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TLDR：複数の画像予測に再学習開始V3を使用する方法を理解できません。テンソルフローの編集V3 retraining-example.py複数の分類のため
こんにちは親友:)私は数日間、多くのstackoverflow投稿とドキュメントを検索しましたが、私はこの質問に対する答えを見つけることができませんでした。これに関する助けを大いに感謝します！
私は新しい絵にtensorflow inceptionV3モデルを再訓練している、そしてhttps://www.tensorflow.org/versions/r0.9/how_tos/image_retraining/index.htmlでの指示に従って、次のコマンドを使用して、新しい画像上で動作することができます：
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
はしかし、私は複数を分類する必要があります（データセットのような）イメージを作成し、それを行う方法に真剣に取り組んでいます。私は再訓練モデルを使用する方法について
https://github.com/eldor4do/Tensorflow-Examples/blob/master/retraining-example.py
で次の例を見つけたが、再び、それは複数の分類のためにそれを変更する方法の詳細に大きくまばらです。
私はMNISTのチュートリアルから収集したものから、sess.run（）オブジェクトにfeed_dictを入力する必要がありますが、このコンテキストで実装する方法を理解できないため、そこにはまっていました。
ご協力いただければ幸いです。 :)
EDIT：一部の機能が削除されます。
は、いくつかの変更をStyrkeのスクリプトを実行し、私はこれはスクリプトである。この
[email protected]:~/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()
を得ました。
だからあなたのリンクスクリプトを見て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()
こんにちは、お返事ありがとうございます！申し訳ありませんが、私はあなたが意味することを理解していません。すべての写真をロードしてから、sess.run（）をループすると言っていますか？ – Wboy
これは素晴らしい方法です。 ：D – struct
残念ながら、それは私が探しているものではありません。通常のクラシファイア（xgboostなど）に1行ずつフィッティングするのに似ていて、非常に遅い（8k画像では31時間かかる）。私はfeed_dictと分類子にX画像全体をフィードし、ワンショットですべての画像の予測を出力するソリューションを探しています。 – Wboy