Question

我正在尝试在fit_generator函数中使用use_multiprocessing = True运行我的CNN python代码，但出现错误，它在单个进程中也能正常工作，但CPU负载：20％，GPU：8％。

我正在使用Tensorflow后端在具有Windows 10核心i7-7820HK CPU和NVIDIA GTX 1080的MSI笔记本电脑上运行

这是我的代码：

# Part 1 - Building the CNN

# Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.preprocessing.image import ImageDataGenerator


# Initialising the CNN
classifier = Sequential()

# Step 1 - Convolution
classifier.add(Conv2D(32, (3, 3), input_shape=(64, 64, 3), activation='relu'))

# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size=(2, 2)))

# Adding a second convolutional layer
classifier.add(Conv2D(32, (3, 3), activation='relu'))
classifier.add(MaxPooling2D(pool_size=(2, 2)))

# Step 3 - Flattening
classifier.add(Flatten())

# Step 4 - Full connection
classifier.add(Dense(units=128, activation='relu'))
classifier.add(Dense(units=1, activation='sigmoid'))

# Compiling the CNN
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Part 2 - Fitting the CNN to the images
train_datagen = ImageDataGenerator(rescale=1./255,
                                   shear_range=0.2,
                                   zoom_range=0.2,
                                   horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('dataset\\training_set',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'binary')

test_set = test_datagen.flow_from_directory('dataset\\test_set',
                                            target_size = (64, 64),
                                            batch_size = 32,
                                            class_mode = 'binary')

if __name__ == '__main__':
    classifier.fit_generator(training_set,
                             workers=8,
                             max_queue_size=100,
                             use_multiprocessing=True,
                             steps_per_epoch=(8000 / 32),
                             epochs=25,
                             validation_data=test_set,
                             validation_steps=(2000 / 32))

我得到这个错误：

Using TensorFlow backend. Found 8000 images belonging to 2 classes. Found 2000 images belonging to 2 classes. Epoch 1/25 Exception in thread Thread-24: Traceback (most recent call last):   File "C:\Users\MSI-GT75\Anaconda3\envs\cnn\lib\threading.py", line 916, in
_bootstrap_inner
    self.run()   File "C:\Users\MSI-GT75\Anaconda3\envs\cnn\lib\threading.py", line 864, in run
    self._target(*self._args, **self._kwargs)   File "C:\Users\MSI-GT75\Anaconda3\envs\cnn\lib\site-packages\keras\utils\data_utils.py", line 548, in _run
    with closing(self.executor_fn(_SHARED_SEQUENCES)) as executor:   File "C:\Users\MSI-GT75\Anaconda3\envs\cnn\lib\site-packages\keras\utils\data_utils.py", line 522, in <lambda>
    initargs=(seqs,))   File "C:\Users\MSI-GT75\Anaconda3\envs\cnn\lib\multiprocessing\context.py", line 119, in Pool
    context=self.get_context())   File "C:\Users\MSI-GT75\Anaconda3\envs\cnn\lib\multiprocessing\pool.py", line 174, in __init__
    self._repopulate_pool()   File "C:\Users\MSI-GT75\Anaconda3\envs\cnn\lib\multiprocessing\pool.py", line 239, in _repopulate_pool
    w.start()   File "C:\Users\MSI-GT75\Anaconda3\envs\cnn\lib\multiprocessing\process.py", line 105, in start
    self._popen = self._Popen(self)   File "C:\Users\MSI-GT75\Anaconda3\envs\cnn\lib\multiprocessing\context.py", line 322, in _Popen
    return Popen(process_obj)   File "C:\Users\MSI-GT75\Anaconda3\envs\cnn\lib\multiprocessing\popen_spawn_win32.py", line 65, in __init__
    reduction.dump(process_obj, to_child)   File "C:\Users\MSI-GT75\Anaconda3\envs\cnn\lib\multiprocessing\reduction.py", line 60, in dump
    ForkingPickler(file, protocol).dump(obj) TypeError: can't pickle _thread.lock objects

Exception in thread Thread-23: Traceback (most recent call last):   File "C:\Users\MSI-GT75\Anaconda3\envs\cnn\lib\threading.py", line 916, in _bootstrap_inner
    self.run()   File "C:\Users\MSI-GT75\Anaconda3\envs\cnn\lib\threading.py", line 864, in run
    self._target(*self._args, **self._kwargs)   File "C:\Users\MSI-GT75\Anaconda3\envs\cnn\lib\site-packages\keras\utils\data_utils.py", line 548, in _run
    with closing(self.executor_fn(_SHARED_SEQUENCES)) as executor:   File "C:\Users\MSI-GT75\Anaconda3\envs\cnn\lib\site-packages\keras\utils\data_utils.py", line 522, in <lambda>
    initargs=(seqs,))   File "C:\Users\MSI-GT75\Anaconda3\envs\cnn\lib\multiprocessing\context.py", line 119, in Pool
    context=self.get_context())   File "C:\Users\MSI-GT75\Anaconda3\envs\cnn\lib\multiprocessing\pool.py", line 174, in __init__
    self._repopulate_pool()   File "C:\Users\MSI-GT75\Anaconda3\envs\cnn\lib\multiprocessing\pool.py", line 239, in _repopulate_pool
    w.start()   File "C:\Users\MSI-GT75\Anaconda3\envs\cnn\lib\multiprocessing\process.py", line 105, in start
    self._popen = self._Popen(self)   File "C:\Users\MSI-GT75\Anaconda3\envs\cnn\lib\multiprocessing\context.py", line 322, in _Popen
    return Popen(process_obj)   File "C:\Users\MSI-GT75\Anaconda3\envs\cnn\lib\multiprocessing\popen_spawn_win32.py", line 65, in __init__
    reduction.dump(process_obj, to_child)   File "C:\Users\MSI-GT75\Anaconda3\envs\cnn\lib\multiprocessing\reduction.py", line 60, in dump
    ForkingPickler(file, protocol).dump(obj) TypeError: can't pickle _thread.lock objects

更新所有软件包后，将显示此错误，而不是上面的错误：

ValueError: Using a generator with `use_multiprocessing=True` is not supported on Windows (no marshalling of generators across process boundaries). Instead, use single thread/process or multithreading.

在Windows的fit_generator中激活use_multiprocessing时出错

0 个答案: