获取此问题（使用Keras和Tensorflow），我们将非常感谢您的帮助。
我的目录设置为folder/folder/images - 用于培训和测试数据。
我做了一个循环来测试Resnet中的不同深度/ nb_layers，以及一些超级参数，如学习率，批量大小等。测试从4,6,8,10开始 - 所有通往20的路，然后给了我：
生成器的输出应该是元组（x，y，sample_weight）或（x，y）。 发现：无
我不明白为什么它可以用于少数迭代，然后失败。
我阅读here将keras更新为2.0，但被告知不要改变我老板的keras版本。我的版本是1.2.0。
我阅读here将所有标签转换为numpy数组，但keras文档说明在使用flow_from_directory中的'categorical'属性时标签已经发生这种情况
然后我读here将我的train_generator放入一个函数中，然后创建一个无限循环并产生结果，但这会导致数据在程序开始时反复加载。 “找到属于7类的350张图像”（重复10次），然后导致错误
"output of generator should be a tuple (x, y, sample_weight) or (x, y). Found: <keras.preprocessing.image.DirectoryIterator object at 0x0000000063494BE0>"
这是原始错误的堆栈跟踪：
追踪（最近一次呼叫最后一次）：
文件“”，第1行，in runfile（'K：/ Manufacturing Operations / Yield / Tools_Yield / PythonScripts / AI / ISL_DI / Resnet / resISL_Depth.py'， WDIR ='K：/制造 操作/收率/ Tools_Yield / PythonScripts / AI / ISL_DI / RESNET'）
文件 “C：\用户\保罗\应用程序数据\本地\连续\ Anaconda3 \ LIB \站点包\ spyderlib \部件\ externalshell \ sitecustomize.py” 第714行，在runfile中 execfile（filename，namespace）
文件 “C：\用户\保罗\应用程序数据\本地\连续\ Anaconda3 \ LIB \站点包\ spyderlib \部件\ externalshell \ sitecustomize.py” 第89行，在execfile中 exec（compile（f.read（），filename，'exec'），namespace）
档案“K：/制造业 操作/收率/ Tools_Yield / PythonScripts / AI / ISL_DI / RESNET / resISL_Depth.py”， 第233行，在 回调= callbacks_list）
文件 “C：\用户\保罗\应用程序数据\漫游\ Python的\ Python35 \站点包\ keras \发动机\ training.py” 第1481行，在fit_generator中 STR（generator_output））
ValueError：生成器的输出应该是一个元组（x，y， sample_weight）或（x，y）。发现：无
以下是vars以外的代码
rep=0
for i in range(retrainings + 1):
#lr_init = [5, 1, .1, .01]
while rep != len(layers) - 1:
lr_init = [5, 1]
for lr_val in lr_init:
decay_init = .1
epochs_drop = 20
patience=60
# learning rate schedule
def step_decay(epoch):
initial_lrate = lr_val
drop = 0.1
epochs_drop = 60
lrate = initial_lrate * math.pow(drop, math.floor((1+epoch)/epochs_drop))
#print('\nLR: {:.6f}\n'.format(lrate))
return lrate
momentum_init=0.9
sgd = SGD(lr=lr_val, decay=decay_init, momentum=momentum_init, nesterov=False)
##reduce learning rate when loss has stopped improving
#lr_reducer = ReduceLROnPlateau(monitor='val_loss', factor=np.sqrt(0.1), cooldown=0, patience=5, min_lr=0.5e-6)
##stop training when accuracy has stopped improving
early_stopper = EarlyStopping(monitor='val_acc', min_delta=0.001, patience=50)
#csv_logger = CSVLogger('resnet18_cifar10.csv')
repititions = 3
#epochs=[105]
epochs=[200]
drop_out=[0]
#batchsize=[2, 4, 8, 10]
batchsize=[2, 5, 10]
zoom=[0]
shear=[0]
channelshift=[0]
featurewise=[False]
samplewise=[False]
rotation=[0]
nb_train_samples = 350
nb_validation_samples = 140
colormode='rgb'
# input image dimensions
img_width, img_height = 224, 224
nb_classes=7
img_channels = 3
for epoch_val in epochs:
for dropout_val in drop_out:
for batchsize_val in batchsize:
for zoom_val in zoom:
for shear_val in shear:
for channelshift_val in channelshift:
for featurewise_val in featurewise:
for samplewise_val in samplewise:
for rotation_val in rotation:
for r in range(repititions):
# np.random.seed(7)
# tf.set_random_seed(7)
train_data_dir = basepath + pathlist[0]
validation_data_dir = basepath + pathlist[1]
#############################################
#############################################
params={}
params['epochs']=epoch_val
params['drop_out']=dropout_val
params['batchsize']=batchsize_val
params['zoom']=zoom_val
params['shear']=shear_val
params['channelshift']=channelshift_val
params['featurewise']=featurewise_val
params['samplewise']=samplewise_val
params['rotation']=rotation_val
params['lr_init']=lr_val
params['momentum_init']=momentum_init
params['decay_init']=decay_init
params['epochs_drop']=epochs_drop
params['img_size']=list([img_width,img_height])
params['patience']=patience
total = 0
currentlayer = [i * 2 for i in layers[rep]]
total = sum(currentlayer) + 2
savefilename='resnet_' + str(total) + '_BKM_lr_' + str(lr_val) + '_batchSize_' + str(batchsize_val) + '_repition' + str((r+1)) + '_Study'
total = 0
with tf.device('/gpu:0'):
model = resnet_iter.ResnetBuilder.build_resnet_34((img_channels, img_width, img_height), nb_classes, layers[rep])
model.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=['accuracy'])
train_datagen = ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=featurewise_val, # divide inputs by std of the dataset
samplewise_std_normalization=samplewise_val, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
channel_shift_range=channelshift_val, #VGG set to 0
fill_mode="reflect", #VGG set to reflect
rotation_range=rotation_val, # randomly rotate images in the range (degrees, 0 to 180)
rescale=1./255, #VGG set to 1./255
width_shift_range=0.1, # randomly shift images horizontally (fraction of total width) - VGG set to 0
height_shift_range=0.1, # randomly shift images vertically (fraction of total height) - VGG set to 0
shear_range=shear_val, #VGG set to 0
zoom_range=zoom_val, #VGG set to 0.1
horizontal_flip=True, # randomly flip images
vertical_flip=True) # randomly flip images VGG set to True
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batchsize_val,
shuffle=True,
color_mode=colormode,
class_mode='categorical')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batchsize_val,
shuffle=True,
color_mode=colormode,
class_mode='categorical')
lrate = LearningRateScheduler(step_decay)
callbacks_list = [lrate, early_stopper]
try:
A=model.fit_generator(
train_generator,
samples_per_epoch=nb_train_samples,
nb_epoch=epoch_val,
validation_data=validation_generator,
nb_val_samples=nb_validation_samples,
callbacks=callbacks_list)
except:
print("train_generator: " + train_generator)
print("train_data_dir: " + train_data_dir)
files=os.listdir(train_data_dir)
print(len(files))