嘿,我在运行Alexnet特征提取代码时遇到错误。我使用这个github alexnet.pb
创建了link文件。我使用Tensorboard进行了检查,图表进展顺利。
我想使用这个模型从fc7/relu
中提取特征并将其提供给另一个模型。我用这个创建图形:
data = 0
model_dir = 'model'
images_dir = 'images_alexnet/train/' + str(data) + '/'
list_images = [images_dir+f for f in os.listdir(images_dir) if re.search('jpeg|JPEG', f)]
list_images.sort()
def create_graph():
with gfile.FastGFile(os.path.join(model_dir, 'alexnet.pb'), 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
create_graph()
然后喂input
并使用以下方法提取fc7/relu
层:
def extract_features(image_paths, verbose=False):
feature_dimension = 4096
features = np.empty((len(image_paths), feature_dimension))
with tf.Session() as sess:
flattened_tensor = sess.graph.get_tensor_by_name('fc7/relu:0')
for i, image_path in enumerate(image_paths):
if verbose:
print('Processing %s...' % (image_path))
if not gfile.Exists(image_path):
tf.logging.fatal('File does not exist %s', image)
image_data = gfile.FastGFile(image_path, 'rb').read()
feature = sess.run(flattened_tensor, {'input:0': image_data})
features[i, :] = np.squeeze(feature)
return features
但我得到了这个错误:
ValueError: invalid literal for int() with base 10: b'\xff\xd8\xff\xe0\x00\x10JFIF\x00\x01\x01\x00\x00\x01\x00\x01\x00\x00\xff\xdb\x00C\x00\x08\x06\x06\x07\x06\x05\x08\x07\x07\x07\t\t\x08\n\x0c\x14\r\x0c\x0b\x0b\x0c\x19\x12\x13\x0f\x14\x1d\x1a\x1f\x1e\
在喂食图表时,我似乎做错了。我看到使用Tensorboard的图形,看起来占位符dtype
是uint8
。我怎么解决这个问题?
完整错误:
File "C:\ProgramData\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 710, in runfile
execfile(filename, namespace)
File "C:\ProgramData\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 101, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/Users/Hermon Jay/Documents/Python/diabetic_retinopathy_temp6_transfer_learning/feature_extraction_alexnet.py", line 49, in <module>
features = extract_features(list_images)
File "C:/Users/Hermon Jay/Documents/Python/diabetic_retinopathy_temp6_transfer_learning/feature_extraction_alexnet.py", line 44, in extract_features
feature = sess.run(flattened_tensor, {'input:0': image_data})
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 889, in run
run_metadata_ptr)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1089, in _run
np_val = np.asarray(subfeed_val, dtype=subfeed_dtype)
File "C:\ProgramData\Anaconda3\lib\site-packages\numpy\core\numeric.py", line 531, in asarray
return array(a, dtype, copy=False, order=order)
ValueError: invalid literal for int() with base 10: b'\xff\xd8\xff\xe0\x00\x10JFIF\x00\x01\x01\x00\x00\x01\x00\x01\x00\x00\xff\xdb\x00C\x00\x08\x06\x06\x07\x06\x05\x08\x07\x07\x07\t\t\x08\n\x0c\x14\r\x0c\x0b\x0b\x0c\x19\x12\x13\x0f\x14\x1d\x1a\x1f\x1e\
这一行:
image_data = gfile.FastGFile(image_path, 'rb').read()
在image_path
读取文件作为字节数组。但是,input
占位符所期望的是uint8
类型的四维数组。例如,看看你提供的链接中的一个下一个教程10 AlexNet Transfer Learning;函数get_batch
使用额外的图形生成批次,像tf.image.decode_jpeg
这样的操作;然后它将该图的结果作为主网络图的输入。
例如,你可以有这样的东西(如果你的所有图像都适合内存,否则你必须像教程一样批处理它们):
def read_images(image_paths):
with tf.Graph().as_default(), tf.Session() as sess:
file_name = tf.placeholder(tf.string)
jpeg_data = tf.read_file(jpeg_name)
decoded_image = tf.image.decode_jpeg(jpeg_data, channels=3)
images = []
for path in image_paths:
images.append(sess.run(decoded_image, feed_dict={file_name: path}))
return images
def extract_features(image_paths):
images = read_images(image_paths)
with tf.Session() as sess:
flattened_tensor = sess.graph.get_tensor_by_name('fc7/relu:0')
return sess.run(flattened_tensor, {'input:0': images})