TFlearn DNN-tflearn.dnn（）需要很长时间。 （Tensorflow 1.12，CUDA 9.0，cudnn 7.4.1，tflearn 0.32）

Question

多年来我一直在阅读Stackoverflow。但是这次是我第一个问题要问的时候了。

我有一个简单的TFLearn-AlexNet示例： https://github.com/tflearn/tflearn/blob/master/examples/images/alexnet.py

network = input_data(shape=[None, 783, 660, 3])         
network = conv_2d(network, 96, 11, strides=4, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 256, 5, activation='relu')   
network = max_pool_2d(network, 3, strides=2)            
network = local_response_normalization(network)
network = conv_2d(network, 384, 3, activation='relu')   
network = conv_2d(network, 384, 3, activation='relu')   
network = conv_2d(network, 256, 3, activation='relu')   
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = fully_connected(network, 4096, activation='tanh') 
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh') 
network = dropout(network, 0.5)
network = fully_connected(network, 12, activation='softmax')    #10
network = regression(network, optimizer='momentum',
                     loss='categorical_crossentropy',
                     learning_rate=0.001)               #0.001

model = tflearn.DNN(network, tensorboard_verbose=0)

分配网络等大约需要1秒钟。 但是tflearn.DNN（）大约需要300秒，这是不寻常的。

如果这是tflearn的CUDA和cudnn版本更新的错误，您有任何想法吗？

将tensorflow-gpu与Nvidia RTX 2080Ti一起使用