1、MNIST_data一般要放在运行文件的同目录下
2、记得删除以前运行时的MNIST_data文件下的所有压缩文件
3、最好自己找到压缩文件,放到MINST_data
4、MNIST_data的数据下载(四个下载都要在MNIST_data文件下参考第2条)
5、注意如果不能导入tenserflow(cpu),在windos+pycharm环境下大概率是因为你的python版本是3.7!3.6及其以下的python才能运行哦。查看python 版本
6、主体代码如下:
import input_data
import tensorflow as tf
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_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
for i in range(1000):
print('training',i)
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}) )
7、input_data代码如下:
"""Functions for downloading and reading MNIST data."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gzip
import os
import tensorflow.python.platform
import numpy
from six.moves import urllib
from six.mo