import os import numpy as np import matplotlib.pyplot as plt from PIL import Image, ImageChops from skimage import color,data,transform,io #获取所有数据文件夹名称 fileList = os.listdir("F:\data\flowers") trainDataList = [] trianLabel = [] testDataList = [] testLabel = [] #读取每一种花的文件 for j in range(len(fileList)): data = os.listdir("F:\data\flowers\"+fileList[j]) #取每一种花四分之一的数据作为测试数据集 testNum = int(len(data)*0.25) #把每种花的图片进行testNum次乱序处理 while(testNum>0): np.random.shuffle(data) testNum -= 1 #把每种花经过乱序后的四分之三当作训练集 trainData = np.array(data[:-(int(len(data)*0.25))]) #把每种花经过乱序后的四分之一当作测试集 testData = np.array(data[-(int(len(data)*0.25)):]) #从上面选出来的训练集中逐张读取出对应的图片 for i in range(len(trainData)): #其中这些图片都要满足jpg格式的 if(trainData[i][-3:]=="jpg"): #读取一张jpg图片 image = io.imread("F:\data\flowers\"+fileList[j]+"\"+trainData[i]) #把这张图片变成64*64大小的图片 image=transform.resize(image,(64,64)) #保存改变大小的图片到trainDataList列表 trainDataList.append(image) #保存这张图片的标签到trianLabel列表 trianLabel.append(int(j)) #随机生成一个角度，这个角度的范围在顺时针90度和逆时针90度之间 angle = np.random.randint(-90,90) #然后把上面那张64*64大小的图片随机旋转angle个角度 image =transform.rotate(image, angle) #把旋转得到的新图片再变成64*64大小的，因为旋转会改变一张图片的大小 image=transform.resize(image,(64,64)) #把旋转后并且大小是64*64的图片保存到trainDataList列表 trainDataList.append(image) #把旋转后并且大小是64*64的图片对应的标签保存到trianLabel列表 trianLabel.append(int(j)) #逐张读取每种花的测试图片 for i in range(len(testData)): #选取的图片要满足jpg格式的 if(testData[i][-3:]=="jpg"): #读取一张图片 image = io.imread("F:\data\flowers\"+fileList[j]+"\"+testData[i]) #改变这张图片的大小为64*64 image=transform.resize(image,(64,64)) #把改变后的图片保存到testDataList列表中 testDataList.append(image) #把这张图片对应的标签保存到testLabel列表中 testLabel.append(int(j)) print("图片数据读取完了...")
#打印训练集和测试数据集以及它们对应标签的规模 print(np.shape(trainDataList)) print(np.shape(trianLabel)) print(np.shape(testDataList)) print(np.shape(testLabel))
#保存训练集和测试数据集以及它们对应标签到磁盘 np.save("G:\trainDataList",trainDataList) np.save("G:\trianLabel",trianLabel) np.save("G:\testDataList",testDataList) np.save("G:\testLabel",testLabel) print("数据处理完了...")
import numpy as np from keras.utils import to_categorical #将训练数据集和测试数据集对应的标签转变为one-hot编码 trainLabel = np.load("G:\trianLabel.npy") testLabel = np.load("G:\testLabel.npy") trainLabel_encoded = to_categorical(trainLabel) testLabel_encoded = to_categorical(testLabel) np.save("G:\trianLabel",trainLabel_encoded) np.save("G:\testLabel",testLabel_encoded) print("转码类别写盘完了...")
import random import numpy as np trainDataList = np.load("G:\trainDataList.npy") trianLabel = np.load("G:\trianLabel.npy") print("数据加载完了...") trainIndex = [i for i in range(len(trianLabel))] random.shuffle(trainIndex) trainData = [] trainClass = [] for i in range(len(trainIndex)): trainData.append(trainDataList[trainIndex[i]]) trainClass.append(trianLabel[trainIndex[i]]) print("训练数据shuffle完了...") np.save("G:\trainDataList",trainData) np.save("G:\trianLabel",trainClass) print("训练数据写盘完毕...")
X = np.load("G:\trainDataList.npy") Y = np.load("G:\trianLabel.npy") print(np.shape(X)) print(np.shape(Y))
import random import numpy as np testDataList = np.load("G:\testDataList.npy") testLabel = np.load("G:\testLabel.npy") testIndex = [i for i in range(len(testLabel))] random.shuffle(testIndex) testData = [] testClass = [] for i in range(len(testIndex)): testData.append(testDataList[testIndex[i]]) testClass.append(testLabel[testIndex[i]]) print("测试数据shuffle完了...") np.save("G:\testDataList",testData) np.save("G:\testLabel",testClass) print("测试数据写盘完毕...")
X = np.load("G:\testDataList.npy") Y = np.load("G:\testLabel.npy") print(np.shape(X)) print(np.shape(Y)) print(np.shape(testData)) print(np.shape(testLabel))
import tensorflow as tf from random import shuffle INPUT_NODE = 64*64 OUT_NODE = 5 IMAGE_SIZE = 64 NUM_CHANNELS = 3 NUM_LABELS = 5 #第一层卷积层的尺寸和深度 CONV1_DEEP = 16 CONV1_SIZE = 5 #第二层卷积层的尺寸和深度 CONV2_DEEP = 32 CONV2_SIZE = 5 #全连接层的节点数 FC_SIZE = 512 def inference(input_tensor, train, regularizer): #卷积 with tf.variable_scope('layer1-conv1'): conv1_weights = tf.Variable(tf.random_normal([CONV1_SIZE,CONV1_SIZE,NUM_CHANNELS,CONV1_DEEP],stddev=0.1),name='weight') tf.summary.histogram('convLayer1/weights1', conv1_weights) conv1_biases = tf.Variable(tf.Variable(tf.random_normal([CONV1_DEEP])),name="bias") tf.summary.histogram('convLayer1/bias1', conv1_biases) conv1 = tf.nn.conv2d(input_tensor,conv1_weights,strides=[1,1,1,1],padding='SAME') tf.summary.histogram('convLayer1/conv1', conv1) relu1 = tf.nn.relu(tf.nn.bias_add(conv1,conv1_biases)) tf.summary.histogram('ConvLayer1/relu1', relu1) #池化 with tf.variable_scope('layer2-pool1'): pool1 = tf.nn.max_pool(relu1,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME') tf.summary.histogram('ConvLayer1/pool1', pool1) #卷积 with tf.variable_scope('layer3-conv2'): conv2_weights = tf.Variable(tf.random_normal([CONV2_SIZE,CONV2_SIZE,CONV1_DEEP,CONV2_DEEP],stddev=0.1),name='weight') tf.summary.histogram('convLayer2/weights2', conv2_weights) conv2_biases = tf.Variable(tf.random_normal([CONV2_DEEP]),name="bias") tf.summary.histogram('convLayer2/bias2', conv2_biases) #卷积向前学习 conv2 = tf.nn.conv2d(pool1,conv2_weights,strides=[1,1,1,1],padding='SAME') tf.summary.histogram('convLayer2/conv2', conv2) relu2 = tf.nn.relu(tf.nn.bias_add(conv2,conv2_biases)) tf.summary.histogram('ConvLayer2/relu2', relu2) #池化 with tf.variable_scope('layer4-pool2'): pool2 = tf.nn.max_pool(relu2,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME') tf.summary.histogram('ConvLayer2/pool2', pool2) #变型 pool_shape = pool2.get_shape().as_list() #计算最后一次池化后对象的体积（数据个数节点数像素个数） nodes = pool_shape[1]*pool_shape[2]*pool_shape[3] #根据上面的nodes再次把最后池化的结果pool2变为batch行nodes列的数据 reshaped = tf.reshape(pool2,[-1,nodes]) #全连接层 with tf.variable_scope('layer5-fc1'): fc1_weights = tf.Variable(tf.random_normal([nodes,FC_SIZE],stddev=0.1),name='weight') if(regularizer != None): tf.add_to_collection('losses',tf.contrib.layers.l2_regularizer(0.03)(fc1_weights)) fc1_biases = tf.Variable(tf.random_normal([FC_SIZE]),name="bias") #预测 fc1 = tf.nn.relu(tf.matmul(reshaped,fc1_weights)+fc1_biases) if(train): fc1 = tf.nn.dropout(fc1,0.5) #全连接层 with tf.variable_scope('layer6-fc2'): fc2_weights = tf.Variable(tf.random_normal([FC_SIZE,64],stddev=0.1),name="weight") if(regularizer != None): tf.add_to_collection('losses',tf.contrib.layers.l2_regularizer(0.03)(fc2_weights)) fc2_biases = tf.Variable(tf.random_normal([64]),name="bias") #预测 fc2 = tf.nn.relu(tf.matmul(fc1,fc2_weights)+fc2_biases) if(train): fc2 = tf.nn.dropout(fc2,0.5) #全连接层 with tf.variable_scope('layer7-fc3'): fc3_weights = tf.Variable(tf.random_normal([64,NUM_LABELS],stddev=0.1),name="weight") if(regularizer != None): tf.add_to_collection('losses',tf.contrib.layers.l2_regularizer(0.03)(fc3_weights)) fc3_biases = tf.Variable(tf.random_normal([NUM_LABELS]),name="bias") #预测 logit = tf.matmul(fc2,fc3_weights)+fc3_biases return logit
import time import keras import numpy as np from keras.utils import np_utils X = np.load("G:\trainDataList.npy") Y = np.load("G:\trianLabel.npy") print(np.shape(X)) print(np.shape(Y)) print(np.shape(testData)) print(np.shape(testLabel)) batch_size = 10 n_classes=5 epochs=16#循环次数 learning_rate=1e-4 batch_num=int(np.shape(X)[0]/batch_size) dropout=0.75 x=tf.placeholder(tf.float32,[None,64,64,3]) y=tf.placeholder(tf.float32,[None,n_classes]) # keep_prob = tf.placeholder(tf.float32) #加载测试数据集 test_X = np.load("G:\testDataList.npy") test_Y = np.load("G:\testLabel.npy") back = 64 ro = int(len(test_X)/back) #调用神经网络方法 pred=inference(x,1,"regularizer") cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred,labels=y)) # 三种优化方法选择一个就可以 optimizer=tf.train.AdamOptimizer(1e-4).minimize(cost) # train_step = tf.train.GradientDescentOptimizer(0.001).minimize(cost) # train_step = tf.train.MomentumOptimizer(0.001,0.9).minimize(cost) #将预测label与真实比较 correct_pred=tf.equal(tf.argmax(pred,1),tf.argmax(y,1)) #计算准确率 accuracy=tf.reduce_mean(tf.cast(correct_pred,tf.float32)) merged=tf.summary.merge_all() #将tensorflow变量实例化 init=tf.global_variables_initializer() start_time = time.time() with tf.Session() as sess: sess.run(init) #保存tensorflow参数可视化文件 writer=tf.summary.FileWriter('F:/Flower_graph', sess.graph) for i in range(epochs): for j in range(batch_num): offset = (j * batch_size) % (Y.shape[0] - batch_size) # 准备数据 batch_data = X[offset:(offset + batch_size), :] batch_labels = Y[offset:(offset + batch_size), :] sess.run(optimizer, feed_dict={x:batch_data,y:batch_labels}) result=sess.run(merged, feed_dict={x:batch_data,y:batch_labels}) writer.add_summary(result, i) loss,acc = sess.run([cost,accuracy],feed_dict={x:batch_data,y:batch_labels}) print("Epoch:", '%04d' % (i+1),"cost=", "{:.9f}".format(loss),"Training accuracy","{:.5f}".format(acc*100)) writer.close() print("########################训练结束，下面开始测试###################") for i in range(ro): s = i*back e = s+back test_accuracy = sess.run(accuracy,feed_dict={x:test_X[s:e],y:test_Y[s:e]}) print("step:%d test accuracy = %.4f%%" % (i,test_accuracy*100)) print("Final test accuracy = %.4f%%" % (test_accuracy*100)) end_time = time.time() print('Times：',(end_time-start_time)) print('Optimization Completed')
