python3下tensorflow练习（八）之迁移学习

 这周帮同学做了一个CNN的分类任务，因为赶时间所以直接用InceptionV3的参数进行迁移学习，只替换最后一层全连接层，然后对自己的数据集进行4分类的训练。在最后这一层全连接层之前的网络层称之为瓶颈层（BOTTLENECK）。

最后实现了对'雨凇','霜','积雪', '露'的四分类，需要先下载InceptionV3的训练好的模型和我的四分类数据集

1.训练的代码

# -*- coding: utf-8 -*-
"""
@author: jiangcheng
卷积神经网络 Inception-v3模型 
"""
import glob
import os.path
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 = './datasets/inception_dec_2015'
# 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 = './datasets/inception_dec_2015/bottleneck'
 
# 图片数据的文件夹
INPUT_DATA = './datasets/snow/'

# 保存训练好的模型的路径。
TRAIN_FILE = './trained_model/model'
#./trained_model/----存放路径,,,model----文件名字
#os.path.join(MODEL_SAVE_PATH,MODEL_NAME)
# 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 = 5000
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)]

    # 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#  返回其小写 .lower()  
        # 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):
    # 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):
    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
            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):
    # 输入
    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"])# #保存指定的节点，并将节点值保存为常数
        with tf.gfile.FastGFile("./trained_model/nn.pb", mode='wb') as f:
            f.write(constant_graph.SerializeToString())
 
if __name__ == '__main__':
    train()

2.测试代码

# -*- coding: utf-8 -*-
"""
@author: jiangcheng
卷积神经网络 Inception-v3模型  测试
"""
import glob
import os.path
import random
import numpy as np
import tensorflow as tf
from tensorflow.python.platform import gfile
from tensorflow.python.framework import graph_util
import cv2
def predict():
    strings = ['雨凇','霜','积雪', '露']#注意顺序
    def id_to_string(node_id):
        return strings[node_id]
 
    with tf.gfile.FastGFile('./trained_model/nn.pb', 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
        tf.import_graph_def(graph_def, name='')
 
    with tf.Session() as sess:
        softmax_tensor = sess.graph.get_tensor_by_name('output/prob:0')
        # 遍历目录
        for root, dirs, files in os.walk('./temp_input/'):
            for file in files:
                # 载入图片
                image_data = tf.gfile.FastGFile(os.path.join(root, file), 'rb').read()
                predictions = sess.run(softmax_tensor, {'DecodeJpeg/contents:0': image_data})  # 图片格式是jpg格式
                predictions = np.squeeze(predictions)  # 把结果转为1维数据
 
                # 打印图片路径及名称
                image_path = os.path.join(root, file)
                print(image_path)
 
                # 排序
                top_k = predictions.argsort()[::-1]
                print(top_k)
                for node_id in top_k:
                    # 获取分类名称
                    human_string = id_to_string(node_id)
                    # 获取该分类的置信度
                    score = predictions[node_id]
                    print('%s (score = %.5f)' % (human_string, score))
                print()
                img = cv2.imread(image_path)
                cv2.imshow('image', img)
                cv2.waitKey(0)
    cv2.destroyAllWindows()

if __name__ == '__main__':
    predict()

3.演示效果图