import osimport numpy as npimport matplotlib.pyplot as pltfrom PIL import Image, ImageChopsfrom 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 = []

for j in range(len(fileList)): data = os.listdir("F:\\data\\flowers\\"+fileList[j]) testNum = int(len(data)*0.25) while(testNum>0): np.random.shuffle(data) testNum -= 1 trainData = np.array(data[:-(int(len(data)*0.25))]) testData = np.array(data[-(int(len(data)*0.25)):]) for i in range(len(trainData)): if(trainData[i][-3:]=="jpg"): image = io.imread("F:\\data\\flowers\\"+fileList[j]+"\\"+trainData[i]) image=transform.resize(image,(64,64)) trainDataList.append(image) trianLabel.append(int(j)) for i in range(len(testData)): if(testData[i][-3:]=="jpg"): image = io.imread("F:\\data\\flowers\\"+fileList[j]+"\\"+testData[i]) image=transform.resize(image,(64,64)) testDataList.append(image) testLabel.append(int(j))print("图片数据读取完了...")

print(np.shape(trainDataList))print(np.shape(trianLabel))print(np.shape(testDataList))print(np.shape(testLabel))

print("正在写磁盘...") 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 npfrom keras.utils import to_categorical

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 randomimport 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...")

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...")

# coding: utf-8

import tensorflow as tffrom random import shuffle

INPUT_NODE = 64*64OUT_NODE = 5IMAGE_SIZE = 64NUM_CHANNELS = 3NUM_LABELS = 5

#第一层卷积层的尺寸和深度CONV1_DEEP = 16CONV1_SIZE = 5#第二层卷积层的尺寸和深度CONV2_DEEP = 32CONV2_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 timeimport kerasimport numpy as npfrom 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 = 10n_classes=5epochs=16#循环次数learning_rate=1e-4batch_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 = 64ro = 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')

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