tensorflow/mnist.py

import tensorflow as tf

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import tensorflow as tf

import tensorflow.examples.tutorials.mnist.input_data as input_data
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)

sess = tf.InteractiveSession()

x = tf.placeholder("float", shape=[None, 784])
y_ = tf.placeholder("float", shape=[None, 10])

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

init = tf.global_variables_initializer()
sess.run(init)

y = tf.nn.softmax(tf.matmul(x, w) + b)

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

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

for i in range(1000):
  batch = mnist.train.next_batch(50)
  train_step.run(feed_dict={x: batch[0], y_: batch[1]})

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

print (accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}))