diff --git a/lessonFive/to_jpg.py b/lessonFive/to_jpg.py
new file mode 100644
index 0000000..3af140a
--- /dev/null
+++ b/lessonFive/to_jpg.py
@@ -0,0 +1,76 @@
+#coding=utf-8
+import cv2
+import numpy as np
+import os
+import pickle
+
+
+#文件夹名
+str_2 = 'train_data'
+str_1 = 'test_data'
+
+#判断文件夹是否存在，不存在的话创建文件夹
+if os.path.exists(str_1) == False:
+ os.mkdir(str_1)
+if os.path.exists(str_2) == False:
+ os.mkdir(str_2)
+
+# 解压缩，返回解压后的字典，f,encoding='bytes'
+def unpickle(file):
+
+ fo = open(file, 'rb')
+ dict = pickle.load(fo, encoding='bytes')
+ fo.close()
+ return dict
+
+def cifar_jpg(dir_file):
+# 生成训练集图片，如果需要png格式，只需要改图片后缀名即可。
+ for j in range(1, 6):
+ dataName = dir_file + '/' + "data_batch_" + str(j) # 读取当前目录下的data_batch12345文件，dataName其实也是data_batch文件的路径，本文和脚本文件在同一目录下。
+ Xtr = unpickle(dataName)
+ print(Xtr)
+ print(dataName + " is loading...")
+
+ for i in range(0, 10000):
+ img = np.reshape(Xtr[b'data'][i], (3, 32, 32))
+ img = img.transpose(1, 2, 0) # 读取image
+ picName = 'train_data/' + str(Xtr[b'labels'][i]) + '_' + str(i + (j - 1) * 10000) + '.jpg' # Xtr['labels']为图片的标签，值范围0-9，本文中，train文件夹需要存在，并与脚本文件在同一目录下。
+ cv2.imwrite(picName, img)
+ print(dataName + " loaded.")
+
+ print("test_batch is loading...")
+
+# 生成测试集图片
+ testName = dir_file + '/' + 'data_batch_6'
+ testXtr = unpickle(testName)
+ for i in range(0, 10000):
+ img = np.reshape(testXtr[b'data'][i], (3, 32, 32))
+ img = img.transpose(1, 2, 0)
+ picName = 'test_data/' + str(testXtr[b'labels'][i]) + '_' + str(i) + '.jpg'
+ cv2.imwrite(picName, img)
+ print("test_batch loaded.")
+ return
+
+#标签与名字的对应关系
+def label_name():
+ label_name_dict ={
+ 'airplane': "0",
+ 'automobile': "1",
+ 'bird': "2",
+ 'cat': "3",
+ 'deer': "4",
+ 'dog': "5",
+ 'frog': "6",
+ 'horse': "7",
+ 'ship': "8",
+ 'truck': "9"
+ }
+ return label_name_dict
+
+if __name__ == '__main__':
+ dir_file = 'train_data'
+ #cifar_jpg(dir_file)
+try:
+ cifar_jpg(dir_file)
+except:
+ print('函数报错')
diff --git a/lessonOne/imgClassifierWeb/cnnModel.py b/lessonOne/imgClassifierWeb/cnnModel.py
index 8cac31f..1196aaf 100644
--- a/lessonOne/imgClassifierWeb/cnnModel.py
+++ b/lessonOne/imgClassifierWeb/cnnModel.py
@@ -106,7 +106,7 @@ def fc_layer(flattened_layer, num_inputs, num_outputs):
fc_resultl = tf.matmul(flattened_layer, fc_weights)
return fc_resultl
batch_size=gConfig['percent']*gConfig['dataset_size']/100
- self.data_tensor=tf.placeholder(tf.float32,shape=tf.placeholder(tf.float32,shape=[batch_size,gConfig['im_dim'], gConfig['im_dim'],gConfig['num_channels']],name='data_tensor'))
+ self.data_tensor=tf.placeholder(tf.float32,shape=[batch_size,gConfig['im_dim'], gConfig['im_dim'],gConfig['num_channels']],name='data_tensor')
self.label_tensor=tf.placeholder(tf.int32,shape=[batch_size],name='label_tensor')
keep_prop=tf.Variable(initial_value=0.5,name="keep_prop")
self.fc_result=create_CNN(input_data=self.data_tensor,num_classes=gConfig['num_dataset_classes'],keep_prop=gConfig['keeps'])
diff --git a/lessonSix/vgg16/app.py b/lessonSix/vgg16/app.py
new file mode 100644
index 0000000..7d8a266
--- /dev/null
+++ b/lessonSix/vgg16/app.py
@@ -0,0 +1,130 @@
+import flask
+import werkzeug
+import os
+import scipy.misc
+import tensorflow as tf
+import getConfig
+import execute
+
+gConfig = {}
+gConfig = getConfig.get_config(config_file='config.ini')
+
+# Creating a new Flask Web application. It accepts the package name.
+app = flask.Flask("imgClassifierWeb")
+
+
+def CNN_predict():
+ global sess
+ global model
+ global graph
+
+ """
+ global:
+ """
+ global secure_filename
+ # 从本地目录读取需要分类的图片
+ img = scipy.misc.imread(os.path.join(app.root_path, secure_filename))
+
+ """
+ 校验图片格式
+ """
+ if (img.ndim) == 3:
+ """
+ 是否为32*32
+ """
+ if img.shape[0] == img.shape[1] and img.shape[0] == 32:
+ """
+ 是否为3通道，GRB
+ """
+ if img.shape[-1] == 3:
+
+ predicted_class = execute.predict_line(sess, model, img, graph)
+ """
+ 将返回的结果用页面模板给渲染出来
+ """
+ return flask.render_template(template_name_or_list="prediction_result.html",
+ predicted_class=predicted_class)
+ else:
+ """ 如果检测出图片格式不符合要求，则返回错误并返回上传图片的格式"""
+ return flask.render_template(template_name_or_list="error.html", img_shape=img.shape)
+ else:
+ """ 如果检测出图片格式不符合要求，则返回错误并返回上传图片的格式"""
+ return flask.render_template(template_name_or_list="error.html", img_shape=img.shape)
+ return "遇到非图片格式的未知错误，请联系技术人员解决"
+
+
+"""
+flask路由系统：
+1、使用flask.Flask.route() 修饰器。
+2、使用flask.Flask.add_url_rule()函数。
+3、直接访问基于werkzeug路由系统的flask.Flask.url_map.
+参考知识链接：https://www.jianshu.com/p/e69016bd8f08
+1、@app.route('/index.html')
+ def index():
+ return "Hello World!"
+2、def index():
+ return "Hello World!"
+ index = app.route('/index.html')(index)
+app.add_url_rule:app.add_url_rule(rule,endpoint,view_func)
+关于rule、ednpoint、view_func以及函数注册路由的原理可以参考：https://www.cnblogs.com/eric-nirnava/p/endpoint.html
+"""
+app.add_url_rule(rule="/predict/", endpoint="predict", view_func=CNN_predict)
+"""
+知识点：
+flask.request属性
+form:
+一个从POST和PUT请求解析的 MultiDict（一键多值字典）。
+args:
+MultiDict，要操作 URL （如 ?key=value ）中提交的参数可以使用 args 属性:
+searchword = request.args.get('key', '')
+values:
+CombinedMultiDict，内容是form和args。
+可以使用values替代form和args。
+cookies:
+顾名思义，请求的cookies，类型是dict。
+stream:
+在可知的mimetype下，如果进来的表单数据无法解码，会没有任何改动的保存到这个·stream·以供使用。很多时候，当请求的数据转换为string时，使用data是最好的方式。这个stream只返回数据一次。
+headers:
+请求头，字典类型。
+data:
+包含了请求的数据，并转换为字符串，除非是一个Flask无法处理的mimetype。
+files:
+MultiDict，带有通过POST或PUT请求上传的文件。
+method:
+请求方法，比如POST、GET
+知识点参考链接：https://blog.csdn.net/yannanxiu/article/details/53116652
+werkzeug
+"""
+
+
+def upload_image():
+ global secure_filename
+ if flask.request.method == "POST": # 设置request的模式为POST
+ img_file = flask.request.files["image_file"] # 获取需要分类的图片
+ secure_filename = werkzeug.secure_filename(img_file.filename) # 生成一个没有乱码的文件名
+ img_path = os.path.join(app.root_path, secure_filename) # 获取图片的保存路径
+ img_file.save(img_path) # 将图片保存在应用的根目录下
+ print("图片上传成功.")
+ """
+
+ """
+ return flask.redirect(flask.url_for(endpoint="predict"))
+ return "图片上传失败"
+
+
+"""
+"""
+app.add_url_rule(rule="/upload/", endpoint="upload", view_func=upload_image, methods=["POST"])
+
+
+def redirect_upload():
+ return flask.render_template(template_name_or_list="upload_image.html")
+
+
+"""
+"""
+app.add_url_rule(rule="/", endpoint="homepage", view_func=redirect_upload)
+sess = tf.Session()
+sess, model, graph = execute.init_session(sess, conf='config.ini')
+if __name__ == "__main__":
+ app.run(host="localhost", port=7777, debug=False)
diff --git a/lessonSix/vgg16/config.ini b/lessonSix/vgg16/config.ini
new file mode 100644
index 0000000..a0c48d2
--- /dev/null
+++ b/lessonSix/vgg16/config.ini
@@ -0,0 +1,24 @@
+[strings]
+# Mode : train, test, serve
+mode = train
+working_directory = model/
+dataset_path=/Users/zhaoyingjun/Learning/TensorFlow_code/lessonOne/imgClassifierWeb/train_data/
+dataset_test=/Users/zhaoyingjun/Learning/TensorFlow_code/lessonOne/imgClassifierWeb/test_data/
+
+[ints]
+steps_per_checkpoint = 10
+num_dataset_classes=10
+dataset_size=50000
+im_dim=32
+num_channels = 3
+num_files=5
+images_per_file=10000
+max_gradient_norm=5
+
+[floats]
+learning_rate = 0.01
+keeps=0.5
+learning_rate_decay_factor = 0.9
+percent=1
+end_learning_rate=0.0
+
diff --git a/lessonSix/vgg16/execute.py b/lessonSix/vgg16/execute.py
new file mode 100644
index 0000000..63849a4
--- /dev/null
+++ b/lessonSix/vgg16/execute.py
@@ -0,0 +1,190 @@
+import tensorflow as tf
+import numpy as np
+import vggModel
+import os
+import pickle
+import time
+import getConfig
+import sys
+import random
+gConfig = {}
+
+def read_data(dataset_path, im_dim, num_channels,num_files,images_per_file):
+ files_names = os.listdir(dataset_path)
+ print(files_names)
+ # 获取训练集中训练文件的名称
+ """
+ 在CIFAR10中已经为我们标注和准备好了数据，一时找不到合适的高质量的标注训练集，我们就是使用CIFAR10的来作为我们的训练集。
+ 在训练集中一共有50000个训练样本，放到5个二进制文件中心，每个样本有3072个像素点，是32*3维度的
+ """
+ #创建空的多维数组用于存放图片二进制数据
+ dataset_array = np.zeros(shape=(num_files * images_per_file, im_dim, im_dim, num_channels))
+ # 创建空的数组用于存放图片的标注信息
+ dataset_labels = np.zeros(shape=(num_files * images_per_file), dtype=np.uint8)
+ index = 0
+ #从训练集中读取二进制数据并将其维度转换成224*224*3
+ for file_name in files_names:
+
+ if file_name[0:len(file_name)-1] == "data_batch_":
+ print("正在处理数据 : ", file_name)
+ data_dict = unpickle_patch(dataset_path + file_name)
+ images_data = data_dict[b"data"]
+ # 格式转换为224x224x3 shape.
+ print(images_data.shape)
+ images_data_reshaped = np.reshape(images_data,
+ newshape=(len(images_data), im_dim, im_dim, num_channels))
+ # 将维度转换后的图片数据存入指定数组内
+ dataset_array[index * images_per_file:(index + 1) * images_per_file, :, :, :] = images_data_reshaped
+ # 将维度转换后的标注数据存入指定数组内
+ dataset_labels[index * images_per_file:(index + 1) * images_per_file] = data_dict[b"labels"]
+ index = index + 1
+ return dataset_array, dataset_labels # 返回数据
+
+def unpickle_patch(file):
+ #打开文件，读取二进制文件，返回读取到的数据
+ patch_bin_file = open(file, 'rb')
+ patch_dict = pickle.load(patch_bin_file, encoding='bytes')#Loading the details of the binary file into a dictionary.
+ return patch_dict
+
+def create_model(session,forward_only):
+
+ #用vggModel实例化一个对象model
+ model=vggModel.vggModel(gConfig['percent'],gConfig['learning_rate'],gConfig['learning_rate_decay_factor'])
+ if 'pretrained_model'in gConfig:
+ model.saver.restore(session,gConfig['pretrained_model'])
+ return model
+ ckpt=tf.train.get_checkpoint_state(gConfig['working_directory'])
+ #判断是否已经有Model文件存在，如果model文件存在则加载原来的model并在原来的moldel继续训练，如果不存在则新建model相关文件
+ if ckpt and ckpt.model_checkpoint_path:
+ print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
+ model.saver.restore(session, ckpt.model_checkpoint_path)
+ session.run(tf.global_variables_initializer())
+ graph = tf.get_default_graph()
+
+ return model,graph
+
+ else:
+ print("Created model with fresh parameters.")
+ session.run(tf.global_variables_initializer())
+ graph = tf.get_default_graph()
+ return model,graph
+#获取批量处理数据，考虑到配置不同，如果没有GPU建议将percent调小一点，即将训练集调小
+def get_batch(data,labels,percent):
+ num_elements = np.uint32(percent * data.shape[0] / 100)
+ #之所以没有采用tf自带的tf.train.batch是因为那个接口对内存的消耗太大了，我们采用这种随机取index的方法简单又实用。
+ n=np.random.randint(len(labels),size=num_elements)
+ shuffle_datas=[]
+ shuffled_labels=[]
+ for i in n:
+ shuffle_datas.append(data[i,:,:,:])
+ shuffled_labels.append(labels[i])
+ return shuffle_datas,shuffled_labels
+
+#定义训练函数
+def train():
+ """使用BFC内存管理管理算法，tf.ConfigProto()用于GPU的管理，可以控制GPU的使用率
+ #allow growth
+ config = tf.ConfigProto()
+ config.gpu_options.allow_growth = True
+ # per_process_gpu_memory_fraction
+ gpu_options=tf.GPUOptions(per_process_gpu_memory_fraction=0.7)
+ config=tf.ConfigProto(gpu_options=gpu_options)
+ 关于BFC算法：
+ 将内存分块管理，按块进行空间分配和释放。
+ 通过split操作将大内存块分解成用户需要的小内存块。
+ 通过merge操作合并小的内存块，做到内存碎片回收
+ 通过bin这个抽象数据结构实现对空闲块高效管理。
+ """
+
+ config = tf.ConfigProto()
+ config.gpu_options.allocator_type = 'BFC'
+
+ #读取训练集的数据到内存中
+ dataset_array, dataset_labels = read_data(dataset_path=gConfig['dataset_path'], im_dim=gConfig['im_dim'],
+ num_channels=gConfig['num_channels'],num_files=gConfig['num_files'],images_per_file=gConfig['images_per_file'])
+ #打印训练数据的维度
+ print("Size of data : ", dataset_array.shape)
+ #读取测试数据到内存中
+ dataset_array_test, dataset_labels_test = read_data(dataset_path=gConfig['dataset_test'], im_dim=gConfig['im_dim'], num_channels=gConfig['num_channels'],num_files=1,images_per_file=gConfig['images_per_file'])
+ #打印测试数据的维度
+ print("Size of data : ", dataset_array_test.shape)
+
+ with tf.Session(config=config) as sess:
+ model,_=create_model(sess,False)
+
+ step_time, accuracy = 0.0, 0.0
+ current_step = 0
+ previous_correct = []
+ # 开始训练循环，这里没有设置结束条件，当学习率下降到为0时停止
+ while model.learning_rate.eval()>gConfig['end_learning_rate']:
+
+ shuffled_datas, shuffled_labels = get_batch(data=dataset_array, labels=dataset_labels,
+ percent=gConfig['percent'])
+ shuffled_datas_test, shuffled_labels_test = get_batch(data=dataset_array_test, labels=dataset_labels_test,
+ percent=gConfig['percent']*5)
+
+ start_time = time.time()
+ step_correct=model.step(sess,shuffled_datas,shuffled_labels,False)
+ step_time += (time.time() - start_time) / gConfig['steps_per_checkpoint']
+ accuracy += step_correct / gConfig['steps_per_checkpoint']
+ current_step += 1
+
+ # 达到一个训练模型保存点后，将模型保存下来，并打印出这个保存点的平均准确率
+ if current_step % gConfig['steps_per_checkpoint'] == 0:
+ #如果超过三次预测正确率没有升高则改变学习率
+ if len(previous_correct) > 2 and accuracy == min(previous_correct[-3:]):
+ sess.run(model.learning_rate_decay_op)
+ previous_correct.append(accuracy)
+ checkpoint_path = os.path.join(gConfig['working_directory'], "cnn.ckpt")
+ #saver=tf.train.Saver()
+ model.saver.save(sess, checkpoint_path,global_step=model.global_step)
+
+
+ #以下为增加模型在测试集上的准确率测试
+ graph = tf.get_default_graph()
+ #以下获取默认计算图的参数
+ softmax_propabilities = graph.get_tensor_by_name(name="softmax_probs:0")
+ softmax_predictions = tf.argmax(softmax_propabilities, axis=1)
+ data_tensor = graph.get_tensor_by_name(name="data_tensor:0")
+ label_tensor = graph.get_tensor_by_name(name="label_tensor:0")
+ keep_prop = graph.get_tensor_by_name(name="keep_prop:0")
+
+ feed_dict_testing = {data_tensor: shuffled_datas_test,
+ label_tensor: shuffled_labels_test,
+ keep_prop: 1.0}
+
+ softmax_propabilities_, softmax_predictions_ = sess.run([softmax_propabilities, softmax_predictions],
+ feed_dict=feed_dict_testing)
+
+ correct = np.array(np.where(softmax_predictions_ == shuffled_labels_test))
+ correct = correct.size
+ #打印出模型在训练集上的准确率
+ print("在", str(gConfig['percent'] *gConfig['dataset_size']/100),"个训练集上训练的准确率", ' : ', accuracy)
+ print("学习率 %.4f 每步耗时 %.2f " % ( model.learning_rate.eval(),step_time))
+ step_time, accuracy = 0.0,0.0
+
+ #打印出模型在测试集上的准确率
+ print("模型在测试集上的准确率为 : ", correct/(gConfig['percent']*gConfig['dataset_size']/100))
+
+ sys.stdout.flush()
+
+def init_session(sess,conf='config.ini'):
+ global gConfig
+ gConfig=getConfig.get_config(conf)
+ model,graph=create_model(sess,True)
+ return sess, model,graph
+
+def predict_line(sess,model,img,graph):
+ predict_name=model.step(sess,img,img,graph,True)
+ return predict_name
+
+if __name__=='__main__':
+ if len(sys.argv) - 1:
+ gConfig = getConfig(sys.argv[1])
+ else:
+ # get configuration from config.ini
+ gConfig = getConfig.get_config()
+ if gConfig['mode']=='train':
+ train()
+ elif gConfig['mode']=='server':
+ print('Sever Usage:python3 app.py')
\ No newline at end of file
diff --git a/lessonSix/vgg16/getConfig.py b/lessonSix/vgg16/getConfig.py
new file mode 100644
index 0000000..4421059
--- /dev/null
+++ b/lessonSix/vgg16/getConfig.py
@@ -0,0 +1,9 @@
+import configparser
+def get_config(config_file='config.ini'):
+ parser=configparser.ConfigParser()
+ parser.read(config_file)
+ # get the ints, floats and strings
+ _conf_ints = [(key, int(value)) for key, value in parser.items('ints')]
+ _conf_floats = [(key, float(value)) for key, value in parser.items('floats')]
+ _conf_strings = [(key, str(value)) for key, value in parser.items('strings')]
+ return dict(_conf_ints + _conf_floats + _conf_strings)
\ No newline at end of file
diff --git a/lessonSix/vgg16/predict_img/airplane.png b/lessonSix/vgg16/predict_img/airplane.png
new file mode 100755
index 0000000..8916132
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diff --git a/lessonSix/vgg16/predict_img/deer.png b/lessonSix/vgg16/predict_img/deer.png
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index 0000000..1e3af0f
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diff --git a/lessonSix/vgg16/predict_img/dog.png b/lessonSix/vgg16/predict_img/dog.png
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diff --git a/lessonSix/vgg16/predict_img/im.png b/lessonSix/vgg16/predict_img/im.png
new file mode 100755
index 0000000..0df1a2e
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diff --git a/lessonSix/vgg16/predict_img/truck.png b/lessonSix/vgg16/predict_img/truck.png
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diff --git a/lessonSix/vgg16/static/project_styles.css b/lessonSix/vgg16/static/project_styles.css
new file mode 100755
index 0000000..c109253
--- /dev/null
+++ b/lessonSix/vgg16/static/project_styles.css
@@ -0,0 +1,27 @@
+body{
+background-color: #c4d5f2;
+}
+
+.error{
+font-size:60px;
+}
+
+.error-msg{
+font-size:50px;
+color: red;
+}
+
+span{
+font-size: 40px;
+color: red;
+}
+
+h3{
+font-size: 30px;
+color: blue;
+}
+
+input{
+font-size: 30px;
+color: green;
+}
diff --git a/lessonSix/vgg16/static/result.js b/lessonSix/vgg16/static/result.js
new file mode 100755
index 0000000..6e2af05
--- /dev/null
+++ b/lessonSix/vgg16/static/result.js
@@ -0,0 +1,3 @@
+function show_alert(predicted_class){
+alert("图片类别预测结果为："+predicted_class+"。")
+}
diff --git a/lessonSix/vgg16/templates/error.html b/lessonSix/vgg16/templates/error.html
new file mode 100755
index 0000000..69b28e9
--- /dev/null
+++ b/lessonSix/vgg16/templates/error.html
@@ -0,0 +1,15 @@
+
+
+
+
+
+ Error
+
+
+
+ Error
+ 系统检测出您上传的图片与模型训练所用图片格式不一致，请重新上传.
+ Return to homepage.
+
+
+
diff --git a/lessonSix/vgg16/templates/prediction_result.html b/lessonSix/vgg16/templates/prediction_result.html
new file mode 100755
index 0000000..2948042
--- /dev/null
+++ b/lessonSix/vgg16/templates/prediction_result.html
@@ -0,0 +1,15 @@
+
+
+
+
+
+
+ 图片分类预测结果
+
+
+图片里是 : {{predicted_class}}
+
+ 返回首页.
+
+
+
\ No newline at end of file
diff --git a/lessonSix/vgg16/templates/upload_image.html b/lessonSix/vgg16/templates/upload_image.html
new file mode 100755
index 0000000..1a0cee0
--- /dev/null
+++ b/lessonSix/vgg16/templates/upload_image.html
@@ -0,0 +1,17 @@
+
+
+
+
+
+ 上传图片
+
+
+
+
+
\ No newline at end of file
diff --git a/lessonSix/vgg16/to_cifar10.py b/lessonSix/vgg16/to_cifar10.py
new file mode 100644
index 0000000..1f90bf9
--- /dev/null
+++ b/lessonSix/vgg16/to_cifar10.py
@@ -0,0 +1,74 @@
+# -*- coding:utf-8 -*-
+import pickle,pprint
+from PIL import Image
+import numpy as np
+import os
+
+class DictSave(object):
+ def __init__(self,filenames,file):
+ self.filenames = filenames
+ self.file=file
+ self.arr = []
+ self.all_arr = []
+ self.label=[]
+
+
+ def image_input(self,filenames,file):
+ i=0
+ for filename in filenames:
+ self.arr,self.label = self.read_file(filename,file)
+ if self.all_arr==[]:
+ self.all_arr = self.arr
+ else:
+ self.all_arr = np.concatenate((self.all_arr,self.arr))
+
+ print(i)
+ i=i+1
+ def read_file(self,filename,file):
+
+ im = Image.open(filename)#打开一个图像
+ # 将图像的RGB分离
+ r, g, b = im.split()
+ # 将PILLOW图像转成数组
+ r_arr = plimg.pil_to_array(r)
+ g_arr = plimg.pil_to_array(g)
+ b_arr = plimg.pil_to_array(b)
+
+ # 将60*180二维数组转成1024的一维数组
+ #r_arr1 = r_arr.reshape(10800)
+ #g_arr1 = g_arr.reshape(10800)
+ #b_arr1 = b_arr.reshape(10800)
+ # 3个一维数组合并成一个一维数组,大小为32400
+ arr = np.concatenate((r_arr, g_arr, b_arr))
+ #print(file)
+ #label=file[0]
+ label=[]
+ for i in file:
+ #print(i[0])
+ label.append(i[0])
+ #print (label)
+ return arr,label
+ def pickle_save(self,arr,label):
+ print ("正在存储")
+ # 构造字典,所有的图像数据都在arr数组里,这里只存图像数据,没有存label
+ contact = {'data': arr,'label':label}
+ f = open('data_batch', 'wb')
+
+ pickle.dump(contact, f)#把字典存到文本中去
+ f.close()
+ print ("存储完毕")
+if __name__ == "__main__":
+ file_dir='train_data'
+ L=[]
+ F=[]
+ for root,dirs,files in os.walk(file_dir):
+ for file in files:
+ if os.path.splitext(file)[1] == '.jpg':
+ L.append(os.path.join(root, file))
+ F.append(file)
+
+ ds = DictSave(L,F)
+ ds.image_input(ds.filenames,ds.file)
+ print(ds.all_arr)
+ ds.pickle_save(ds.all_arr,ds.label)
+ print ("最终数组的大小:"+str(ds.all_arr.shape))
\ No newline at end of file
diff --git a/lessonSix/vgg16/to_jpg.py b/lessonSix/vgg16/to_jpg.py
new file mode 100644
index 0000000..8dae6f2
--- /dev/null
+++ b/lessonSix/vgg16/to_jpg.py
@@ -0,0 +1,76 @@
+#coding=utf-8
+import cv2
+import numpy as np
+import os
+import pickle
+
+
+#文件夹名
+str_2 = 'train_data'
+str_1 = 'test_data'
+
+#判断文件夹是否存在，不存在的话创建文件夹
+if os.path.exists(str_1) == False:
+ os.mkdir(str_1)
+if os.path.exists(str_2) == False:
+ os.mkdir(str_2)
+
+# 解压缩，返回解压后的字典，f,encoding='bytes'
+def unpickle(file):
+
+ fo = open(file, 'rb')
+ dict = pickle.load(fo, encoding='bytes')
+ fo.close()
+ return dict
+
+def cifar_jpg(dir_file):
+# 生成训练集图片，如果需要png格式，只需要改图片后缀名即可。
+ for j in range(1, 6):
+ dataName = dir_file + '/' + "data_batch_" + str(j) # 读取当前目录下的data_batch12345文件，dataName其实也是data_batch文件的路径，本文和脚本文件在同一目录下。
+ Xtr = unpickle(dataName)
+ print(Xtr)
+ print(dataName + " is loading...")
+
+ for i in range(0, 10000):
+ img = np.reshape(Xtr[b'data'][i], (3, 32, 32))
+ img = img.transpose(1, 2, 0) # 读取image
+ picName = 'train_data/' + str(Xtr[b'labels'][i]) + str(i + (j - 1) * 10000) + '.jpg' # Xtr['labels']为图片的标签，值范围0-9，本文中，train文件夹需要存在，并与脚本文件在同一目录下。
+ cv2.imwrite(picName, img)
+ print(dataName + " loaded.")
+
+ print("test_batch is loading...")
+
+# 生成测试集图片
+ testName = dir_file + '/' + 'data_batch_6'
+ testXtr = unpickle(testName)
+ for i in range(0, 10000):
+ img = np.reshape(testXtr[b'data'][i], (3, 32, 32))
+ img = img.transpose(1, 2, 0)
+ picName = 'test_data/' + str(testXtr[b'labels'][i]) + str(i) + '.jpg'
+ cv2.imwrite(picName, img)
+ print("test_batch loaded.")
+
+
+#标签与名字的对应关系
+def label_name():
+ label_name_dict ={
+ 'airplane': "0",
+ 'automobile': "1",
+ 'bird': "2",
+ 'cat': "3",
+ 'deer': "4",
+ 'dog': "5",
+ 'frog': "6",
+ 'horse': "7",
+ 'ship': "8",
+ 'truck': "9"
+ }
+ return label_name_dict
+
+if __name__ == '__main__':
+ dir_file = 'train_data'
+ #cifar_jpg(dir_file)
+try:
+ cifar_jpg(dir_file)
+except:
+ print('出错了')
diff --git a/lessonSix/vgg16/train_data/batches.meta b/lessonSix/vgg16/train_data/batches.meta
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index 0000000..4467a6e
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diff --git a/lessonSix/vgg16/train_data/data_batch_1 b/lessonSix/vgg16/train_data/data_batch_1
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index 0000000..ab404a5
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diff --git a/lessonSix/vgg16/train_data/data_batch_2 b/lessonSix/vgg16/train_data/data_batch_2
new file mode 100644
index 0000000..6bf1369
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diff --git a/lessonSix/vgg16/train_data/data_batch_3 b/lessonSix/vgg16/train_data/data_batch_3
new file mode 100644
index 0000000..66a0d63
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diff --git a/lessonSix/vgg16/train_data/data_batch_4 b/lessonSix/vgg16/train_data/data_batch_4
new file mode 100644
index 0000000..cf8d03d
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diff --git a/lessonSix/vgg16/train_data/data_batch_5 b/lessonSix/vgg16/train_data/data_batch_5
new file mode 100644
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diff --git a/lessonSix/vgg16/vggModel.py b/lessonSix/vgg16/vggModel.py
new file mode 100644
index 0000000..483a000
--- /dev/null
+++ b/lessonSix/vgg16/vggModel.py
@@ -0,0 +1,166 @@
+"""
+
+"""
+import tensorflow as tf
+import numpy as np
+import pickle
+import getConfig
+from collections import Counter
+
+import tensorflow.contrib.slim as slim
+
+
+gConfig={}
+gConfig=getConfig.get_config(config_file='config.ini')
+
+"""
+vgg16使用tf.contrin.slim大大简化了代码，如果按照传统的写法写vgg16那可能要写死人，现在我们
+只要30行以内的代码就可以完成一个vgg16 的构建。
+
+因此这里我们重点将vgg16的代码结构
+
+
+知识点：
+tf.truncated_normal_initializer
+
+l2_regularizer
+
+
+
+"""
+
+class vggModel(object):
+ def __init__(self,percent,learning_rate,learning_rate_decay_factor):
+ self.percent=percent
+ self.learning_rate=tf.Variable(float(learning_rate), trainable=False)
+ self.learning_rate_decay_op = self.learning_rate.assign(self.learning_rate * learning_rate_decay_factor)
+ self.global_step = tf.Variable(0, trainable=False)
+
+ def vgg16Model(input_data, num_classes,keep_prob):
+
+ with slim.arg_scope([slim.conv2d, slim.fully_connected],
+ activation_fn=tf.nn.relu,
+ weights_initializer=tf.truncated_normal_initializer(0.0, 0.01),
+ weights_regularizer=slim.l2_regularizer(0.0005)):
+ #第一段卷积堆叠，两层堆叠，conv3-64的意义是3*3的卷积核一共64个
+ net = slim.repeat(input_data, 2, slim.conv2d, 64, [3, 3], scope='conv1')
+ net = slim.max_pool2d(net, [2, 2], scope='pool1')
+ #第二段卷积核堆叠，两层堆叠，128个3*3的卷积核，注意这里和感受野的区别
+ net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
+ net = slim.max_pool2d(net, [2, 2], scope='pool2')
+ #第三段卷积核堆叠，三层堆叠，256个3*3的卷积核，注意这里和感受野的区别
+ net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
+ net = slim.max_pool2d(net, [2, 2], scope='pool3')
+ #第四段卷积核堆叠，三层堆叠，512个3*3的卷积核，注意这里和感受野的区别
+ net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4')
+ net = slim.max_pool2d(net, [2, 2], scope='pool4')
+ #第五段卷积核堆叠，三层堆叠，512个3*3的卷积核，注意这里和感受野的区别
+ net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')
+ net = slim.max_pool2d(net, [2, 2], scope='pool5')
+ #Flatten层用来将输入“压平”，即把多维的输入一维化，常用在从卷积层到全连接层的过渡。Flatten不影响batch的大小。
+ net = slim.flatten(net, scope='flat')
+ #全连接层
+ net = slim.fully_connected(net, 4096, scope='fc1')
+ net = slim.dropout(net, keep_prob=keep_prob, scope='dropt1')
+ #全连接层
+ net = slim.fully_connected(net, 4096, scope='fc2')
+ net = slim.dropout(net, keep_prob=keep_prob, scope='dropt2')
+ #全连接层
+ net = slim.fully_connected(net, num_classes, scope='fc3')
+ net = slim.softmax(net, scope='net')
+
+ return net
+
+ #这块内容和第一课的内容是一样的
+ batch_size=gConfig['percent']*gConfig['dataset_size']/100
+ self.data_tensor=tf.placeholder(tf.float32,shape=[batch_size,gConfig['im_dim'], gConfig['im_dim'],gConfig['num_channels']],name='data_tensor')
+ self.label_tensor=tf.placeholder(tf.int32,shape=[batch_size],name='label_tensor')
+ keep_prob=tf.Variable(initial_value=0.5,name="keep_prob")
+ self.fc_result=vgg16Model(input_data=self.data_tensor,num_classes=gConfig['num_dataset_classes'],keep_prob=gConfig['keeps'])
+ self.softmax_propabilities=tf.nn.softmax(self.fc_result,name="softmax_probs")
+ self.softmax_predictions=tf.argmax(self.softmax_propabilities,axis=1)
+
+ #将label变成one-hot编码，因为softmax_propabilities是一个数组，是10个概率，每个概率代表着预测结果属于其index类的概率，为了计算交叉熵，我们需要把label也转换成一个数组
+ self.label_tensor=tf.one_hot(int(batch_size),self.label_tensor,10)
+ #计算交叉熵
+ cross_entropy=tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.softmax_propabilities,
+ labels=self.label_tensor)
+ cost=tf.reduce_mean(cross_entropy)
+
+ self.ops=tf.train.GradientDescentOptimizer(self.learning_rate).minimize(cost,global_step=self.global_step)
+ #保存所有变量的
+ sess=tf.Session()
+ sess.run(tf.global_variables_initializer())
+ self.saver = tf.train.Saver(tf.all_variables())
+
+ def step(self,sess,shuffled_data,shuffled_labels,graph,forward_only=None):
+
+ keep_prob = tf.placeholder(tf.float32)
+ gConfig=getConfig.get_config(config_file='config.ini')
+ #是否只进行正向传播，及正向传播是进行预测，反向传播是进行训练
+ if forward_only:
+ keep_prob = graph.get_tensor_by_name(name="keep_prob:0")
+ data_tensor=graph.get_tensor_by_name(name="data_tensor:0")
+ k_size=gConfig['percent']*gConfig['dataset_size']/100
+ dataset_array = np.random.rand(int(k_size), 32, 32, 3)
+ dataset_array[0,:,:,:] = shuffled_data
+ print(shuffled_data)
+ print(dataset_array[0])
+ print(dataset_array)
+ feed_dict_test={data_tensor:dataset_array,keep_prob:1.0
+ }
+ softmax_propabilities_, softmax_predictions_ = sess.run([self.softmax_propabilities, self.softmax_predictions],
+ feed_dict=feed_dict_test)
+
+ file=gConfig['dataset_path'] + "batches.meta"
+ patch_bin_file = open(file, 'rb')
+ label_names_dict = pickle.load(patch_bin_file)
+ print(label_names_dict)
+ print(softmax_predictions_[0])
+ print(softmax_predictions_)
+ print(Counter(softmax_predictions_).most_common(1))
+
+ #k=Counter(softmax_predictions_).most_common(1)
+ #print(k)
+ dataset_label_names = label_names_dict["label_names"]
+ return dataset_label_names[softmax_predictions_[0]]
+ else:
+
+ cnn_feed_dict = {self.data_tensor: shuffled_data, self.label_tensor: shuffled_labels, keep_prob: gConfig['keeps']}
+ softmax_predictions_, _ = sess.run([self.softmax_predictions, self.ops],feed_dict=cnn_feed_dict)
+ # 统计预测争取的数量
+ correct = np.array(np.where(softmax_predictions_ == shuffled_labels))
+
+ accuracy = correct.size/(self.percent*gConfig['dataset_size']/100)
+ return accuracy #输出准确率
+
+ """
+ 准确率（accuracy）： 正确预测占全部样本的比例
+
+ 精准率（precision）：正确预测为正占全部预测为正的比例
+
+ 召回率（recall）： 正确预测为正占全部正样本的比例
+
+
+ """
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+