Open margaretmm opened 6 years ago
""" 卷积神经网络 Inception-v3模型 迁移学习 """ import glob import os.path import platform import random import numpy as np import tensorflow as tf from tensorflow.python.platform import gfile from tensorflow.python.framework import graph_util
BOTTLENECK_TENSOR_SIZE = 2048
BOTTLENECK_TENSOR_NAME = 'pool_3/_reshape:0'
JPEG_DATA_TENSOR_NAME = 'DecodeJpeg/contents:0'
MODEL_FILE = 'classify_image_graph_def.pb' TRAINED_MODEL_FILE='trained.pb'
if platform.system() in ['Linux']:
MODEL_DIR = 'model/' TRAINED_MODEL_DIR='modelTrained/' # 因为一个训练数据会被使用多次，所以可以将原始图像通过Inception-v3模型计算得到的特征向量保存在文件中，免去重复的计算。 # 下载的谷歌训练好的Inception-v3模型文件目录 CACHE_DIR = 'bottleneck/' # 在这个文件夹中每一个子文件夹代表一个需要区分的类别，每个子文件夹中存放了对应类别的图片。 INPUT_DATA='data/train/'
else: MODEL_DIR = 'D:\05_PycharmProjects\test\inceptionV3\model\' TRAINED_MODEL_DIR='D:\05_PycharmProjects\test\inceptionV3\modelTrained\'
CACHE_DIR = 'D:\\05_PycharmProjects\\test\\inceptionV3\\bottleneck\\' INPUT_DATA = 'D:\\05_PycharmProjects\\test\\inceptionV3\\data\\train\\' #'D:\\05_PycharmProjects\\test\\OpenCV\\images\\'
VALIDATION_PERCENTAGE = 10
TEST_PERCENTACE = 10
LEARNING_RATE = 0.01 STEPS = 1000 BATCH = 100
def create_image_lists(testing_percentage, validation_percentage): """ 这个函数把数据集分成训练，验证，测试三部分 :param testing_percentage:测试的数据百分比 10 :param validation_percentage:验证的数据百分比 10 :return: """ result = {}
sub_dirs = [x[0] for x in os.walk(INPUT_DATA)] # ['/path/to/flower_data', '/path/to/flower_data\\daisy', '/path/to/flower_data\\dandelion', # '/path/to/flower_data\\roses', '/path/to/flower_data\\sunflowers', '/path/to/flower_data\\tulips'] # 数组中的第一个目录是当前目录，这里设置标记，不予处理 is_root_dir = True for sub_dir in sub_dirs: # 遍历目录数组，每次处理一种 if is_root_dir: is_root_dir = False continue # 获取当前目录下所有的有效图片文件 extensions = ['png','PNG','jpg', 'jepg', 'JPG', 'JPEG'] file_list = [] dir_name = os.path.basename(sub_dir) # 返回路径名路径的基本名称，如：daisy|dandelion|roses|sunflowers|tulips for extension in extensions: file_glob = os.path.join(INPUT_DATA, dir_name, '*.' + extension) # 将多个路径组合后返回 file_list.extend(glob.glob(file_glob)) # glob.glob返回所有匹配的文件路径列表，extend往列表中追加另一个列表 if not file_list: continue # 通过目录名获取类别名称 label_name = dir_name.lower() # 返回其小写 # 初始化当前类别的训练数据集、测试数据集、验证数据集 training_images = [] testing_images = [] validation_images = [] for file_name in file_list: # 遍历此类图片的每张图片的路径 base_name = os.path.basename(file_name) # 路径的基本名称也就是图片的名称，如：102841525_bd6628ae3c.jpg # 随机讲数据分到训练数据集、测试集和验证集 chance = np.random.randint(100) if chance < validation_percentage: validation_images.append(base_name) elif chance < (testing_percentage + validation_percentage): testing_images.append(base_name) else: training_images.append(base_name) result[label_name] = { 'dir': dir_name, 'training': training_images, 'testing': testing_images, 'validation': validation_images } return result
def get_image_path(image_lists, image_dir, label_name, index, category): """ :param image_lists:所有图片信息 :param image_dir:根目录 （ 图片特征向量根目录 CACHE_DIR | 图片原始路径根目录 INPUT_DATA ） :param label_name:类别的名称（ daisy|dandelion|roses|sunflowers|tulips ） :param index:编号 :param category:所属的数据集（ training|testing|validation ） :return: 一张图片的地址 """
label_lists = image_lists[label_name] # 获取这种类别的图片中，特定的数据集(base_name的一维数组) category_list = label_lists[category] #print("label_name:",label_name," category:",category) try: mod_index = index % len(category_list) # 图片的编号%此数据集中图片数量 except Exception as e: print(Exception,":",e) # 获取图片文件名 base_name = category_list[mod_index] sub_dir = label_lists['dir'] # 拼接地址 full_path = os.path.join(image_dir, sub_dir, base_name) return full_path
def get_bottleneck_path(image_lists, label_name, index, category): return get_image_path(image_lists, CACHE_DIR, label_name, index, category) + '.txt' # CACHE_DIR 特征向量的根地址
def run_bottleneck_on_image(sess, image_data, image_data_tensor, bottleneck_tensor): """ :param sess: :param image_data:图片内容 :param image_data_tensor: :param bottleneck_tensor: :return: """ bottleneck_values = sess.run(bottleneck_tensor, {image_data_tensor: image_data}) bottleneck_values = np.squeeze(bottleneck_values) return bottleneck_values
def get_or_create_bottleneck(sess, image_lists, label_name, index, category, jpeg_data_tensor, bottleneck_tensor): """ :param sess: :param image_lists: :param label_name:类别名 :param index:图片编号 :param category: :param jpeg_data_tensor: :param bottleneck_tensor: :return: """ label_lists = image_lists[label_name] sub_dir = label_lists['dir'] sub_dir_path = os.path.join(CACHE_DIR, sub_dir) # 到类别的文件夹 if not os.path.exists(sub_dir_path): os.makedirs(sub_dir_path)
bottleneck_path = get_bottleneck_path(image_lists, label_name, index, category) # 获取图片特征向量的路径 if not os.path.exists(bottleneck_path): # 如果不存在 # 获取图片原始路径 image_path = get_image_path(image_lists, INPUT_DATA, label_name, index, category) # 获取图片内容 image_data = gfile.FastGFile(image_path, 'rb').read() # 计算图片特征向量 bottleneck_values = run_bottleneck_on_image(sess, image_data, jpeg_data_tensor, bottleneck_tensor) # 将特征向量存储到文件 bottleneck_string = ','.join(str(x) for x in bottleneck_values) with open(bottleneck_path, 'w') as bottleneck_file: bottleneck_file.write(bottleneck_string) else: # 读取保存的特征向量文件 with open(bottleneck_path, 'r') as bottleneck_file: bottleneck_string = bottleneck_file.read() # 字符串转float数组 bottleneck_values = [float(x) for x in bottleneck_string.split(',')] return bottleneck_values
def get_random_cached_bottlenecks(sess, n_classes, image_lists, how_many, category, jpeg_data_tensor, bottleneck_tensor): """ :param sess: :param n_classes: 类别数量 :param image_lists: :param how_many: 一个batch的数量 :param category: 所属的数据集 :param jpeg_data_tensor: :param bottleneck_tensor: :return: 特征向量列表，类别列表 """ bottlenecks = [] groundtruths = [] for in range(how_many):
label_index = random.randrange(n_classes) label_name = list(image_lists.keys())[label_index] # 随机图片的类别名 image_index = random.randrange(65536) # 随机图片的编号 bottleneck = get_or_create_bottleneck(sess, image_lists, label_name, image_index, category, jpeg_data_tensor, bottleneck_tensor) # 计算此图片的特征向量 ground_truth = np.zeros(n_classes, dtype=np.float32) ground_truth[label_index] = 1.0 bottlenecks.append(bottleneck) ground_truths.append(ground_truth) return bottlenecks, ground_truths
def get_test_bottlenecks(sess, image_lists, n_classes, jpeg_data_tensor, bottleneck_tensor): bottlenecks = [] ground_truths = [] label_name_list = list(image_lists.keys()) # ['dandelion', 'daisy', 'sunflowers', 'roses', 'tulips'] for label_index, label_name in enumerate(label_name_list): # 枚举每个类别,如:0 sunflowers category = 'testing' for index, unused_base_name in enumerate(image_lists[label_name][category]): # 枚举此类别中的测试数据集中的每张图片
bottleneck = get_or_create_bottleneck( sess, image_lists, label_name, index, category, jpeg_data_tensor, bottleneck_tensor) ground_truth = np.zeros(n_classes, dtype=np.float32) ground_truth[label_index] = 1.0 bottlenecks.append(bottleneck) ground_truths.append(ground_truth) return bottlenecks, ground_truths
def create_inception_graph(): with tf.Graph().as_default() as graph: model_filename = os.path.join( MODEL_DIR, MODEL_FILE) with gfile.FastGFile(model_filename, 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) bottleneck_tensor, jpeg_data_tensor = tf.import_graph_def(graph_def, name='', return_elements=[ BOTTLENECK_TENSOR_NAME, JPEG_DATA_TENSOR_NAME]) return graph, bottleneck_tensor, jpeg_data_tensor
def add_final_training_ops(class_count, bottleneck_tensor):
bottleneck_input = tf.placeholder_with_default(bottleneck_tensor, [None, BOTTLENECK_TENSOR_SIZE], name='BottleneckInputPlaceholder') ground_truth_input = tf.placeholder(tf.float32, [None, class_count], name='GroundTruthInput') # 全连接层 with tf.name_scope('output'): weights = tf.Variable(tf.truncated_normal([BOTTLENECK_TENSOR_SIZE, class_count], stddev=0.001)) biases = tf.Variable(tf.zeros([class_count])) logits = tf.matmul(bottleneck_input, weights) + biases final_tensor = tf.nn.softmax(logits, name='prob') # 损失 cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=ground_truth_input) cross_entropy_mean = tf.reduce_mean(cross_entropy) train_step = tf.train.GradientDescentOptimizer(LEARNING_RATE).minimize(cross_entropy_mean) # 正确率 with tf.name_scope('evaluation'): correct_prediction = tf.equal(tf.argmax(final_tensor, 1), tf.argmax(ground_truth_input, 1)) evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) return (train_step, evaluation_step, cross_entropy_mean, bottleneck_input, ground_truth_input)
def train(_): image_lists = create_image_lists(TEST_PERCENTACE, VALIDATION_PERCENTAGE) n_classes = len(image_lists.keys()) print('n_classes：', n_classes)
graph, bottleneck_tensor, jpeg_data_tensor = create_inception_graph() print(bottleneck_tensor.graph is tf.get_default_graph()) with tf.Session(graph=graph) as sess: train_step, evaluation_step, cross_entropy_mean, bottleneck_input, ground_truth_input = add_final_training_ops( n_classes, bottleneck_tensor) # 初始化参数 init = tf.global_variables_initializer() sess.run(init) for i in range(STEPS): # 每次获取一个batch的训练数据 train_bottlenecks, train_ground_truth = get_random_cached_bottlenecks(sess, n_classes, image_lists, BATCH, 'training', jpeg_data_tensor, bottleneck_tensor) # 训练 sess.run(train_step, feed_dict={bottleneck_input: train_bottlenecks, ground_truth_input: train_ground_truth}) # 验证 if i % 100 == 0 or i + 1 == STEPS: validation_bottlenecks, validation_ground_truth = get_random_cached_bottlenecks(sess, n_classes, image_lists, BATCH, 'validation', jpeg_data_tensor, bottleneck_tensor) validation_accuracy = sess.run(evaluation_step, feed_dict={bottleneck_input: validation_bottlenecks, ground_truth_input: validation_ground_truth}) print('Step %d: Validation accuracy on random sampled %d examples = %.1f%%' % ( i, BATCH, validation_accuracy * 100)) # 测试 test_bottlenecks, test_ground_truth = get_test_bottlenecks(sess, image_lists, n_classes, jpeg_data_tensor, bottleneck_tensor) test_accuracy = sess.run(evaluation_step, feed_dict={bottleneck_input: test_bottlenecks, ground_truth_input: test_ground_truth}) print('Final test accuracy = %.1f%%' % (test_accuracy * 100)) constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def, ["output/prob"]) with tf.gfile.FastGFile(TRAINED_MODEL_DIR+TRAINED_MODEL_FILE, mode='wb') as f: f.write(constant_graph.SerializeToString())
if name == 'main': tf.app.run(train)
-- coding: utf-8 --
""" 卷积神经网络 Inception-v3模型 迁移学习 """ import glob import os.path import platform import random import numpy as np import tensorflow as tf from tensorflow.python.platform import gfile from tensorflow.python.framework import graph_util
inception-v3 模型瓶颈层的节点个数
BOTTLENECK_TENSOR_SIZE = 2048
inception-v3 模型中代表瓶颈层结果的张量名称
BOTTLENECK_TENSOR_NAME = 'pool_3/_reshape:0'
图像输入张量所对应的名称
JPEG_DATA_TENSOR_NAME = 'DecodeJpeg/contents:0'
下载的谷歌训练好的inception-v3模型文件目录
MODEL_DIR = './inceptionV3'
下载的谷歌训练好的inception-v3模型文件名
MODEL_FILE = 'classify_image_graph_def.pb' TRAINED_MODEL_FILE='trained.pb'
if platform.system() in ['Linux']:
下载的谷歌训练好的Inception-v3模型文件目录
else: MODEL_DIR = 'D:\05_PycharmProjects\test\inceptionV3\model\' TRAINED_MODEL_DIR='D:\05_PycharmProjects\test\inceptionV3\modelTrained\'
下面的变量定义了这些文件的存放地址。
验证的数据百分比
VALIDATION_PERCENTAGE = 10
测试的数据百分比
TEST_PERCENTACE = 10
定义神经网路的设置
LEARNING_RATE = 0.01 STEPS = 1000 BATCH = 100
这个函数把数据集分成训练，验证，测试三部分
def create_image_lists(testing_percentage, validation_percentage): """ 这个函数把数据集分成训练，验证，测试三部分 :param testing_percentage:测试的数据百分比 10 :param validation_percentage:验证的数据百分比 10 :return: """ result = {}
获取目录下所有子目录
这个函数通过类别名称、所属数据集和图片编号获取一张图片的地址
def get_image_path(image_lists, image_dir, label_name, index, category): """ :param image_lists:所有图片信息 :param image_dir:根目录 （ 图片特征向量根目录 CACHE_DIR | 图片原始路径根目录 INPUT_DATA ） :param label_name:类别的名称（ daisy|dandelion|roses|sunflowers|tulips ） :param index:编号 :param category:所属的数据集（ training|testing|validation ） :return: 一张图片的地址 """
获取给定类别的图片集合
图片的特征向量的文件地址
def get_bottleneck_path(image_lists, label_name, index, category): return get_image_path(image_lists, CACHE_DIR, label_name, index, category) + '.txt' # CACHE_DIR 特征向量的根地址
计算特征向量
def run_bottleneck_on_image(sess, image_data, image_data_tensor, bottleneck_tensor): """ :param sess: :param image_data:图片内容 :param image_data_tensor: :param bottleneck_tensor: :return: """ bottleneck_values = sess.run(bottleneck_tensor, {image_data_tensor: image_data}) bottleneck_values = np.squeeze(bottleneck_values) return bottleneck_values
获取一张图片对应的特征向量的路径
def get_or_create_bottleneck(sess, image_lists, label_name, index, category, jpeg_data_tensor, bottleneck_tensor): """ :param sess: :param image_lists: :param label_name:类别名 :param index:图片编号 :param category: :param jpeg_data_tensor: :param bottleneck_tensor: :return: """ label_lists = image_lists[label_name] sub_dir = label_lists['dir'] sub_dir_path = os.path.join(CACHE_DIR, sub_dir) # 到类别的文件夹 if not os.path.exists(sub_dir_path): os.makedirs(sub_dir_path)
随机获取一个batch的图片作为训练数据（特征向量，类别）
def get_random_cached_bottlenecks(sess, n_classes, image_lists, how_many, category, jpeg_data_tensor, bottleneck_tensor): """ :param sess: :param n_classes: 类别数量 :param image_lists: :param how_many: 一个batch的数量 :param category: 所属的数据集 :param jpeg_data_tensor: :param bottleneck_tensor: :return: 特征向量列表，类别列表 """ bottlenecks = [] groundtruths = [] for in range(how_many):
随机一个类别和图片编号加入当前的训练数据
获取全部的测试数据
def get_test_bottlenecks(sess, image_lists, n_classes, jpeg_data_tensor, bottleneck_tensor): bottlenecks = [] ground_truths = [] label_name_list = list(image_lists.keys()) # ['dandelion', 'daisy', 'sunflowers', 'roses', 'tulips'] for label_index, label_name in enumerate(label_name_list): # 枚举每个类别,如:0 sunflowers category = 'testing' for index, unused_base_name in enumerate(image_lists[label_name][category]): # 枚举此类别中的测试数据集中的每张图片
def create_inception_graph(): with tf.Graph().as_default() as graph: model_filename = os.path.join( MODEL_DIR, MODEL_FILE) with gfile.FastGFile(model_filename, 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) bottleneck_tensor, jpeg_data_tensor = tf.import_graph_def(graph_def, name='', return_elements=[ BOTTLENECK_TENSOR_NAME, JPEG_DATA_TENSOR_NAME]) return graph, bottleneck_tensor, jpeg_data_tensor
def add_final_training_ops(class_count, bottleneck_tensor):
输入
def train(_): image_lists = create_image_lists(TEST_PERCENTACE, VALIDATION_PERCENTAGE) n_classes = len(image_lists.keys()) print('n_classes：', n_classes)
if name == 'main': tf.app.run(train)