获取卷积算法失败。这可能是因为 cuDNN 初始化失败

Question

我正在尝试学习 VGG16 模型。但是现在，我得到了这样的错误，

Using TensorFlow backend. UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[{{node conv2d_1/convolution}} = Conv2D[T=DT_FLOAT, data_format="NCHW", dilations=[1, 1, 1, 1], padding="VALID", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](conv2d_1/convolution-0-TransposeNHWCToNCHW-LayoutOptimizer, conv2d_1/kernel/read)]] [[{{node dense_3/Softmax/_211}} = _Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_237_dense_3/Softmax", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]]

这是我的系统版本，

Windows 10
张量流 1.10.0
Python 3.6.7
cuDNN 和 CUDA；
英伟达 GeForce GTX 1050TI
Keras，使用 TensorFlow 后端.2.2.4

nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2017 NVIDIA Corporation Built on Fri_Sep__1_21:08:32_Central_Daylight_Time_2017 Cuda compilation tools, release 9.0, V9.0.176

如果您需要代码；

from keras.models import Sequential
from keras.layers.core import Flatten, Dense, Dropout
from keras.layers.convolutional import Conv2D, MaxPooling2D, ZeroPadding2D
from keras.optimizers import SGD
import cv2, numpy as np


def VGG_16(weights_path=None):
    model = Sequential()
    model.add(ZeroPadding2D((1,1), input_shape=(224, 224, 3), data_format='channels_last'))
    model.add(Conv2D(64, kernel_size=(3, 3), strides=1, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(64, kernel_size=(3, 3), strides=1, activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2), data_format='channels_last'))

    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2), data_format='channels_last'))

    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(256, kernel_size=(3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(256, kernel_size=(3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(256, kernel_size=(3, 3), activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2), data_format='channels_last'))

    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(512, kernel_size=(3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(512, kernel_size=(3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(512, kernel_size=(3, 3), activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2), data_format='channels_last'))

    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(512, kernel_size=(3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(512, kernel_size=(3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(512, kernel_size=(3, 3), activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2), data_format='channels_last'))

    model.add(Flatten())
    model.add(Dense(4096, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(4096, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(1000, activation='softmax'))

    if weights_path:
        model.load_weights(weights_path)

    return model

if __name__ == "__main__":
    from keras.applications.vgg16 import decode_predictions
    im = cv2.resize(cv2.imread('karisik_meyveler.jpg'), (224, 224)).astype(np.float32)
    im[:,:,0] -= 103.939
    im[:,:,1] -= 116.779
    im[:,:,2] -= 123.68
    im = im.transpose((1,0,2))
    im = np.expand_dims(im, axis=0)

    # Test pretrained model
    model = VGG_16('vgg16_weights_tf_dim_ordering_tf_kernels.h5')
    sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
    model.compile(optimizer=sgd, loss='categorical_crossentropy')
    out = model.predict(im)
    predictions = decode_predictions(out)

弹出错误；

UnknownError Traceback (most recent call last)
<ipython-input-1-9b64406a16ce> in <module>()
     69     sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
     70     model.compile(optimizer=sgd, loss='categorical_crossentropy')
---> 71     out = model.predict(im)
     72     predictions = decode_predictions(out)

Answer 1

解决方法：检查NVIDIA驱动的更新并更新。

获取卷积算法失败。这可能是因为 cuDNN 初始化失败

Failed to get convolution algorithm. This is probably because cuDNN failed to initialize

keras

tensorflow

cudnn