tensorflow 卷积神经网络 Inception-v3模型迁移学习

本文主要是介绍tensorflow 卷积神经网络 Inception-v3模型迁移学习，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

____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 = Truefor sub_dir in sub_dirs:  # 遍历目录数组，每次处理一种if is_root_dir:is_root_dir = Falsecontinue# 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|tulipsfor 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] = 1.0bottlenecks.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 sunflowerscategory = '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.jpg1 1419608016_707b887337_n.jpg2 14244410747_22691ece4a_n.jpg...105 9467543719_c4800becbb_m.jpg106 9595857626_979c45e5bf_n.jpg107 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] = 1.0bottlenecks.append(bottleneck)ground_truths.append(ground_truth)return bottlenecks, ground_truthsdef 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) + biasesfinal_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%
'''