Keras深度学习不';即使安装了tensorflow GPU,也不能在GPU上运行
我想用keras tensorflow GPU后端训练CNN图像分类模型。我已经检查过了,tensorflow能够检测到GPU。但keras并没有使用GPU来训练模型。任务管理器还指示CPU利用率为100%,GPU利用率为0%,而培训模式为 我已经安装了Keras深度学习不';即使安装了tensorflow GPU,也不能在GPU上运行,tensorflow,keras,deep-learning,spyder,Tensorflow,Keras,Deep Learning,Spyder,我想用keras tensorflow GPU后端训练CNN图像分类模型。我已经检查过了,tensorflow能够检测到GPU。但keras并没有使用GPU来训练模型。任务管理器还指示CPU利用率为100%,GPU利用率为0%,而培训模式为 我已经安装了 visual studio社区2017 Python 3.7.3 CUDA 10.0 Cudnn 7.6 水蟒 我使用的是windows 10 64位、GPU 1050 GTX 4gb、CPU intel i5第七代 为了安装tensorflo
conda create --name tf_gpu tensorflow-gpu
我还尝试了以下3种方法来强制GPU进行训练
with tensorflow.device('/gpu:0'):
#code
from keras import backend
assert len(backend.tensorflow_backend._get_available_gpus()) > 0
#code
from keras import backend as K
K.tensorflow_backend._get_available_gpus()
#code
我在虚拟环境中安装的软件包
# packages in environment at C:\Users\Sreenivasa Reddy\Anaconda3\envs\tf_gpu:
#
# Name Version Build Channel
_tflow_select 2.1.0 gpu
absl-py 0.7.1 py37_0
alabaster 0.7.12 py37_0
asn1crypto 0.24.0 py37_0
astor 0.7.1 py37_0
astroid 2.2.5 py37_0
attrs 19.1.0 py37_1
babel 2.7.0 py_0
backcall 0.1.0 py37_0
blas 1.0 mkl
bleach 3.1.0 py37_0
ca-certificates 2019.5.15 0
certifi 2019.6.16 py37_0
cffi 1.12.3 py37h7a1dbc1_0
chardet 3.0.4 py37_1
cloudpickle 1.2.1 py_0
colorama 0.4.1 py37_0
cryptography 2.7 py37h7a1dbc1_0
cudatoolkit 10.0.130 0
cudnn 7.6.0 cuda10.0_0
decorator 4.4.0 py37_1
defusedxml 0.6.0 py_0
docutils 0.14 py37_0
entrypoints 0.3 py37_0
freetype 2.9.1 ha9979f8_1
gast 0.2.2 py37_0
grpcio 1.16.1 py37h351948d_1
h5py 2.9.0 py37h5e291fa_0
hdf5 1.10.4 h7ebc959_0
icc_rt 2019.0.0 h0cc432a_1
icu 58.2 ha66f8fd_1
idna 2.8 py37_0
imagesize 1.1.0 py37_0
intel-openmp 2019.4 245
ipykernel 5.1.1 py37h39e3cac_0
ipython 7.6.1 py37h39e3cac_0
ipython_genutils 0.2.0 py37_0
isort 4.3.21 py37_0
jedi 0.13.3 py37_0
jinja2 2.10.1 py37_0
jpeg 9b hb83a4c4_2
jsonschema 3.0.1 py37_0
jupyter_client 5.3.1 py_0
jupyter_core 4.5.0 py_0
Keras 2.2.4 <pip>
keras-applications 1.0.8 py_0
keras-preprocessing 1.1.0 py_1
keyring 18.0.0 py37_0
lazy-object-proxy 1.4.1 py37he774522_0
libpng 1.6.37 h2a8f88b_0
libprotobuf 3.8.0 h7bd577a_0
libsodium 1.0.16 h9d3ae62_0
libtiff 4.0.10 hb898794_2
markdown 3.1.1 py37_0
markupsafe 1.1.1 py37he774522_0
mccabe 0.6.1 py37_1
mistune 0.8.4 py37he774522_0
mkl 2019.4 245
mkl_fft 1.0.12 py37h14836fe_0
mkl_random 1.0.2 py37h343c172_0
mock 3.0.5 py37_0
nbconvert 5.5.0 py_0
nbformat 4.4.0 py37_0
numpy 1.16.4 py37h19fb1c0_0
numpy-base 1.16.4 py37hc3f5095_0
numpydoc 0.9.1 py_0
olefile 0.46 py37_0
openssl 1.1.1c he774522_1
packaging 19.0 py37_0
pandoc 2.2.3.2 0
pandocfilters 1.4.2 py37_1
parso 0.5.0 py_0
pickleshare 0.7.5 py37_0
pillow 6.1.0 py37hdc69c19_0
pip 19.1.1 py37_0
prompt_toolkit 2.0.9 py37_0
protobuf 3.8.0 py37h33f27b4_0
psutil 5.6.3 py37he774522_0
pycodestyle 2.5.0 py37_0
pycparser 2.19 py37_0
pyflakes 2.1.1 py37_0
pygments 2.4.2 py_0
pylint 2.3.1 py37_0
pyopenssl 19.0.0 py37_0
pyparsing 2.4.0 py_0
pyqt 5.9.2 py37h6538335_2
pyreadline 2.1 py37_1
pyrsistent 0.14.11 py37he774522_0
pysocks 1.7.0 py37_0
python 3.7.3 h8c8aaf0_1
python-dateutil 2.8.0 py37_0
pytz 2019.1 py_0
pywin32 223 py37hfa6e2cd_1
PyYAML 5.1.1 <pip>
pyzmq 18.0.0 py37ha925a31_0
qt 5.9.7 vc14h73c81de_0
qtawesome 0.5.7 py37_1
qtconsole 4.5.1 py_0
qtpy 1.8.0 py_0
requests 2.22.0 py37_0
rope 0.14.0 py_0
scipy 1.2.1 py37h29ff71c_0
setuptools 41.0.1 py37_0
sip 4.19.8 py37h6538335_0
six 1.12.0 py37_0
snowballstemmer 1.9.0 py_0
sphinx 2.1.2 py_0
sphinxcontrib-applehelp 1.0.1 py_0
sphinxcontrib-devhelp 1.0.1 py_0
sphinxcontrib-htmlhelp 1.0.2 py_0
sphinxcontrib-jsmath 1.0.1 py_0
sphinxcontrib-qthelp 1.0.2 py_0
sphinxcontrib-serializinghtml 1.1.3 py_0
spyder 3.3.6 py37_0
spyder-kernels 0.5.1 py37_0
sqlite 3.29.0 he774522_0
tensorboard 1.13.1 py37h33f27b4_0
tensorflow 1.13.1 gpu_py37h83e5d6a_0
tensorflow-base 1.13.1 gpu_py37h871c8ca_0
tensorflow-estimator 1.13.0 py_0
tensorflow-gpu 1.13.1 h0d30ee6_0
termcolor 1.1.0 py37_1
testpath 0.4.2 py37_0
tk 8.6.8 hfa6e2cd_0
tornado 6.0.3 py37he774522_0
traitlets 4.3.2 py37_0
urllib3 1.24.2 py37_0
vc 14.1 h0510ff6_4
vs2015_runtime 14.15.26706 h3a45250_4
wcwidth 0.1.7 py37_0
webencodings 0.5.1 py37_1
werkzeug 0.15.4 py_0
wheel 0.33.4 py37_0
win_inet_pton 1.1.0 py37_0
wincertstore 0.2 py37_0
wrapt 1.11.2 py37he774522_0
xz 5.2.4 h2fa13f4_4
zeromq 4.3.1 h33f27b4_3
zlib 1.2.11 h62dcd97_3
zstd 1.3.7 h508b16e_0
我的keras代码
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPool2D
from keras.layers import Flatten
from keras.layers import Dense
from keras import backend as K
K.tensorflow_backend._get_available_gpus()
classifier=Sequential()
classifier.add(Convolution2D(32,3,3,input_shape=(32,32,3),activation='relu'))
classifier.add(MaxPool2D(pool_size=(2,2)))
classifier.add(Convolution2D(32,3,3,activation='relu'))
classifier.add(MaxPool2D(pool_size=(2,2)))
classifier.add(Convolution2D(64,3,3,activation='relu'))
classifier.add(MaxPool2D(pool_size=(2,2)))
classifier.add(Flatten())
classifier.add(Dense(output_dim=128, activation='relu'))
classifier.add(Dense(output_dim=1, activation='sigmoid'))
classifier.compile(optimizer='adam',loss='binary_crossentropy', metrics=['accuracy'])
from keras.preprocessing.image import ImageDataGenerator
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(
'C:/Users/Sreenivasa Reddy/Desktop/Deep_Learning_A_Z/Volume_1_Supervised_Deep_Learning/Part2_Convolutional_Neural_Networks/Convolutional_Neural_Networks/dataset/training_set',
target_size=(32, 32),
batch_size=32,
class_mode='binary')
test_set = test_datagen.flow_from_directory(
'C:/Users/Sreenivasa Reddy/Desktop/Deep_Learning_A_Z/Volume_1_Supervised_Deep_Learning/Part2_Convolutional_Neural_Networks/Convolutional_Neural_Networks/dataset/test_set',
target_size=(32, 32),
batch_size=32,
class_mode='binary')
classifier.fit_generator(
training_set,
steps_per_epoch=8000,
epochs=25,
validation_data=test_set,
validation_steps=2000)
iPython控制台中的输出
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPool2D
from keras.layers import Flatten
from keras.layers import Dense
from keras import backend as K
K.tensorflow_backend._get_available_gpus()
Out[15]: ['/job:localhost/replica:0/task:0/device:GPU:0']
classifier=Sequential()
classifier.add(Convolution2D(32,3,3,input_shape=(32,32,3),activation='relu'))
classifier.add(MaxPool2D(pool_size=(2,2)))
classifier.add(Convolution2D(32,3,3,activation='relu'))
classifier.add(MaxPool2D(pool_size=(2,2)))
classifier.add(Convolution2D(64,3,3,activation='relu'))
classifier.add(MaxPool2D(pool_size=(2,2)))
classifier.add(Flatten())
classifier.add(Dense(output_dim=128, activation='relu'))
classifier.add(Dense(output_dim=1, activation='sigmoid'))
classifier.compile(optimizer='adam',loss='binary_crossentropy', metrics=['accuracy'])
from keras.preprocessing.image import ImageDataGenerator
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(
'C:/Users/Sreenivasa Reddy/Desktop/Deep_Learning_A_Z/Volume_1_Supervised_Deep_Learning/Part2_Convolutional_Neural_Networks/Convolutional_Neural_Networks/dataset/training_set',
target_size=(32, 32),
batch_size=32,
class_mode='binary')
test_set = test_datagen.flow_from_directory(
'C:/Users/Sreenivasa Reddy/Desktop/Deep_Learning_A_Z/Volume_1_Supervised_Deep_Learning/Part2_Convolutional_Neural_Networks/Convolutional_Neural_Networks/dataset/test_set',
target_size=(32, 32),
batch_size=32,
class_mode='binary')
classifier.fit_generator(
training_set,
steps_per_epoch=8000,
epochs=25,
validation_data=test_set,
validation_steps=2000)
__main__:2: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), input_shape=(32, 32, 3..., activation="relu")`
__main__:4: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation="relu")`
__main__:6: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), activation="relu")`
__main__:9: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation="relu", units=128)`
__main__:10: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation="sigmoid", units=1)`
Found 8000 images belonging to 2 classes.
Found 2000 images belonging to 2 classes.
Epoch 1/25
782/8000 [=>............................] - ETA: 17:38 - loss: 0.6328 - acc: 0.6310
注意:为了从iPython控制台复制代码片段,我在运行了一段时间后停止了内核
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPool2D
from keras.layers import Flatten
from keras.layers import Dense
from keras import backend as K
K.tensorflow_backend._get_available_gpus()
Out[15]: ['/job:localhost/replica:0/task:0/device:GPU:0']
classifier=Sequential()
classifier.add(Convolution2D(32,3,3,input_shape=(32,32,3),activation='relu'))
classifier.add(MaxPool2D(pool_size=(2,2)))
classifier.add(Convolution2D(32,3,3,activation='relu'))
classifier.add(MaxPool2D(pool_size=(2,2)))
classifier.add(Convolution2D(64,3,3,activation='relu'))
classifier.add(MaxPool2D(pool_size=(2,2)))
classifier.add(Flatten())
classifier.add(Dense(output_dim=128, activation='relu'))
classifier.add(Dense(output_dim=1, activation='sigmoid'))
classifier.compile(optimizer='adam',loss='binary_crossentropy', metrics=['accuracy'])
from keras.preprocessing.image import ImageDataGenerator
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(
'C:/Users/Sreenivasa Reddy/Desktop/Deep_Learning_A_Z/Volume_1_Supervised_Deep_Learning/Part2_Convolutional_Neural_Networks/Convolutional_Neural_Networks/dataset/training_set',
target_size=(32, 32),
batch_size=32,
class_mode='binary')
test_set = test_datagen.flow_from_directory(
'C:/Users/Sreenivasa Reddy/Desktop/Deep_Learning_A_Z/Volume_1_Supervised_Deep_Learning/Part2_Convolutional_Neural_Networks/Convolutional_Neural_Networks/dataset/test_set',
target_size=(32, 32),
batch_size=32,
class_mode='binary')
classifier.fit_generator(
training_set,
steps_per_epoch=8000,
epochs=25,
validation_data=test_set,
validation_steps=2000)
__main__:2: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), input_shape=(32, 32, 3..., activation="relu")`
__main__:4: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation="relu")`
__main__:6: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), activation="relu")`
__main__:9: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation="relu", units=128)`
__main__:10: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation="sigmoid", units=1)`
Found 8000 images belonging to 2 classes.
Found 2000 images belonging to 2 classes.
Epoch 1/25
782/8000 [=>............................] - ETA: 17:38 - loss: 0.6328 - acc: 0.6310
编辑:我训练了RNN和ANN模型,当我在训练时检查任务管理器时,CUDA利用率约为35%,但对于CNN模型,CUDA利用率为2%。CUDA 35%的未使用率不是很低吗?为什么CNN不利用35%
EDIT2:奇怪的是,当我增加批量时,模型列车速度非常慢,当我减少批量时(即当我将其设置为1)模型列车速度更快,对此有什么解释吗?我在这里问我的问题,因为我还没有获得发表评论的权利:/ 您提到您尝试了不同的方法: 使用tensorflow.device('/gpu:0'):#代码 在您发布的代码中,我看不到它们,也看不到使用gpu的不同方法,但我认为您使用gpu来获得上面的输出 如果您使用这些方法会发生什么?它仍然只使用gpu还是会出错 你可以尝试一下这样的方法并发布结果吗:
# Creates a graph.
with tf.device('/gpu:0'):
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)
# Creates a session with log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Runs the op.
print(sess.run(c))
如本例所述:
编辑
至于你的利用率问题
这可能有多种原因,例如:
我希望这能有所帮助。我上面提到的三种方法,我将它们分别放在keras代码上方进行了尝试,但没有一种方法强制使用gpu资源。我运行了您的代码,得到的输出是
SyntaxError:print调用中缺少括号。您是指print(sess.run)?
当我在括号中包含sess.run时,我得到了
对不起我的错误,我的意思是“print(sess.run(c))将纠正上面的错误。”。如果一切正常,tf gpu正常工作,它将给出[[22.28.][49.64.]]如果出现错误,可能是您的Tensorflow安装有问题。然后,您应该尝试卸载tensorflow和tensorflow gpu,然后只重新安装此处提到的tensorflow gpu()另一个可以帮助您的方法是按照此处的建议将phyton降级为3.6()是的!它打印出[[22.28.][49.64.]]。那么这意味着tf gpu正在工作?请检查我原始问题的编辑部分。也许你可以从中推断出一些事情,没错,你的tf gpu似乎工作得很好。我编辑了我的答案以适合你的利用率问题。