首页分享 Tensorflow五种花卉分类

Tensorflow五种花卉分类

来源：花匠小妙招时间：2024-11-03 13:32

train.py 1

from skimage import io,transform import glob import os import tensorflow as tf import numpy as np import time import matplotlib.pyplot as plt #数据集地址 path='D:date_1flowers' #模型保存地址 model_path='D:model_1model.ckpt' #将所有的图片resize成100*100 w=100 #宽 h=100 #高 c=3 #通道数 #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),mode='constant') 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训练集（train)优化、验证集（validation）人工处理参数调整和测试集（test）不经过处理 ratio=0.8 s=np.int(num_example*ratio) x_train=data[:s] #训练集%80 验证集20% y_train=label[:s] x_val=data[s:] y_val=label[s:] #-----------------构建网络---------------------- #占位符 tf.reset_default_graph() #定义一个容器存放图像数据 x=tf.placeholder(tf.float32,shape=[None,w,h,c],name='x') #参数有数据类型数据形状名称 y_=tf.placeholder(tf.int32,shape=[None,],name='y_') #卷积层全都采用了补0，所以经过卷积层长和宽不变，只有深度加深。池化层全都没有补0，所以经过池化层长和宽均减小，深度不变。 #卷积层提取特征池化层对输入的特征图进行压缩，一方面使特征图变小，简化网络计算复杂度；一方面进行特征压缩，提取主要特征 #全连接层连接所有的特征，将输出值送给分类器（如softmax分类器） def inference(input_tensor, train, regularizer): # 张量训练正则化 with tf.variable_scope('layer1-conv1'): #卷积核5x5 输入通道3个输出通道32个 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') #激活relu非线性处理 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设置更大一些交叉熵计算损失 n_epoch=5 #训练次数 batch_size=64 #批处理参数训练集样本总数 saver=tf.train.Saver() #定义模型保存器/载入器 sess=tf.Session() sess.run(tf.global_variables_initializer()) fig_loss = np.zeros([n_epoch]) fig_acc1 = np.zeros([n_epoch]) fig_acc2= np.zeros([n_epoch]) 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)) fig_loss[epoch] = np.sum(train_loss)/ n_batch fig_acc1[epoch] = 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)) fig_acc2[epoch] = np.sum(val_acc) / n_batch # 训练loss图 fig, ax1 = plt.subplots() lns1 = ax1.plot(np.arange(n_epoch), fig_loss, label="Loss") ax1.set_xlabel('iteration') ax1.set_ylabel('training loss') # 训练和验证两种准确率曲线图放在一张图中 fig2, ax2 = plt.subplots() ax3 = ax2.twinx()#由ax2图生成ax3图 lns2 = ax2.plot(np.arange(n_epoch), fig_acc1, label="Loss") lns3 = ax3.plot(np.arange(n_epoch), fig_acc2, label="Loss") ax2.set_xlabel('iteration') ax2.set_ylabel('training acc') ax3.set_ylabel('val acc') # 合并图例 lns = lns3 + lns2 labels = ["train acc", "val acc"] plt.legend(lns, labels, loc=7) plt.show() saver.save(sess,model_path) sess.close()

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208

predict.py 1

from skimage import io,transform import tensorflow as tf import numpy as np path1 = "D:date_1flowerstulip8838983024_5c1a767878_n.jpg" path2 = "D:date_1flowerssunflower4895721242_89014e723c_n.jpg" path3 = "D:date_1flowersrose475947979_554062a608_m.jpg" path4 = "D:date_1flowersdandelion5749815755_12f9214649_n.jpg" path5 = "D:date_1flowersdaisy286875003_f7c0e1882d.jpg" path=[path1,path2,path3,path4,path5] flower_dict = {0:'dasiy',1:'dandelion',2:'rose',3:'sunflower',4:'tulip'} w=100 h=100 c=3 def read_one_image(path): img = io.imread(path) img = transform.resize(img,(w,h),mode='constant') 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('D:model_1model.ckpt.meta') saver.restore(sess,tf.train.latest_checkpoint('D:model_1')) 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]]+"-----"+"原图像路径："+path[i])

12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455