TensorFlow MNIST（手写识别 softmax）实例运行

MNIST（手写识别）实例运行

首先，你必须有一个编译环境，并且已经正确编译安装。环境配置参考：

一、MNIST 运行1）先下载训练数据

四个包都下载下来，在下面代码的运行目录下创建一个目录，把四个包放在里面

train–idx3-ubyte.gz：设置（字节）

train–idx1-ubyte.gz：设置（28881字节）

t10k–idx3-ubyte.gz：测试集（字节）

t10k–idx1-ubyte.gz：测试集（4542字节）

当然你也可以不下载，前提是运行服务器可以正常访问下载目录。如果有问题，请参考【问题1）】解决）

2) MNIST 代码 A：旧版本（在官方教程中）

中文：

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})

.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

mnist.py

2)新版本.py

.py文件内容相同，.py文件不同

.py文件目录：

\\mnist.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，把训练数据复制过去：

cp /*.gz /tmp//mnist//

运行：

.py