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import globimport os.pathimport randomimport numpy as npimport tensorflow as tffrom tensorflow.python.platform import gfile# 数据参数MODEL_DIR = 'model/'  # inception-v3模型的文件夹MODEL_FILE = 'tensorflow_inception_graph.pb'  # inception-v3模型文件名CACHE_DIR = 'data/tmp/bottleneck'  # 图像的特征向量保存地址INPUT_DATA = 'data/image'  # 图片数据文件夹VALIDATION_PERCENTAGE = 10  # 验证数据的百分比TEST_PERCENTAGE = 20  # 测试数据的百分比# inception-v3模型参数BOTTLENECK_TENSOR_SIZE = 2048  # inception-v3模型瓶颈层的节点个数BOTTLENECK_TENSOR_NAME = 'pool_3/_reshape:0'  # inception-v3模型中代表瓶颈层结果的张量名称JPEG_DATA_TENSOR_NAME = 'DecodeJpeg/contents:0'  # 图像输入张量对应的名称# 神经网络的训练参数LEARNING_RATE = 0.02STEPS = 1500 #原来 学习率 0.01 步数 1000步BATCH = 100CHECKPOINT_EVERY = 100NUM_CHECKPOINTS = 15# 从数据文件夹中读取所有的图片列表并按训练、验证、测试分开def create_image_lists(validation_percentage, test_percentage):    result = {}  # 保存所有图像。key为类别名称。value也是字典，存储了所有的图片名称    sub_dirs = [x[0] for x in os.walk(INPUT_DATA)]  # 获取所有子目录    is_root_dir = True  # 第一个目录为当前目录，需要忽略    # 分别对每个子目录进行操作    for sub_dir in sub_dirs:        if is_root_dir:            is_root_dir = False            continue        # 获取当前目录下的所有有效图片        extensions = {'jpg', 'jpeg', 'JPG', 'JPEG'}        file_list = []  # 存储所有图像        dir_name = os.path.basename(sub_dir)  # 获取路径的最后一个目录名字        for extension in extensions:            file_glob = os.path.join(INPUT_DATA, dir_name, '*.' + extension)            file_list.extend(glob.glob(file_glob))        if not file_list:            continue        # 将当前类别的图片随机分为训练数据集、测试数据集、验证数据集        label_name = dir_name.lower()  # 通过目录名获取类别的名称        training_images = []        testing_images = []        validation_images = []        for file_name in file_list:            base_name = os.path.basename(file_name)  # 获取该图片的名称            chance = np.random.randint(100)  # 随机产生100个数代表百分比            if chance < validation_percentage:                validation_images.append(base_name)            elif chance < (validation_percentage + test_percentage):                testing_images.append(base_name)            else:                training_images.append(base_name)        # 将当前类别的数据集放入结果字典        result[label_name] = {            'dir': dir_name,            'training': training_images,            'testing': testing_images,            'validation': validation_images        }    # 返回整理好的所有数据    return result# 通过类别名称、所属数据集、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):    label_lists = image_lists[label_name]  # 获取给定类别中的所有图片    category_list = label_lists[category]  # 根据所属数据集的名称获取该集合中的全部图片    mod_index = index % len(category_list)  # 规范图片的索引    base_name = category_list[mod_index]  # 获取图片的文件名    sub_dir = label_lists['dir']  # 获取当前类别的目录名    full_path = os.path.join(image_dir, sub_dir, base_name)  # 图片的绝对路径    return full_path# 通过类别名称、所属数据集、图片编号获取特征向量值的地址def get_bottleneck_path(image_lists, label_name, index, category):    return get_image_path(image_lists, CACHE_DIR, label_name, index,                          category) + '.txt'# 使用inception-v3处理图片获取特征向量def run_bottleneck_on_image(sess, image_data, image_data_tensor,                            bottleneck_tensor):    bottleneck_values = sess.run(bottleneck_tensor,                                 {image_data_tensor: image_data})    bottleneck_values = np.squeeze(bottleneck_values)  # 将四维数组压缩成一维数组    return bottleneck_values# 获取一张图片经过inception-v3模型处理后的特征向量def get_or_create_bottleneck(sess, image_lists, label_name, index, category,                             jpeg_data_tensor, bottleneck_tensor):    # 获取一张图片对应的特征向量文件的路径    label_lists = image_lists[label_name]    sub_dir = label_lists['dir']    sub_dir_path = os.path.join(CACHE_DIR, sub_dir)    if not os.path.exists(sub_dir_path):        os.makedirs(sub_dir_path)    bottleneck_path = get_bottleneck_path(image_lists, label_name, index,                                          category)    # 如果该特征向量文件不存在，则通过inception-v3模型计算并保存    if not os.path.exists(bottleneck_path):        image_path = get_image_path(image_lists, INPUT_DATA, label_name, index,                                    category)  # 获取图片原始路径        image_data = gfile.FastGFile(image_path, 'rb').read()  # 获取图片内容        # image_data.shape # 查看图像维度        bottleneck_values = run_bottleneck_on_image( #存放压缩至一维的图像特征向量            sess, image_data, jpeg_data_tensor,            bottleneck_tensor)  # 通过inception-v3计算特征向量        # bottleneck_tensor 加载inception-v3模型，并返回数据输入张量        # jpeg_data_tensor 加载inception-v3模型，瓶颈层输出张量        # bottleneck_values.shape        # 将特征向量存入文件        bottleneck_string = ','.join(str(x) for x in bottleneck_values)        with open(bottleneck_path, 'w') as bottleneck_file:            bottleneck_file.write(bottleneck_string)    else:        # 否则直接从文件中获取图片的特征向量        with open(bottleneck_path, 'r') as bottleneck_file:            bottleneck_string = bottleneck_file.read()        bottleneck_values = [float(x) for x in bottleneck_string.split(',')]    # 返回得到的特征向量    return bottleneck_values# 随机获取一个batch图片作为训练数据def get_random_cached_bottlenecks(sess, n_classes, image_lists, how_many,                                  category, jpeg_data_tensor,                                  bottleneck_tensor):    bottlenecks = []    ground_truths = []    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        label_index = random.randrange(n_classes)   #label_index 存放数据标签        label_name = list(image_lists.keys())[label_index]        image_index = random.randrange(65535)        bottleneck = get_or_create_bottleneck(            sess, image_lists, label_name, image_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# 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())    # 枚举所有的类别和每个类别中的测试图片    for label_index, label_name in enumerate(label_name_list):        category = 'testing'        for index, unused_base_name in enumerate(                image_lists[label_name][category]):            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_truthsdef main(_):    # 读取所有的图片 n_classes为分类种类    image_lists = create_image_lists(VALIDATION_PERCENTAGE, TEST_PERCENTAGE)    n_classes = len(image_lists.keys())    # tensorflow 里面已经存好了一张默认图，可以使用tf.get_default_graph() 来调用    with tf.Graph().as_default() as graph:        # 读取训练好的inception-v3模型        with gfile.FastGFile(os.path.join(MODEL_DIR, MODEL_FILE), 'rb') as f:            graph_def = tf.GraphDef()            graph_def.ParseFromString(f.read())            # 加载inception-v3模型，并返回数据输入张量和瓶颈层输出张量            bottleneck_tensor, jpeg_data_tensor = tf.import_graph_def(                graph_def,                return_elements=[                    BOTTLENECK_TENSOR_NAME, JPEG_DATA_TENSOR_NAME                ])            # print(bottleneck_tensor.shape)     #查看瓶颈层维度 shape（1,2048）            # print(jpeg_data_tensor.shape)# 和 数据张量维度        # 定义新的神经网络输入        bottleneck_input = tf.placeholder(            tf.float32, [None, BOTTLENECK_TENSOR_SIZE],            name='BottleneckInputPlaceholder')        # 定义新的标准答案输入        ground_truth_input = tf.placeholder(            tf.float32, [None, n_classes], name='GroundTruthInput')        # 定义一层全连接层解决新的图片分类问题    #维度为2048,2 均差为0.1        with tf.name_scope('final_training_ops'):            weights = tf.Variable(                tf.truncated_normal(                    [BOTTLENECK_TENSOR_SIZE, n_classes], stddev=0.1))#(2048,2)            biases = tf.Variable(tf.zeros([n_classes]))#定义最后一层全连接层为二分类            logits = tf.matmul(bottleneck_input, weights) + biases            final_tensor = tf.nn.softmax(logits)        # 定义交叉熵损失函数        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(#使用随机梯度下降算法 实现参数更新            cross_entropy_mean)        # 计算正确率        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))#根据给出的axis在input_tensor上求平均值                # tf.cast 转化数据格式至float32    # 训练过程    with tf.Session(graph=graph) as sess:        init = tf.global_variables_initializer().run()#初始化模型的参数        # 模型和摘要的保存目录        import time        timestamp = str(int(time.time()))        out_dir = os.path.abspath(#存放模型和摘要目录            os.path.join(os.path.curdir, 'runs', timestamp))        print('\nWriting to {}\n'.format(out_dir))        # 损失值和正确率的摘要        loss_summary = tf.summary.scalar('loss', cross_entropy_mean)#用于画图loss 用来显示标量信息        acc_summary = tf.summary.scalar('accuracy', evaluation_step)        # 训练摘要        train_summary_op = tf.summary.merge([loss_summary, acc_summary])#将所有summary全部保存到磁盘,以便tensorboard显示        train_summary_dir = os.path.join(out_dir, 'summaries', 'train')#./158.../train        train_summary_writer = tf.summary.FileWriter(train_summary_dir,#指定一个文件用来保存图8                                                     sess.graph)        # 开发摘要        dev_summary_op = tf.summary.merge([loss_summary, acc_summary])        dev_summary_dir = os.path.join(out_dir, 'summaries', 'dev')        dev_summary_writer = tf.summary.FileWriter(dev_summary_dir, sess.graph)#指定用一个文件来保存信息        # 保存检查点        checkpoint_dir = os.path.abspath(os.path.join(out_dir, 'checkpoints'))        checkpoint_prefix = os.path.join(checkpoint_dir, 'model')        if not os.path.exists(checkpoint_dir):            os.makedirs(checkpoint_dir)            saver = tf.train.Saver(                tf.global_variables(), max_to_keep=NUM_CHECKPOINTS)        for i in range(STEPS):#STEPS=1500            # 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)            _, train_summaries = sess.run(                [train_step, train_summary_op],                feed_dict={                    bottleneck_input: train_bottlenecks,                    ground_truth_input: train_ground_truth                })            # 保存每步的摘要            train_summary_writer.add_summary(train_summaries, i)            # 在验证集上测试正确率            if i % 100 == 0 or i + 1 == STEPS:#bottle为新输入0.1234123..                validation_bottlenecks, validation_ground_truth = get_random_cached_bottlenecks(                    sess, n_classes, image_lists, BATCH, 'validation',                    jpeg_data_tensor, bottleneck_tensor)                validation_accuracy, dev_summaries = sess.run(                    [evaluation_step, dev_summary_op],                    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))            # 每隔checkpoint_every保存一次模型和测试摘要            if i % CHECKPOINT_EVERY == 0:                dev_summary_writer.add_summary(dev_summaries, i)                path = saver.save(sess, checkpoint_prefix, global_step=i)                print('Saved model checkpoint to {}\n'.format(path))        # 最后在验证集上测试正确率        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))        # 保存标签        output_labels = os.path.join(out_dir, 'labels.txt')        with tf.gfile.FastGFile(output_labels, 'w') as f:            keys = list(image_lists.keys())            for i in range(len(keys)):                keys[i] = '%2d -> %s' % (i, keys[i])            f.write('\n'.join(keys) + '\n')if __name__ == '__main__':    tf.app.run()    #就是程序调用main()...

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