TLDR：无法弄清楚如何使用重新训练的inceptionV3进行多个图像预测。
Hello kind people :)我花了几天时间搜索了很多stackoverflow帖子和文档，但我找不到这个问题的答案。非常感谢任何帮助！
我在新图片上重新训练了tensorflow inceptionV3模型，并且可以按照https://www.tensorflow.org/versions/r0.9/how_tos/image_retraining/index.html上的说明并使用以下命令处理新图像：
bazel build tensorflow/examples/label_image:label_image && \
bazel-bin/tensorflow/examples/label_image/label_image \
--graph=/tmp/output_graph.pb --labels=/tmp/output_labels.txt \
--output_layer=final_result \
--image= IMAGE_DIRECTORY_TO_CLASSIFY
但是，我需要对多个图像进行分类（如数据集），并严重依赖于如何操作。我在
找到了以下示例https://github.com/eldor4do/Tensorflow-Examples/blob/master/retraining-example.py
关于如何使用再训练模型，但同样，关于如何针对多个分类修改它的细节非常稀少。
根据我从MNIST教程中收集到的内容，我需要在sess.run（）对象中输入feed_dict，但由于我无法理解如何在此上下文中实现它，因此卡在那里。
非常感谢任何帮助！ :)
编辑：
运行Styrke的脚本并做了一些修改，我得到了这个
waffle@waffleServer:~/git$ python tensorflowMassPred.py I
tensorflow/stream_executor/dso_loader.cc:108] successfully opened
CUDA library libcublas.so locally I
tensorflow/stream_executor/dso_loader.cc:108] successfully opened
CUDA library libcudnn.so locally I
tensorflow/stream_executor/dso_loader.cc:108] successfully opened
CUDA library libcufft.so locally I
tensorflow/stream_executor/dso_loader.cc:108] successfully opened
CUDA library libcuda.so locally I
tensorflow/stream_executor/dso_loader.cc:108] successfully opened
CUDA library libcurand.so locally
/home/waffle/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py:1197:
VisibleDeprecationWarning: converting an array with ndim > 0 to an
index will result in an error in the future
result_shape.insert(dim, 1) I
tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:924] successful
NUMA node read from SysFS had negative value (-1), but there must be
at least one NUMA node, so returning NUMA node zero I
tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0
with properties: name: GeForce GTX 660 major: 3 minor: 0
memoryClockRate (GHz) 1.0975 pciBusID 0000:01:00.0 Total memory:
2.00GiB Free memory: 1.78GiB I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0 I
tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0: Y I
tensorflow/core/common_runtime/gpu/gpu_device.cc:806] Creating
TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 660, pci
bus id: 0000:01:00.0) W tensorflow/core/framework/op_def_util.cc:332]
Op BatchNormWithGlobalNormalization is deprecated. It will cease to
work in GraphDef version 9. Use tf.nn.batch_normalization(). E
tensorflow/core/common_runtime/executor.cc:334] Executor failed to
create kernel. Invalid argument: NodeDef mentions attr 'T' not in
Op<name=MaxPool; signature=input:float -> output:float;
attr=ksize:list(int),min=4; attr=strides:list(int),min=4;
attr=padding:string,allowed=["SAME", "VALID"];
attr=data_format:string,default="NHWC",allowed=["NHWC", "NCHW"]>;
NodeDef: pool = MaxPool[T=DT_FLOAT, data_format="NHWC", ksize=[1, 3,
3, 1], padding="VALID", strides=[1, 2, 2, 1],
_device="/job:localhost/replica:0/task:0/gpu:0"](pool/control_dependency)
[[Node: pool = MaxPool[T=DT_FLOAT, data_format="NHWC", ksize=[1, 3,
3, 1], padding="VALID", strides=[1, 2, 2, 1],
_device="/job:localhost/replica:0/task:0/gpu:0"](pool/control_dependency)]]
Traceback (most recent call last): File
"/home/waffle/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py",
line 715, in _do_call
return fn(*args) File "/home/waffle/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py",
line 697, in _run_fn
status, run_metadata) File "/home/waffle/anaconda3/lib/python3.5/contextlib.py", line 66, in
__exit__
next(self.gen) File "/home/waffle/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/errors.py",
line 450, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status)) tensorflow.python.framework.errors.InvalidArgumentError: NodeDef
mentions attr 'T' not in Op<name=MaxPool; signature=input:float ->
output:float; attr=ksize:list(int),min=4;
attr=strides:list(int),min=4; attr=padding:string,allowed=["SAME",
"VALID"]; attr=data_format:string,default="NHWC",allowed=["NHWC",
"NCHW"]>; NodeDef: pool = MaxPool[T=DT_FLOAT, data_format="NHWC",
ksize=[1, 3, 3, 1], padding="VALID", strides=[1, 2, 2, 1],
_device="/job:localhost/replica:0/task:0/gpu:0"](pool/control_dependency)
[[Node: pool = MaxPool[T=DT_FLOAT, data_format="NHWC", ksize=[1, 3,
3, 1], padding="VALID", strides=[1, 2, 2, 1],
_device="/job:localhost/replica:0/task:0/gpu:0"](pool/control_dependency)]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last): File "tensorflowMassPred.py",
line 116, in <module>
run_inference_on_image() File "tensorflowMassPred.py", line 98, in run_inference_on_image
{'DecodeJpeg/contents:0': image_data}) File "/home/waffle/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py",
line 372, in run
run_metadata_ptr) File "/home/waffle/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py",
line 636, in _run
feed_dict_string, options, run_metadata) File "/home/waffle/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py",
line 708, in _do_run
target_list, options, run_metadata) File "/home/waffle/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py",
line 728, in _do_call
raise type(e)(node_def, op, message) tensorflow.python.framework.errors.InvalidArgumentError: NodeDef
mentions attr 'T' not in Op<name=MaxPool; signature=input:float ->
output:float; attr=ksize:list(int),min=4;
attr=strides:list(int),min=4; attr=padding:string,allowed=["SAME",
"VALID"]; attr=data_format:string,default="NHWC",allowed=["NHWC",
"NCHW"]>; NodeDef: pool = MaxPool[T=DT_FLOAT, data_format="NHWC",
ksize=[1, 3, 3, 1], padding="VALID", strides=[1, 2, 2, 1],
_device="/job:localhost/replica:0/task:0/gpu:0"](pool/control_dependency)
[[Node: pool = MaxPool[T=DT_FLOAT, data_format="NHWC", ksize=[1, 3,
3, 1], padding="VALID", strides=[1, 2, 2, 1],
_device="/job:localhost/replica:0/task:0/gpu:0"](pool/control_dependency)]]
Caused by op 'pool', defined at: File "tensorflowMassPred.py", line
116, in <module>
run_inference_on_image() File "tensorflowMassPred.py", line 87, in run_inference_on_image
create_graph() File "tensorflowMassPred.py", line 68, in create_graph
_ = tf.import_graph_def(graph_def, name='') File "/home/waffle/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/importer.py",
line 274, in import_graph_def
op_def=op_def) File "/home/waffle/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py",
line 2260, in create_op
original_op=self._default_original_op, op_def=op_def) File "/home/waffle/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py",
line 1230, in __init__
self._traceback = _extract_stack()
这是脚本：删除了一些功能。
import os
import numpy as np
import tensorflow as tf
os.chdir('tensorflow/') #if need to run in the tensorflow directory
import csv,os
import pandas as pd
import glob
imagePath = '../_images_processed/test'
modelFullPath = '/tmp/output_graph.pb'
labelsFullPath = '/tmp/output_labels.txt'
# FILE NAME TO SAVE TO.
SAVE_TO_CSV = 'tensorflowPred.csv'
def makeCSV():
global SAVE_TO_CSV
with open(SAVE_TO_CSV,'w') as f:
writer = csv.writer(f)
writer.writerow(['id','label'])
def makeUniqueDic():
global SAVE_TO_CSV
df = pd.read_csv(SAVE_TO_CSV)
doneID = df['id']
unique = doneID.unique()
uniqueDic = {str(key):'' for key in unique} #for faster lookup
return uniqueDic
def create_graph():
"""Creates a graph from saved GraphDef file and returns a saver."""
# Creates graph from saved graph_def.pb.
with tf.gfile.FastGFile(modelFullPath, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
def run_inference_on_image():
answer = []
global imagePath
if not tf.gfile.IsDirectory(imagePath):
tf.logging.fatal('imagePath directory does not exist %s', imagePath)
return answer
if not os.path.exists(SAVE_TO_CSV):
makeCSV()
files = glob.glob(imagePath+'/*.jpg')
uniqueDic = makeUniqueDic()
# Get a list of all files in imagePath directory
#image_list = tf.gfile.ListDirectory(imagePath)
# Creates graph from saved GraphDef.
create_graph()
with tf.Session() as sess:
softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
for pic in files:
name = getNamePicture(pic)
if name not in uniqueDic:
image_data = tf.gfile.FastGFile(pic, 'rb').read()
predictions = sess.run(softmax_tensor,
{'DecodeJpeg/contents:0': image_data})
predictions = np.squeeze(predictions)
top_k = predictions.argsort()[-5:][::-1] # Getting top 5 predictions
f = open(labelsFullPath, 'rb')
lines = f.readlines()
labels = [str(w).replace("\n", "") for w in lines]
# for node_id in top_k:
# human_string = labels[node_id]
# score = predictions[node_id]
# print('%s (score = %.5f)' % (human_string, score))
pred = labels[top_k[0]]
with open(SAVE_TO_CSV,'a') as f:
writer = csv.writer(f)
writer.writerow([name,pred])
return answer
if __name__ == '__main__':
run_inference_on_image()
答案 0 :(得分：7)
原始jpeg数据似乎直接送到decode_jpeg操作，一次只能输入一个图像作为输入。为了一次处理多个图像，您可能需要定义更多decode_jpeg操作。如果可以这样做那么我现在不知道如何。
下一个最好的事情，很简单，可能是通过TensorFlow会话循环逐个对所有图像进行分类。这样你至少可以避免重新加载图形并为你想要分类的每个图像开始一个新的TF会话，如果你不得不这么做的话，这两个都需要花费很多时间。
这里我更改了run_inference_on_image()函数的定义，因此它应该对imagePath变量指定的目录中的所有图像进行分类。我没有测试过这段代码，所以可能会有一些小问题需要修复。
def run_inference_on_image():
answer = []
if not tf.gfile.IsDirectory(imagePath):
tf.logging.fatal('imagePath directory does not exist %s', imagePath)
return answer
# Get a list of all files in imagePath directory
image_list = tf.gfile.ListDirectory(imagePath)
# Creates graph from saved GraphDef.
create_graph()
with tf.Session() as sess:
softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
for i in image_list:
image_data = tf.gfile.FastGFile(i, 'rb').read()
predictions = sess.run(softmax_tensor,
{'DecodeJpeg/contents:0': image_data})
predictions = np.squeeze(predictions)
top_k = predictions.argsort()[-5:][::-1] # Getting top 5 predictions
f = open(labelsFullPath, 'rb')
lines = f.readlines()
labels = [str(w).replace("\n", "") for w in lines]
for node_id in top_k:
human_string = labels[node_id]
score = predictions[node_id]
print('%s (score = %.5f)' % (human_string, score))
answer.append(labels[top_k[0]])
return answer
答案 1 :(得分：4)
所以看看你的链接脚本：
with tf.Session() as sess:
softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
predictions = sess.run(softmax_tensor,
{'DecodeJpeg/contents:0': image_data})
predictions = np.squeeze(predictions)
top_k = predictions.argsort()[-5:][::-1] # Getting top 5 predictions
在此代码段中，image_data是您要提供给模型的新图片，之前已加载了几行：
image_data = tf.gfile.FastGFile(imagePath, 'rb').read()
因此，我的直觉是将run_inference_on_image更改为接受imagePath作为参数，并使用os.listdir和os.path.join对数据集中的每个图片执行此操作。
答案 2 :(得分：0)
我有同样的问题。我遵循了所有可能的解决方案，最后找到了一个适合我的解决方案。当用于重新训练模型的Tensorflow版本与使用它的版本不同时，会发生此错误。
解决方案是将Tensorflow更新到最新版本。由于我使用pip来安装Tensorflow，我只需运行以下命令：
sudo pip install tensorflow --upgrade
它完美无缺。