I am trying to learn VGG16 model. But now, I got an error like,
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"]]
Here are my system versions,
- Windows 10
- Tensorflow 1.10.0
- Python 3.6.7
- cuDNN and CUDA;
- NVIDIA GeForce GTX 1050TI
- Keras, Using TensorFlow backend.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
If you need the code;
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)
The error pops up;
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)
