TensorFlow MNIST（手写识别 softmax）实例运行

首先要有编译环境，并且已经正确的编译安装，关于环境配置参考：http://www.cnblogs.com/dyufei/p/8027517.html

一、MNIST 运行

1）首先下载训练数据

在 http://yann.lecun.com/exdb/mnist/ 将四个包都下载下来,在下面代码的运行目录下创建MNIST_data目录，将四个包放进去

train-images-idx3-ubyte.gz: training set images (9912422 bytes)

train-labels-idx1-ubyte.gz: training set labels (28881 bytes)

t10k-images-idx3-ubyte.gz: test set images (1648877 bytes)

t10k-labels-idx1-ubyte.gz: test set labels (4542 bytes)

当然也可以不下载，前提是运行TensorFlow的服务器可以正常访问下载目录，如果出问题参照【问题1）】解决）

2) MNIST 代码

A: 比较旧的版本（官方教程里面的）

https://tensorflow.google.cn/get_started/mnist/beginners

中文：http://www.tensorfly.cn/tfdoc/tutorials/mnist_beginners.html

完整代码如下：mnist.py

import input_data

import  tensorflow as tf

FLAGS = None

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

x = tf.placeholder("float",[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("float",[None,10])

cross_entroy = -tf.reduce_sum(y_ * tf.log(y))

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entroy)

init = tf.initialize_all_variables()

sess = tf.Session()

sess.run(init)

for _ 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})

input_data.py

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import gzip

import os

import tempfile

import numpy

from six.moves import urllib

from six.moves import xrange  # pylint: disable=redefined-builtin

import tensorflow as tf

from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets

运行

python mnist.py

2) 新版本mnist_softmax.py

input_data.py 文件内容相同，mnist_softmax.py文件不同

mnist_softmax.py 文件目录：

tensorflow\tensorflow\examples\tutorials\mnist\mnist_softmax.py

完整代码：

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import argparse

import sys

import os

os.environ['TF_CPP_MIN_LOG_LEVEL']='2'

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

FLAGS = None

def main(_):

  # Import data

  mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)

  # Create the model

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

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

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

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

  # Define loss and optimizer

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

  # The raw formulation of cross-entropy,

  #

  #   tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),

  #                                 reduction_indices=[1]))

  #

  # can be numerically unstable.

  #

  # So here we use tf.nn.softmax_cross_entropy_with_logits on the raw

  # outputs of 'y', and then average across the batch.

  cross_entropy = tf.reduce_mean(

      tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))

  train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

  sess = tf.InteractiveSession()

  tf.global_variables_initializer().run()

  # Train

  for _ in range(1000):

    batch_xs, batch_ys = mnist.train.next_batch(100)

    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

  # Test trained model

  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}))

if __name__ == '__main__':

  parser = argparse.ArgumentParser()

  parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/mnist/input_data',

                      help='Directory for storing input data')

  FLAGS, unparsed = parser.parse_known_args()

  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

数据路径不同,将训练数据copy过去：

cp MNIST_data/*.gz /tmp/tensorflow/mnist/input_data/

运行：

python mnist_softmax.py

巴特西

TensorFlow MNIST（手写识别 softmax）实例运行

TensorFlow MNIST（手写识别 softmax）实例运行

一、MNIST 运行

1）首先下载训练数据

2) MNIST 代码

A: 比较旧的版本（官方教程里面的）

完整代码如下：mnist.py

input_data.py

运行

2) 新版本mnist_softmax.py

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