我重写了以下代码以处理大规模数据集。我正在使用 Python 生成器根据逐批生成的数据拟合模型。
def subtract_mean_gen(x_source,y_source,avg_image,batch):
batch_list_x=[]
batch_list_y=[]
for line,y in zip(x_source,y_source):
x=line.astype('float32')
x=x-avg_image
batch_list_x.append(x)
batch_list_y.append(y)
if len(batch_list_x) == batch:
yield (np.array(batch_list_x),np.array(batch_list_y))
batch_list_x=[]
batch_list_y=[]
model = resnet.ResnetBuilder.build_resnet_18((img_channels, img_rows, img_cols), nb_classes)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
val = subtract_mean_gen(X_test,Y_test,avg_image_test,batch_size)
model.fit_generator(subtract_mean_gen(X_train,Y_train,avg_image_train,batch_size), steps_per_epoch=X_train.shape[0]//batch_size,epochs=nb_epoch,validation_data = val,
validation_steps = X_test.shape[0]//batch_size)
我收到以下错误:
239/249 [===========================>..] - ETA: 60s - loss: 1.3318 - acc: 0.8330Exception in thread Thread-1:
Traceback (most recent call last):
File "/usr/lib/python2.7/threading.py", line 801, in __bootstrap_inner
self.run()
File "/usr/lib/python2.7/threading.py", line 754, in run
self.__target(*self.__args, **self.__kwargs)
File "/usr/local/lib/python2.7/dist-packages/keras/utils/data_utils.py", line 560, in data_generator_task
generator_output = next(self._generator)
StopIteration
240/249 [===========================>..] - ETA: 54s - loss: 1.3283 - acc: 0.8337Traceback (most recent call last):
File "cifa10-copy.py", line 125, in <module>
validation_steps = X_test.shape[0]//batch_size)
File "/usr/local/lib/python2.7/dist-packages/keras/legacy/interfaces.py", line 87, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1809, in fit_generator
generator_output = next(output_generator)
StopIteration
我调查了一个类似的问题 here但是,我无法解决引发 StopIteration 的错误。
最佳答案
keras 的生成器必须是无限的:
def subtract_mean_gen(x_source,y_source,avg_image,batch):
while True:
batch_list_x=[]
batch_list_y=[]
for line,y in zip(x_source,y_source):
x=line.astype('float32')
x=x-avg_image
batch_list_x.append(x)
batch_list_y.append(y)
if len(batch_list_x) == batch:
yield (np.array(batch_list_x),np.array(batch_list_y))
batch_list_x=[]
batch_list_y=[]
错误的发生是因为 keras 试图获得一个新的批处理,但是你的生成器已经结束了。 (即使您定义了正确数量的步骤,keras 也有一个队列将尝试从生成器获取更多批处理,即使您处于最后一步。)
显然,您有一个默认队列大小,它是 10(异常出现在结束前 10 个批处理,因为队列试图在结束后获取一个批处理)。
关于python - 停止迭代 : generator_output = next(output_generator),我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/48709839/