tensorflow 卷积神经网络 Inception-v3模型 迁移学习

____tz_zs小练习

案例来源于 《TensorFlow实战Google深度学习框架》

谷歌提供的训练好的Inception-v3模型： https://storage.googleapis.com/download.tensorflow.org/models/inception_dec_2015.zip

案例使用的数据集： http://download.tensorflow.org/example_images/flower_photos.tgz

数据集文件解压后，包含5个子文件夹，子文件夹的名称为花的名称，代表了不同的类别。平均每一种花有734张图片，图片是RGB色彩模式，大小也不相同。

# -*- coding: utf-8 -*-
"""
@author: tz_zs

卷积神经网络 Inception-v3模型 迁移学习
"""
import glob
import os.path
import random
import numpy as np
import tensorflow as tf
from tensorflow.python.platform import gfile

# 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 = '/path/to/model/google2015-inception-v3'
# 下载的谷歌训练好的inception-v3模型文件名
MODEL_FILE = 'tensorflow_inception_graph.pb'

# 保存训练数据通过瓶颈层后提取的特征向量
CACHE_DIR = 'tmp/bottleneck'

# 图片数据的文件夹
INPUT_DATA = '/path/to/flower_data'

# 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 = 4000
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
    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 = ['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


# 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
    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]
    mod_index = index % len(category_list)  # 图片的编号%此数据集中图片数量
    # 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


# 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)

    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


# 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 = []
    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_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


# 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
            '''
            print(index, unused_base_name)
            0 10386503264_e05387e1f7_m.jpg
            1 1419608016_707b887337_n.jpg
            2 14244410747_22691ece4a_n.jpg
            ...
            105 9467543719_c4800becbb_m.jpg
            106 9595857626_979c45e5bf_n.jpg
            107 9922116524_ab4a2533fe_n.jpg
            '''
            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 main(_):
    image_lists = create_image_lists(TEST_PERCENTACE, VALIDATION_PERCENTAGE)
    n_classes = len(image_lists.keys())
    # 读取模型
    with gfile.FastGFile(os.path.join(MODEL_DIR, MODEL_FILE), 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
    # 加载模型，返回对应名称的张量
    bottleneck_tensor, jpeg_data_tensor = tf.import_graph_def(graph_def, return_elements=[BOTTLENECK_TENSOR_NAME,
                                                                                          JPEG_DATA_TENSOR_NAME])
    # 输入
    bottleneck_input = tf.placeholder(tf.float32, [None, BOTTLENECK_TENSOR_SIZE], name='BottleneckInputPlaceholder')
    ground_truth_input = tf.placeholder(tf.float32, [None, n_classes], name='GroundTruthInput')

    # 全连接层
    with tf.name_scope('final_training_ops'):
        weights = tf.Variable(tf.truncated_normal([BOTTLENECK_TENSOR_SIZE, n_classes], stddev=0.001))
        biases = tf.Variable(tf.zeros([n_classes]))
        logits = tf.matmul(bottleneck_input, weights) + biases
        final_tensor = tf.nn.softmax(logits)

    # 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)
    # 优化
    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))

    with tf.Session() as sess:
        # 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))


if __name__ == '__main__':
    tf.app.run()

'''
Step 0: Validation accuracy on random sampled 100 examples = 40.0%
Step 100: Validation accuracy on random sampled 100 examples = 81.0%
Step 200: Validation accuracy on random sampled 100 examples = 79.0%
Step 300: Validation accuracy on random sampled 100 examples = 92.0%
Step 400: Validation accuracy on random sampled 100 examples = 90.0%
Step 500: Validation accuracy on random sampled 100 examples = 88.0%
Step 600: Validation accuracy on random sampled 100 examples = 89.0%
Step 700: Validation accuracy on random sampled 100 examples = 94.0%
Step 800: Validation accuracy on random sampled 100 examples = 91.0%
Step 900: Validation accuracy on random sampled 100 examples = 88.0%
Step 1000: Validation accuracy on random sampled 100 examples = 84.0%
Step 1100: Validation accuracy on random sampled 100 examples = 92.0%
Step 1200: Validation accuracy on random sampled 100 examples = 86.0%
Step 1300: Validation accuracy on random sampled 100 examples = 91.0%
Step 1400: Validation accuracy on random sampled 100 examples = 96.0%
Step 1500: Validation accuracy on random sampled 100 examples = 89.0%
Step 1600: Validation accuracy on random sampled 100 examples = 94.0%
Step 1700: Validation accuracy on random sampled 100 examples = 90.0%
Step 1800: Validation accuracy on random sampled 100 examples = 94.0%
Step 1900: Validation accuracy on random sampled 100 examples = 94.0%
Step 2000: Validation accuracy on random sampled 100 examples = 94.0%
Step 2100: Validation accuracy on random sampled 100 examples = 93.0%
Step 2200: Validation accuracy on random sampled 100 examples = 92.0%
Step 2300: Validation accuracy on random sampled 100 examples = 96.0%
Step 2400: Validation accuracy on random sampled 100 examples = 92.0%
Step 2500: Validation accuracy on random sampled 100 examples = 92.0%
Step 2600: Validation accuracy on random sampled 100 examples = 93.0%
Step 2700: Validation accuracy on random sampled 100 examples = 90.0%
Step 2800: Validation accuracy on random sampled 100 examples = 92.0%
Step 2900: Validation accuracy on random sampled 100 examples = 91.0%
Step 3000: Validation accuracy on random sampled 100 examples = 96.0%
Step 3100: Validation accuracy on random sampled 100 examples = 90.0%
Step 3200: Validation accuracy on random sampled 100 examples = 94.0%
Step 3300: Validation accuracy on random sampled 100 examples = 97.0%
Step 3400: Validation accuracy on random sampled 100 examples = 95.0%
Step 3500: Validation accuracy on random sampled 100 examples = 92.0%
Step 3600: Validation accuracy on random sampled 100 examples = 94.0%
Step 3700: Validation accuracy on random sampled 100 examples = 94.0%
Step 3800: Validation accuracy on random sampled 100 examples = 95.0%
Step 3900: Validation accuracy on random sampled 100 examples = 95.0%
Step 3999: Validation accuracy on random sampled 100 examples = 94.0%
Final test accuracy = 95.4%
'''