我正在尝试使用Keras运行Conv2D网络以读取一个包含20bn Jester中的手势图像的文件夹集 我知道Conv2D可能无法正常工作，但是我想在更改太多代码之前先获取一些我以前使用过的东西才能正确运行。 但是，我一直遇到
ValueError: Tensor("training/Adamax/Const:0", shape=(), dtype=int64) must be from the same graph as Tensor("Adamax/iterations:0", shape=(), dtype=resource).
并且对它的理解不足。 我尝试过其他有关重置图形的答案
import keras
keras.backend.clear_session()
或
tf.reset_default_graph()
，但两者都不起作用。
我的图像文件结构类似于：
../images/train/[Gesture]/[Sample]/Image001.png
比我以前使用的级别更深，但是flow_from_directory正确输出了图像和类计数，用于训练和验证集
Found 3456570 images belonging to 27 classes.
Found 532578 images belonging to 27 classes.
Conda列表：
...
cudatoolkit 10.0.130 0
cudnn 7.6.4 cuda10.0_0
...
keras 2.3.1 0
keras-applications 1.0.8 py_0
keras-base 2.3.1 py37_0
keras-gpu 2.3.1 0
keras-preprocessing 1.1.0 py_1
...
tensorboard 1.14.0 py37hf484d3e_0
tensorflow 1.14.0 gpu_py37h4491b45_0
tensorflow-base 1.14.0 gpu_py37h8d69cac_0
tensorflow-estimator 1.14.0 py_0
tensorflow-gpu 1.14.0 h0d30ee6_0
代码：
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import glob
import shutil
import pickle
import cv2
import numpy as np
import matplotlib.pyplot as plt
import random
from IPython.display import display
from PIL import Image
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, BatchNormalization, Activation
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.layers.convolutional import Conv3D, MaxPooling3D
from keras.constraints import maxnorm
from keras.utils import np_utils
from keras.preprocessing.image import ImageDataGenerator
import tensorflow as tf
os.environ["CUDA_VISIBLE_DEVICES"]="1"
tf.reset_default_graph()
# read in the training and validation labels
trainPairs = np.genfromtxt('/home/me/Videos/sign_language/jester-v1-train.csv', delimiter=';', skip_header=0, dtype=[('class', 'S12'),('sign','S50')])
trainLabels = [v for k,v in trainPairs]
validPairs = np.genfromtxt('/home/me/Videos/sign_language/jester-v1-validation.csv', delimiter=';', skip_header=0, dtype=[('class', 'S12'),('sign','S50')])
validLabels = [v for k,v in validPairs]
def copyDirectory(src, dest):
try:
shutil.copytree(src, dest)
# Directories are the same
except shutil.Error as e:
print('Directory not copied. Error: %s' % e)
# Any error saying that the directory doesn't exist
except OSError as e:
print('Directory not copied. Error: %s' % e)
source = '/media/me/other/20bn-jester-v1/'
dest = '/media/me/other/jester/validation/'
# counter = 0
# for k,v in validPairs:
# counter = counter + 1
# source_folder = source + k.decode("utf-8")
# dest_folder = dest + v.decode("utf-8") + "/" + k.decode("utf-8")
# if counter%100 == 0:
# print(k)
# print(v)
# print(counter)
# print(source_folder)
# print(dest_folder)
# if os.path.isdir(source_folder):
# if os.path.isdir(dest + v.decode("utf-8")):
# copyDirectory(source_folder, dest_folder)
# if counter%1000 == 0:
# print(counter)
datagen = ImageDataGenerator()
train_it = datagen.flow_from_directory('/media/me/other/jester/train/', class_mode='categorical', batch_size=64)
valid_it = datagen.flow_from_directory('/media/me/other/jester/validation/', class_mode='categorical', batch_size=64)
# test_it = datagen.flow_from_directory('/media/me/other/jester/test/', class_mode='binary', batch_size=64)
seed = 21
epochs = 5
optimizer = 'Adamax'
with tf.device("/cpu:0"):
model = Sequential()
model = Sequential()
#model.add(Conv2D(32,(3,3), input_shape=(X_train.shape[1:]), padding='same'))
#TODO is this the right shape??
model.add(Conv2D(32,(3,3), input_shape=(256, 256, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(32, (3,3), input_shape=(3,32,32), activation='relu', padding='same'))
model.add(Dropout(0.2))
model.add(BatchNormalization())
model.add(Conv2D(64, (3,3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(BatchNormalization())
model.add(Conv2D(128, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(BatchNormalization())
model.add(Flatten())
model.add(Dropout(0.2))
model.add(Dense(256, kernel_constraint=maxnorm(3)))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(BatchNormalization())
model.add(Dense(128, kernel_constraint=maxnorm(3)))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(BatchNormalization())
#TODO make this a variable
model.add(Dense(27))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
### I think everything up to here is ok???
global graph
graph = tf.get_default_graph()
for layer in model.layers:
print(layer.output_shape)
print(model.summary())
np.random.seed(seed)
image_batch_train, label_batch_train = next(iter(train_it))
print("Image batch shape: ", image_batch_train.shape)
print("Label batch shape: ", label_batch_train.shape)
dataset_labels = sorted(train_it.class_indices.items(), key=lambda pair:pair[1])
dataset_labels = np.array([key.title() for key, value in dataset_labels])
print(dataset_labels)
from keras import backend as K
K.clear_session()
import keras
keras.backend.clear_session()
tf.reset_default_graph()
model.fit_generator(train_it, steps_per_epoch=16, validation_data=valid_it, validation_steps=8)
#scores = model.evaluate(test_it, steps=24, verbose=0)
#print("Accuracy: %.2f%%" % (scores[1]*100))
编辑1：添加了日志
Traceback (most recent call last):
File "<ipython-input-1-09b1bdd2e389>", line 152, in <module>
model.fit_generator(train_it, steps_per_epoch=16, validation_data=valid_it, validation_steps=8)
File "/home/me/Programs/anaconda3/envs/hand-gesture/lib/python3.7/site-packages/keras/legacy/interfaces.py", line 91, in wrapper
return func(*args, **kwargs)
File "/home/me/Programs/anaconda3/envs/hand-gesture/lib/python3.7/site-packages/keras/engine/training.py", line 1732, in fit_generator
initial_epoch=initial_epoch)
File "/home/me/Programs/anaconda3/envs/hand-gesture/lib/python3.7/site-packages/keras/engine/training_generator.py", line 42, in fit_generator
model._make_train_function()
File "/home/me/Programs/anaconda3/envs/hand-gesture/lib/python3.7/site-packages/keras/engine/training.py", line 316, in _make_train_function
loss=self.total_loss)
File "/home/me/Programs/anaconda3/envs/hand-gesture/lib/python3.7/site-packages/keras/legacy/interfaces.py", line 91, in wrapper
return func(*args, **kwargs)
File "/home/me/Programs/anaconda3/envs/hand-gesture/lib/python3.7/site-packages/keras/optimizers.py", line 599, in get_updates
self.updates = [K.update_add(self.iterations, 1)]
File "/home/me/Programs/anaconda3/envs/hand-gesture/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py", line 1268, in update_add
return tf_state_ops.assign_add(x, increment)
File "/home/me/Programs/anaconda3/envs/hand-gesture/lib/python3.7/site-packages/tensorflow/python/ops/state_ops.py", line 195, in assign_add
return ref.assign_add(value)
File "/home/me/Programs/anaconda3/envs/hand-gesture/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py", line 1108, in assign_add
name=name)
File "/home/me/Programs/anaconda3/envs/hand-gesture/lib/python3.7/site-packages/tensorflow/python/ops/gen_resource_variable_ops.py", line 68, in assign_add_variable_op
"AssignAddVariableOp", resource=resource, value=value, name=name)
File "/home/me/Programs/anaconda3/envs/hand-gesture/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py", line 366, in _apply_op_helper
g = ops._get_graph_from_inputs(_Flatten(keywords.values()))
File "/home/me/Programs/anaconda3/envs/hand-gesture/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 6135, in _get_graph_from_inputs
_assert_same_graph(original_graph_element, graph_element)
File "/home/me/Programs/anaconda3/envs/hand-gesture/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 6071, in _assert_same_graph
(item, original_item))
ValueError: Tensor("training/Adamax/Const:0", shape=(), dtype=int64) must be from the same graph as Tensor("Adamax/iterations:0", shape=(), dtype=resource).
答案 0 :(得分：0)
问题是您在训练模型之前要重置默认图形：
tf.reset_default_graph() # <-- remove this line
model.fit_generator(train_it, steps_per_epoch=16, validation_data=valid_it, validation_steps=8)
问题如下。您首先要在开始时重置默认图形，除非您的实际脚本在此之前有更多代码，否则实际上没有任何区别，因为默认图形在那时是空的。然后创建模型，并在新的默认图形上创建操作。新的默认图形是您以后使用的图形：
graph = tf.get_default_graph()
问题是稍后，在两次清除Keras会话（也没有任何作用）之后，您再次重置了默认图形。当您调用fit时，训练过程开始，并且创建了Keras模型的一些新图形对象。由于您的默认图形已更改（因为您将其重置），因此这些新对象在与其余对象不同的图形中创建，这会导致错误。我认为，如果您在训练过程中使用以前的默认图形作为默认图形，仍然可以重置图形并使其正常工作：
tf.reset_default_graph()
with graph.as_default(): # Use former default graph as default
model.fit_generator(train_it, steps_per_epoch=16, validation_data=valid_it, validation_steps=8)
我不确定Keras的默认会话是否会一直正常工作（因为它可能是为新的默认图形创建的），但是我认为它应该...无论如何，如果您出于任何原因想要要将Keras模型隔离在自己的图形中，而不是重置图形，您可以按照以下步骤进行操作：
with tf.Graph().as_default() as graph: # Make a new graph and use it as default
# Make dataset
# Make model
# Train