Administrator@SuperComputer MINGW64 ~

$ docker run -it -p 8888:8888 registry.cn-hangzhou.aliyuncs.com/denverdino/tens

orflow bash

root@b3e200093da9:/notebooks# python

Python 2.7.6 (default, Oct 26 2016, 20:30:19)

[GCC 4.8.4] on linux2

Type "help", "copyright", "credits" or "license" for more information.

>>> from tensorflow.examples.tutorials.mnist import input_data
-----------------------------------------------------------对于中间这个数据是怎么来的，我只能说是从网上下的，具体存放在哪个文件间中，我至今都没有找到

>>> mnist = input_data.read_data_sets("/MNIST_data/",one_hot = True)

Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.

Extracting /MNIST_data/train-images-idx3-ubyte.gz

Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.

Extracting /MNIST_data/train-labels-idx1-ubyte.gz

Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.

Extracting /MNIST_data/t10k-images-idx3-ubyte.gz

Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.

Extracting /MNIST_data/t10k-labels-idx1-ubyte.gz

>>> import tensorflow as tf

>>> x = tf.placeholder(tf.float32,[None,784])

>>> W = tf.Variable(tf.zeros([784,10]))

>>> b = tf.Variable(tf.zeros([10]))

>>> y = tf.nn.softmax(tf.matmul(x,W)+b)

>>> y_ = tf.placeholder(tf.float32,[None,10])

>>> cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y),reduction_indices=[1]))

>>> train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
>>> init = tf.initialize_all_variables()//这个函数现在已经不用了，应该使用下边的那一行函数

WARNING:tensorflow:From <stdin>:1 in <module>.: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.

Instructions for updating:

Use `tf.global_variables_initializer` instead.

>>> 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,tf.float32))

>>> print(sess.run(accuracy,feed_dict={x:mnist.test.images,y_:mnist.test.labels}

))

0.9167

>>>