转载至：http://blog.csdn.net/nnnnnnnnnnnny/article/details/70244232 此例程出自《TensorFlow实战Google深度学习框架》6.5.2小节 卷积神经网络迁移学习。 数据集来自http://download.tensorflow.org/example_images/flower_photos.tgz ，及谷歌提供的Inception-v3模型https://storage.googleapis.com/download.tensorflow.org/models/inception_dec_2015.zip 。 自行下载和解压。 解压后的文件夹包含5个子文件夹，每个子文件夹的名称为一种花的名称，代表了不同的类别。 工程目录：
-
transfer_learning
-
flower_data //存放原始图片的文件夹，有5个子文件夹， 每个子文件夹的名称为一种花的名称
-
daisy //daisy类花图片的文件夹
-
dandelion
-
roses
-
sunflowers
-
tulips
-
LICENSE.txt
-
model //存放模型的文件夹
-
imagenet_comp_graph_label_strings.txt
-
LICENSE
-
tensorflow_inception_graph.pb //模型文件
-
tmp
-
bottleneck //保存模型瓶颈层的特征结果
-
daisy //daisy类花特征的文件夹
-
dandelion
-
roses
-
sunflowers
-
tulips
-
transfer_flower.py //所有的程序都在这里了
代码实现
transfer_flower.py
import glob
import os.path
import random
import numpy
as np
import tensorflow
as tf
from tensorflow.python.platform
import gfile
BOTTLENECK_TENSOR_SIZE =
2048
BOTTLENECK_TENSOR_NAME =
'pool_3/_reshape:0'
JPEG_DATA_TENSOR_NAME =
'DecodeJpeg/contents:0'
MODEL_DIR =
'model/'
MODEL_FILE =
'tensorflow_inception_graph.pb'
CACHE_DIR =
'tmp/bottleneck/'
INPUT_DATA =
'flower_data/'
VALIDATION_PERCENTAGE =
10
TEST_PERCENTAGE =
10
LEARNING_RATE =
0.01
STEPS =
4000
BATCH =
100
def create_image_lists(testing_percentage, validation_percentage):
result = {}
sub_dirs = [x[
0]
for x
in os.walk(INPUT_DATA)]
is_root_dir =
True
for sub_dir
in sub_dirs:
if is_root_dir:
is_root_dir =
False
continue
extensions = [
'jpg',
'jpeg',
'JPG',
'JPEG']
file_list = []
dir_name = os.path.basename(sub_dir)
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
label_name = dir_name.lower()
training_images = []
testing_images = []
validation_images = []
for file_name
in file_list:
base_name = os.path.basename(file_name)
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):
label_lists = image_lists[label_name]
category_list = label_lists[category]
mod_index = index % len(category_list)
base_name = category_list[mod_index]
sub_dir = label_lists[
'dir']
full_path = os.path.join(image_dir, sub_dir, base_name)
return full_path
def get_bottlenect_path(image_lists, label_name, index, category):
return get_image_path(image_lists, CACHE_DIR, label_name, index, category) +
'.txt';
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
def get_or_create_bottleneck(sess, image_lists, label_name, index, category, jpeg_data_tensor, bottleneck_tensor):
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_bottlenect_path(image_lists, label_name, index, category)
if not os.path.exists(bottleneck_path):
image_path = get_image_path(image_lists, INPUT_DATA, label_name, index, category)
image_data = gfile.FastGFile(image_path,
'rb').read()
bottleneck_values = run_bottleneck_on_image(sess, image_data, jpeg_data_tensor, bottleneck_tensor)
bottleneck_string =
','.join(str(x)
for x
in bottleneck_values)
with open(bottleneck_path,
'w')
as bottleneck_file:
bottleneck_file.write(bottleneck_string)
else:
with open(bottleneck_path,
'r')
as bottleneck_file:
bottleneck_string = bottleneck_file.read()
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):
bottlenecks = []
ground_truths = []
for _
in range(how_many):
label_index = random.randrange(n_classes)
label_name = list(image_lists.keys())[label_index]
image_index = random.randrange(
65536)
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
def get_test_bottlenecks(sess, image_lists, n_classes, jpeg_data_tensor, bottleneck_tensor):
bottlenecks = []
ground_truths = []
label_name_list = list(image_lists.keys())
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_truths
def main(_):
image_lists = create_image_lists(TEST_PERCENTAGE, VALIDATION_PERCENTAGE)
n_classes = len(image_lists.keys())
with gfile.FastGFile(os.path.join(MODEL_DIR, MODEL_FILE),
'rb')
as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
bottleneck_tensor, jpeg_data_tensor = tf.import_graph_def(graph_def, return_elements=[BOTTLENECK_TENSOR_NAME, JPEG_DATA_TENSOR_NAME])
bottleneck_input = tf.placeholder(tf.float32, [
None, BOTTLENECK_TENSOR_SIZE], name=
'BottleneckInputPlaceholder')
ground_truth_input = tf.placeholder(tf.float32, [
None, n_classes], name=
'GroundTruthInput')
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)
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)
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:
tf.global_variables_initializer().run()
for i
in range(STEPS):
train_bottlenecks, train_ground_truth = get_random_cached_bottlenecks(
sess, n_classes, image_lists, BATCH,
'training', jpeg_data_tensor, bottleneck_tensor)
sess.run(train_step, feed_dict={bottleneck_input: train_bottlenecks, ground_truth_input: train_ground_truth})
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))
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()
实验及分析
训练及测试结果 2.代码疑问 不知道大家有注意到没有，数据集里给的是不同大小的图片，而程序里却可以直接送入Inception-v3模型，从而得到同样尺寸的结果特征向量。我在书籍的github上问了这个问题，得到的回复是：Inception-v3模型中包含了图像预处理和大小调整的部分。目前并没有往下继续考究。原问题详见：caicloud/tensorflow-tutorial第6章迁移学习例程疑问
