花卉数据集分类准确率在95%左右。主要的原因在于本文的CNN模型较于简单，而且花卉数据集本身就比mnist手写数字数据集分类难度就要大一点，同样的模型在mnist手写数字的识别上准确率要比花卉数据集准确率高不少。【本文地址】
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