深度學習環境搭建極簡教程（win10+cuda_9.0）

開始入深度學習的坑了，記錄一次環境搭建記錄

看着別的博客安裝賊複雜，我這個算是賊簡單。

• 首先安裝好vs2017 勾選C++
• 然後再下面的操作

1.Python環境

直接安裝Python 3.6.8 64位，不需要安裝臃腫的Anaconda。

2.CUDA 9.0

cuda_9.0.176_win10
如果安裝不上，完全卸載顯卡驅動，推薦使用工具Display Driver Uninstaller (DDU) V17.0.8.5 Released在安全模式下卸載顯卡驅動後再正常安裝

3.CuDNN

cudnn-9.0-windows10-x64-v7.4.2.24
下載完成後，解壓複製到Toolkit目錄下即可

4.安裝Keras和Tensorflow-GPU

pip install Keras
pip install Tensorflow-GPU

5.Hello World

from keras import models
from keras import layers
from keras.datasets import mnist
from keras.utils import to_categorical

(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
network = models.Sequential()
network.add(layers.Dense(512, activation='relu', input_shape=(28 * 28,)))
network.add(layers.Dense(10, activation='softmax'))
network.compile(optimizer='rmsprop',
                loss='categorical_crossentropy',
                metrics=['accuracy'])
train_images = train_images.reshape((60000, 28 * 28))
train_images = train_images.astype('float32') / 255
test_images = test_images.reshape((10000, 28 * 28))
test_images = test_images.astype('float32') / 255
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)
network.fit(train_images, train_labels, epochs=5, batch_size=128)
test_loss, test_acc = network.evaluate(test_images, test_labels)
print('test_acc:', test_acc)