Tensorflow的GPU配置：mnist例子的运行

Tensorflow装好，首先测试mnist示例，学习这个例子就像编程要首先学习“Hello Word”例子一样。
在终端中输入：

$ python

继续输入以下代码，前两行是找到输入数据源和下载mnist数据集和标签。

import tensorflow.examples.tutorials.mnist.input_data as input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
import tensorflow as tf
x = tf.placeholder(tf.float32, [None, 784]) 
W = tf.Variable(tf.zeros([784,10])) 
b = tf.Variable(tf.zeros([10])+0.1) 
y = tf.nn.softmax(tf.matmul(x,W) + b) 
y_ = tf.placeholder("float", [None,10]) 
cross_entropy = -tf.reduce_sum(y_*tf.log(y)) 
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy) 
init = tf.global_variables_initializer() sess = tf.Session() 
sess.run(init) 
for i in range(1000): 
    batch_xs, batch_ys = mnist.train.next_batch(100) 
    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) 
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) 
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) 
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

下载的数据集和标签在MNIST_data这个文件夹中

运行结果如下图，结果：0.9164