sudo apt purge nvidia*

ubuntu-drivers devices            # 可以看到显卡等设备，和推荐的驱动

sudo ubuntu-drivers autoinstall   # 安装推荐驱动，通常是最新版

sudo add-apt-repository ppa:graphics-drivers

sudo apt-get update

bash Anaconda3-5.3.0-Linux-x86_64.sh # make sure append the Anaconda executable directory to your PATH environment variable in .bashrc

source ~/.bashrc

python --version # to show the python version

conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/

conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/

conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/

conda config --set show_channel_urls yes

[global]

index-url = https://pypi.tuna.tsinghua.edu.cn/simple/

[install]

trusted-host=https://pypi.tuna.tsinghua.edu.cn

conda update conda -y

conda update anaconda -y

conda update python -y

conda update --all -y

conda create --name tf-gpu   # Create a Python "virtual environment" for TensorFlow using conda

conda activate tf-gpu       # 注意运行此命令后，命令行开头的提示变为(tf-gpu) user@computer:~$，表示tf-gpu环境处于激活状态
# 后面的命令，都在tf-gpu环境下执行，我保留了命令行的提示，以示区别

(tf-gpu) user@computer:~$ conda install tensorflow-gpu -y # install TensorFlow with GPU acceleration and all of the dependencies.

(tf-gpu) user@computer:~$ conda install ipykernel -y
(tf-gpu) user@computer:~$ conda install jupyter

(tf-gpu) user@computer:~$ python -m ipykernel install --user --name tf-gpu --display-name "TensorFlow-GPU"

(tf-gpu) user@computer:~$ conda install keras -y

jupyter notebook

# Import dependencies

import keras

from keras.datasets import mnist

from keras.models import Sequential

from keras.layers import Dense, Dropout

from keras.layers import Flatten,  MaxPooling2D, Conv2D

from keras.callbacks import TensorBoard

# Load and process the MNIST data

# 推荐先下载mnist.npz到目录~/.keras/datasets/

(X_train,y_train), (X_test, y_test) = mnist.load_data(path="mnist.npz")

X_train = X_train.reshape(60000,28,28,1).astype('float32')

X_test = X_test.reshape(10000,28,28,1).astype('float32')

X_train /= 255

X_test /= 255

n_classes = 10

y_train = keras.utils.to_categorical(y_train, n_classes)

y_test = keras.utils.to_categorical(y_test, n_classes)

# Create the LeNet-5 neural network architecture

model = Sequential()

model.add(Conv2D(32, kernel_size=(3,3), activation='relu', input_shape=(28,28,1)) )

model.add(Conv2D(64, kernel_size=(3,3), activation='relu'))

model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Dropout(0.25))

model.add(Flatten())

model.add(Dense(128, activation='relu'))

model.add(Dropout(0.5))
model.add(Dense(n_classes, activation='softmax')) # Compile the model

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) # Set log data to feed to TensorBoard for visual analysis

tensor_board = TensorBoard('./logs/LeNet-MNIST-1') # Train the model

model.fit(X_train, y_train, batch_size=128, epochs=15, verbose=1,

          validation_data=(X_test,y_test), callbacks=[tensor_board])

 (tf-gpu) dbk@i9:~$ tensorboard --logdir=./logs --port 6006