此问题从未发生过，但从今天开始，即使使用非常小的批量大小，Tensorflow也会尝试分配大量内存。
我遵循了本教程：
https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html
＆＃34;使用预先训练的网络的瓶颈功能：一分钟内准确度达到90％＆＃34;
这是我的代码：
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dropout, Flatten, Dense
from keras import applications
img_width, img_height = 150, 150
top_model_weights_path = 'bottleneck_fc_model.h5'
train_data_dir = 'C:\\ImageData\\Augmented\\Train'
validation_data_dir = 'C:\\ImageData\\Augmented\\Validate'
#train_data_dir = 'C:\\Users\\NSA\\flower_photos\\Train'
#validation_data_dir = 'C:\\Users\\NSA\\flower_photos\\Validate'
nb_train_samples = 25
nb_validation_samples = 5
epochs = 10
my_batch_size = 10
def save_bottleneck_features():
datagen = ImageDataGenerator(rescale=1./255)
# build the VGG16 network
model = applications.VGG16(include_top=False, weights='imagenet')
generator = datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=my_batch_size,
class_mode=None,
shuffle=False)
bottleneck_features_train = model.predict_generator(
generator,
steps=nb_train_samples // my_batch_size,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
verbose=1)
np.save(open('bottleneck_features_train.npy', 'w'),
bottleneck_features_train)
generator = datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=my_batch_size,
class_mode=None,
shuffle=False)
bottleneck_features_validation = model.predict_generator(
generator, nb_validation_samples // my_batch_size)
np.save(open('bottleneck_features_validation.npy', 'w'),
bottleneck_features_validation)
def train_top_model():
train_data = np.load(open('bottleneck_features_train.npy'))
train_labels = np.array(
[0] * (nb_train_samples / 2) + [1] * (nb_train_samples / 2))
validation_data = np.load(open('bottleneck_features_validation.npy'))
validation_labels = np.array(
[0] * (nb_validation_samples / 2) + [1] * (nb_validation_samples / 2))
model = Sequential()
model.add(Flatten(input_shape=train_data.shape[1:]))
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop',
loss='binary_crossentropy', metrics=['accuracy'])
model.fit(train_data, train_labels,
epochs=epochs,
batch_size=my_batch_size,
validation_data=(validation_data, validation_labels))
model.save_weights(top_model_weights_path)
save_bottleneck_features()
train_top_model()
这是我得到的错误：
PS C:\Users\NSA\ownCloud\Documents\Tensorflow\Skripts> cd 'c:\Users\NSA\ownCloud\Documents\Tensorflow\Skripts'; ${env:PYTHONIOENCODING}='UTF-8'; ${env:PYTHONUNBUFFERED}='1'; & 'C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python36_64\python.exe' 'C:\Users\NSA\.vscode\extensions\ms-python.python-2018.3.1\pythonFiles\PythonTools\visualstudio_py_launcher.py' 'c:\Users\NSA\ownCloud\Documents\Tensorflow\Skripts' '50490' '34806ad9-833a-4524-8cd6-18ca4aa74f14' 'RedirectOutput,RedirectOutput' 'c:\Users\NSA\ownCloud\Documents\Tensorflow\Skripts\first_try_real_transfer_learning_keras_vgg16.py'
C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python36_64\lib\site-packages\h5py\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will
be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
Using TensorFlow backend.
Bottleneck Features saven
2018-04-09 16:02:08.772206: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2018-04-09 16:02:09.345010: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:1212] Found device 0 with properties:
name: GeForce 940MX major: 5 minor: 0 memoryClockRate(GHz): 1.189
pciBusID: 0000:02:00.0
totalMemory: 2.00GiB freeMemory: 1.66GiB
2018-04-09 16:02:09.356147: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:1312] Adding visible gpu devices: 0
2018-04-09 16:02:10.108947: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:993] Creating TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 1429 MB memory) -> physical GPU (device: 0, name: GeForce 940MX, pci bus id: 0000:02:00.0, compute capability: 5.0)
Found 109 images belonging to 2 classes.
2018-04-09 16:02:16.979539: W C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.33GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2018-04-09 16:02:17.441196: W C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.19GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2018-04-09 16:02:17.792983: W C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.14GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2018-04-09 16:02:18.122577: W C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.17GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2/2 [==============================] - 4s 2s/step
Traceback (most recent call last):
File "c:\Users\NSA\ownCloud\Documents\Tensorflow\Skripts\first_try_real_transfer_learning_keras_vgg16.py", line 94, in <module>
save_bottleneck_features()
File "c:\Users\NSA\ownCloud\Documents\Tensorflow\Skripts\first_try_real_transfer_learning_keras_vgg16.py", line 56, in save_bottleneck_features
bottleneck_features_train)
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python36_64\lib\site-packages\numpy\lib\npyio.py", line 511, in save
pickle_kwargs=pickle_kwargs)
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python36_64\lib\site-packages\numpy\lib\format.py", line 565, in write_array
version)
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python36_64\lib\site-packages\numpy\lib\format.py", line 335, in _write_array_header
fp.write(header_prefix)
TypeError: write() argument must be str, not bytes
PS C:\Users\NSA\ownCloud\Documents\Tensorflow\Skripts>
在调用 model.predict_generator（）
时会发生错误起初我认为它的内存耗尽因为我的批量太大，但即使我使用的批量大小为1，它也需要超过2GiB的内存。我使用TensorFlow后端安装了CUDA 9.0，cuDNN 7.0，Tensorflow 1.6.0和Keras 2.1.5。这曾经没有问题，但它突然开始给我这个错误。我使用的是NVIDIA GeForce 940MX
答案 0 :(得分：0)
您的问题与内存或张量流无关。以文本形式打开的文件正在写入字节。 而不是以文本形式打开文件：
open('bottleneck_features_train.npy', 'w')
以字节为单位打开它：
open('bottleneck_features_train.npy', 'wb')
这适用于您打给open的所有电话。