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

图片[1]-TensorFlow  MNIST(手写识别 softmax)实例运行-唐朝资源网

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

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