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